the afsis diagnostic trials agronomic databaseafsis-dt.ciat.cgiar.org/downloads/afsis...

The AFSIS diagnostic trials agronomic database

Contents Introduction and purpose of the DTA database ................................................................................................................................................. 3 Database design and structure - principles ......................................................................................................................................................... 5 Overview of the Diagnostic Trials Agronomic database ..................................................................................................................................... 7 Tables, the number of fields defined and number of records for each table ..................................................................................................... 8 Description of the fields contained in each of the tables ................................................................................................................................... 9

The Sentinel site table ..................................................................................................................................................................................... 9 The experimental field – location and site characteristics (the Field_loctop table) ...................................................................................... 13 Harvest data and yield measurements: the Yld_msr table ............................................................................................................................ 17 Crop growth data (the Crop_grwth table) ................................................................................................................................................... 23 Rainfall and temperature data (The Rainfall_temp table) ............................................................................................................................ 35 Soil moisture data (The SoilMoisture table) ................................................................................................................................................. 37 Identification of the experimental field (the Field_ident table) ................................................................................................................... 39 Pest and diseases (the CropStressFActors table) ........................................................................................................................................... 41 Weather stations (the WeatherStations table .............................................................................................................................................. 46 Nutrient application for the various treatment in the agronomic experiment (the Nutrient appl table) .................................................... 48 Cultivar characteristics (the Cultivar Characteristics table) ........................................................................................................................... 53 The cost for inputs used in the various experiments (the Costs table)......................................................................................................... 58 Calculation of the crop response variables for each experimental field and treatment (the Calc_Plot table) ............................................. 61 Identification of the diagnostic trials experiment (the DTexper_ident table) .............................................................................................. 67 Composition of the fertilizer used in the experiment (the Fert_composition table) .................................................................................... 73 Description of the treatments in terms of fertilizer type and amounts applied (the Treatment table) ....................................................... 77 Partners associated with the experiment and project (the Partner table) ................................................................................................... 79 Project information (the Project_info table) ................................................................................................................................................ 81 Land use and management history of the field where the trials is conducted (the Field_hist table)........................................................... 82 Data on the management of the experimental field (the DT_mngt table) ................................................................................................... 87 Design of the diagnostic trials experiment (the DT_design table) ................................................................................................................ 93 Identification for the individual diagnostic trial (experimental field) (the DT_ident table) .......................................................................... 97 Unique identifier for the plot as part of an experimental field (the Plot_ident table) ................................................................................. 98 Backing up (the Backingup table) .................................................................................................................................................................100

Relationships ....................................................................................................................................................................................................100

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Table occurrences ........................................................................................................................................................................................100 Relationship details ...................................................................................................................................................................................... 103

Layouts .............................................................................................................................................................................................................109 Value lists ......................................................................................................................................................................................................... 143 Accounts ..........................................................................................................................................................................................................146

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Introduction and purpose of the DTA database The African Soil information Services AFSIS has and is conducting a large number of diagnostic trials. These are agronomic trials with the purpose to diagnose soil health constraints (soil nutrient limitations as well as other soil constraints like soil pH, SOC and soil secondary and micronutrient limitations) that may affect agricultural productivity. To this end agronomic data is gathered for the various treatments conducted within the diagnostic trials. At the same soil samples are taken and analyzed and also plant tissue is sampled and grain and stover samples are taken and analyzed to allow for full diagnoses of soil health constraints. The full set of data pertaining to the diagnostic trials consist therefore of agronomic data (yield and crop growth characteristics), soils data and data related to the nutrient concentrations in plant tissue, grain and stover. In this document we describe the database that is designed to keep the agronomic data and data associated with the diagnostic trials, like location, site characteristics, design of the trials and other. The data related to the soil and plant samples analyses are stored in a separate database files. These databases are linked such that data on soils, plant and agronomy can be accessed and retrieved that are related to the same diagnostic trial. At the same time data from the Land Degradation Surveillance Framework (LDSF) needs to be accessible for the analyses of the data from the diagnostic trials. The soil and plant databases as well as the data infrastructure linkages to other databases are described in separate documents. Though developed to store and maintain data related to the diagnostic trials conducted during the first phase of the African Soil information Services project, the aim is to develop a generic tool that allows for storage of data from multi-locational agronomic trials that could serve as a backbone for AFSIS future agronomic work. Or, for that matter, for any organization that conducts these kinds of trials. There have been and are many projects that conduct various kinds of agronomic trials (more specifically nutrient omission trials, nutrient response trials, ISFM trials and other), the data of which is being kept in different formats (often is some kind of spreadsheet) and often, if at all available, not accessible in any kind of way. We certainly try to avoid that this will be the case with the very rich data set that is being generated currently within the AFSIS project phase 1, and want to make sure that the data will become and remain available for further use. Moreover, our aim is very much to develop a generic structure for the database that allows accommodating data from a diverse range of agronomic trials, and to populate and expand the database with data from AFSIS agronomic trials (diagnostic and other) as well as from other sources from past and future agronomic trials to generate an increasingly rich and valuable data set. We hope that the development of this database may contribute to the establishment a central repository for data related to agronomic trials. AGRA, the BMGF and CIAT-TSBF could play an important role towards realizing this. In order to generate such a flexible structure the database is designed such that it allows for entry of data from agronomic trials with different crops, that allows for the specification of the design of the experiments and specification of the treatments implemented in the trials and that allow for trials that are conducted on multiple as well as on single locations as well for those that are replicated over various seasons or years. We realize very well that it is impossible to oversee all possible types of trials that are being conducted often with a different purpose in mind and to develop a database that caters for the storage of data in all those instances. But, if the database is

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structured well it will allow for expanding the database by adding additional tables to allow for storage of additional types of data and information, without having to restructure the whole database. We hope that this database will evolve with the addition of new datasets, while at the same time realizing the restriction to the type of data and experiments this database is intended for as well as the application and type of data analyses it will serve. What is described in this document is the core of the database, which refers to data storage. In our case this does not always relate to the primary or original data as it is collected in the field. Often the way the data is recorded in the field (or in the lab for that matter) is different even if there are protocols for how the trials are implemented and how measurements are done, as is the case with the AFSIS diagnostic trials. So often unit conversions, or calculation of certain parameters like ‘basal area’ of the plant stem or grain yield, for example, are carried out rather then the data related to original measurement (e.g. basal diameter, or basal circumference) is being entered. It assumes that a certain level of processing (in our case referred to level 1 processing) has been done. It also assumes that quality control is conducted on the original data to get the derived variables like grain yield and before the data is entered into the database. The quality control procedures are described in a separate document and are not further discussed in this document. It also implies that the original data files need to be kept for possible future reference, and those together with the documents on the quality control are considered to be part of the database in the broader sense. We have provided for opportunity to store information (meta-data) on the origin of the data and location of the original data files in the current database. Depending on the application and use of the data it is possible to generate layouts or tables that present the data in a way that it can be easily used for exporting (e.g. for further analyses) or reporting. This may involve some level of processing or handling of the data for which scripts are used. This may refer to the importing of data as well. As automation of these functions is specific for the particular application or even data set, these are not included in the current database set up. We consider this to be part of a shell around the core that deals with specific application. We have included some of these layout and scripts for specific use in relation to the AFSIS diagnostic trials that may then serve as examples for developing tools for similar application. To preserve the quality of the data and integrity of the database extensive use is made of look-up tables to enter data automatically by copying the data from related tables where applicable. Where applicable data is entered by calculation based on data from data fields in the same or other tables. These data fields are not supposed to be entered manually or to be imported from external sources, though it is possible to overwrite the calculated value. Also extensive use is made of ‘value lists’ for validation of the data entered, and validation is done by specifying the maximum number of characters allowed or specifying ranges for the data values. In many cases this means that the value list need to be updated if new data values are entered. For example, if data from an additional and new sentinel site is being entered the value list of the sentinel site names need to be updated to allow for the entry of that new name.

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Database design and structure - principles The database is structured around a number of basic entities (or objects if you like) that we think apply to any kind of agronomic trial or set of trials (i.e. experiment). These are the following: Field: The field refers to the particular location where a trial is conducted. This may refer to a farmer’s field or a field on a research station for example. The same field may be used for the same or other trials in different seasons (or years). Each field has a specific location and there are specific site characteristics. Also each field has it’s own history in terms of land use and land management. Each field must have it’s own unique identifier. Diagnostic trial (DT): The diagnostic trial refers to the one particular instance of a diagnostic trial. We have called it diagnostic trial (DT), but whether it is a diagnostic trial or any other kind of agronomic trial is not of any consequence. One particular trial is implemented on a particular field and in a particular cropping season or year and adheres to a particular experimental design. Each DT is identified by a unique code DT experiment: The DT experiment refers to a set of diagnostic trials (or any other kind of agronomic trial) that is conducted in a particular area in a particular season or year according to a particular design. Even if the experiment is repeated in a second year according to exactly the same design it is still considered another experiment. In specific cases the number of diagnostic trials may refer to one. The experiment uses one specific test crop for all the trials in the experiment. That is different crops can be used in the sentinel site in that particular year and season for the different fields, but these would then count as different experiments. The database does not allow for a combination of crops to be specified (e.g. intercropping) as part of the design. This problem can partially be overcome by defining two experiments that are then conducted in the same field, but this is not ideal. Plot: The plot is one of the plots in a DT. We assume that in a particular DT experiment a plot is assigned a particular treatment and that this is the same for all the DT in that experiment. The design of the database does not allow for the same treatment being assigned to different plots in the various DT, though this may be different is a subsequent experiment even though it might contain the same treatments. All observations related to crop growth and yield are done on a plot level and the plot form the smallest unit of observations (even though measurements may be done on individual plants within the plot for example). The plot is different type of entity than ‘field’ in that it does not refer to a specific location; it is rather more of the same nature as ‘diagnostic trial’. The plot has a unique identifier that is used to link to tables that contain the data on plot related observations Tables have been created that provide the identification for each of the above entities and the relationships between these tables are established. They form the core of the database through which all other tables are linked.

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Other entities that likewise apply to the diagnostic trials or any other kind of (set of) agronomic trials: Sentinel site(SS): The sentinel site refers to the area where the trials are being conducted. In the AFSIS terminology the sentinel site is defined as a 10 by 10 km squared area. If forms the basic entity for the LDSF work and other surveys that are carried out. This is, however, not of any consequence for the design of the database and the sentinel site may refer to any kind of area of any size or form as long as it denotes the area where the set of trials are being conducted. The first four characters of its name denote the sentinel site and these should be unique to the sentinel site. This convention should be adhered to for any other (type) experiment for which the data is being entered. In case of AFSIS the sentinel site is divided in 16 tiles of 2.5 by 2.5 km and for each tile a circular area is demarcated within which the sampling points for the LDSF are located (it defines the ‘cluster’). Also the diagnostic trials are located within a ‘cluster’ or a tile. The cluster number forms part of the identification code of an experimental field. It serves in the case of AFSIS to link to other observations that are being made within the cluster and therefore the referential integrity is important (the cluster number should be the same as the cluster number used for the same cluster in any of the other AFSIS databases). However, for any other project or program a different design can be adopted. The only implication is that the number that identifies the field within a sentinel site (area) does not exceed three characters. Treatment: The treatment consists of a combination of different types and amount of inputs. The inputs in our case refer to organic and inorganic fertilizers as well agricultural lime. In the case of AFSIS only ‘manure’ is being used as organic fertilizer, but there are no restrictions to define other types of organic fertilizer or organic matter sources or of inorganic fertilizer sources for that matter. We include the plant density as part of the specification of the treatment as evaluating varying plant densities may be a specific objective of the study and because this will have a direct affect on the amount of fertilizer available per plant. Fertilizer: Fertilizer refers to the specific type of fertilizer or organic resource used in the specific DT-experiment. It is assumed that for an experiment (as set of trials conducted in a particular season) the same resources are being used. It is therefore important the fertilizers and manure for example are well mixed before it is being applied to the various fields in the experiment to assure the same quality of inputs for the various fields. Manure or even the inorganic fertilizer may be of different composition depending on where it is sourced from (e.g. the brand). Even the quality of the same fertilizer may be variable and it is therefore important to ascertain the composition of the fertilizer that is being used in that particular year or season for that particular experiment. The type of fertilizer (e.g. urea), and the DT-experiment (i.e. the sentinel site, and the year and season when the experiment was conducted) therefore defines the primary key for records in this table.

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Cultivar: In every experiment a particular test crop is used, and this should be the same cultivar for all the fields that are part of the experiment. The cultivar characteristics refer to yield potential, maturity group and the like that are important in evaluating the performance of the crop in the trials.

Overview of the Diagnostic Trials Agronomic database The database file is called the Diagnostic Trials_develop.fp7, to reflect that this is the developer copy. Once the development stage has been finalized, debugging has been, etc. the “develop” should be removed from the file name. The overview of the database in terms of number of tables, relationships, layouts etc is given in the table below. There are currently 25 tables in the database. There is one table “Backingup” that contains a field “Count Field” that is just intended to count the number of times that database is opened and closed and that it is part of a script that creates a back up of the database file every fifth time changes have been made to the database. However, in order to make this work and to perform the script the table needs to be related to any table in the database. That is the reason why in the table below the Perform script is indicated as being “Off”. For each table there is a default and standard layout. Quite a number of these layouts should be reviewed and finalized, such that the same style is used throughout and such that fields from related tables are displayed to be able to check whether the correct identifiers are entered and to verify whether the relationships to other tables are properly established (e.g. when yield data for a particular plot is entered, you should be able to access the data related to the experimental field, the experiment itself etc.). So far, custom layouts or the purpose of reporting and importing/exporting of data has not yet been defined. There are quite a number of value lists defined to ensure the correct data is being entered. There is probably some redundancy in the value lists as there may be several value lists that represent earlier versions of the same value list. This needs to be reviewed. All the above is documented in more detail in the subsequent section in this report below. Table 1. Content and settings for Diagnostic Trials_develop.fp7

Overview File Options

Tables 25 Default custom menu set Custom Menu Set 1

Relationships 21 When opening file

Layouts 25 Login using Account Name; Account= Admin

Scripts 1 Switch to layout Off

Value Lists 44 Perform script Off

Custom Functions 0 When closing file

Accounts 3 Perform script Off

Privilege Sets 0

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Extended Privileges 7

File Access (in / out) 0 / 0

FileMaker Data Sources 0

ODBC Data Sources 0

Custom Menu Sets 0

Custom Menus 0

Tables, the number of fields defined and number of records for each table In the table below all the tables are listed that have been defined in this database. The number of records is not of any significance at this moment as only the data from the trials conducted during the first two seasons in Nkhata Bay have been entered for purpose of testing the database and there are quite a number of records still from the earlier version of the database. It is obvious that the database needs to be populated with the data form the various trials and subsequent reports will give the status in terms of the number of records contained for each table. Table 2. List of tables together with the statistics and occurrence in the relationship graph

Table Name Statistics Occurrences in Relationship Graph

SentinelSite 13 fields defined, 8 records SentinelSite

Field_loctop 14 fields defined, 311 records Field_loctop

Yld_msr 22 fields defined, 3154 records Yld_msr

Crop_grwth 48 fields defined, 67289 records Crop_grwth

Rainfall_temp 5 fields defined, 2353 records Rainfall_temp

SoilMoisture 10 fields defined, 0 record SoilMoisture

Field_ident 4 fields defined, 0 record Field_ident

CropStressFactors 15 fields defined, 0 record Pest&Diseases

WeatherStations 4 fields defined, 15 records WeatherStations

Nutrient appl 15 fields defined, 24 records Nutrient appl

Cultivar Characteristics 26 fields defined, 9 records Cultivar Characteristics

Costs 16 fields defined, 3 records Costs

Calc_Plot 19 fields defined, 151 records Calc_Plot

DTexper_ident 16 fields defined, 0 record DTexper_ident

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Fert composition 15 fields defined, 0 record Fert composition

Treatment 6 fields defined, 0 record Treatment

Partner 5 fields defined, 0 record Partner

Partner_SS 2 fields defined, 0 record Partner_SS

Field_hist 18 fields defined, 0 record Field_hist

DT_mngt 20 fields defined, 0 record DT_mngt

DT_treatment 7 fields defined, 0 record DT_treatment

DT_ident 3 fields defined, 0 record DT_ident

Plot_ident 3 fields defined, 0 record Plot_ident

Backingup 1 field defined, 1 record Backingup

Description of the fields contained in each of the tables

The Sentinel site table The records in the sentinel site table contain data that describe the general characteristics of the sentinel site; that is the location (centre coordinates), climate data, and other. Consider including general characteristics on the landscape, i.e. landform (physiognomy of the landscape, hypsometry), land use and other. The idea being that, when generating reports of the results form the diagnostic trials or other, information on the general characteristics of the sentinel site can be included drawing the relevant data from this table. There should be one occurrence for any particular sentinel site in this table and this should be the only place where the full name of the sentinel site is provided. Entry of the value for the name of the sentinel site is currently being governed by a value list that contains the names of all the possible sentinel sites. However, it would be better to define this field as part of a value list itself so that it can be used for the entry of the sentinel site name in any instance where the name of the sentinel site needs to be provided. In the table below the layout in which the fields appear have not been specified as the all fields by default appear on the SentinelSite layout and not in any other layout at the moment. This may change in future though. Also it doesn’t indicate the scripts to which the fields may be subjected, because there are none at the moment. This may change as well the moment automated reporting on the results from the trials is considered. Table 3. Field defined for the SentinelSite table

Field Name

Type Options Comments In Relationships In Value Lists

SSname Normal, Text

Auto-Enter: Allow editing

Full name of a sentinel site

Consider defining a value list for sentinel site

10

Validation: Only during data entry

Storage: Repetitions: 1 Indexing: Minimal Automatically create

indexes as needed Index Language: English

names that references to this field, in case the full name is required for data entry in any other table

SScode Calculated, Text

Calculation: Context table:

SentinelSite Left

(SentinelSite::SSname;4) Storage:

Repetitions: 1 Indexing: Minimal Automatically create


Four letter code for the sentinel site (first four letters of sentinel site name). This is automatically generated and the SScode rather than the full name is used for building relationships with other tables

SentinelSite=Field_ident Partner_SS=SentinelSite

SScode For purpose of validation during entry of the SScode in any other table. It means that the SSname should be specified first in this table before any other data related to the sentinel site is being entered

SSYCoord Normal, Number

Validation: Only during data entry Strict data type:

Numeric Range: -70 - 70 Error message: Value in

decimal degrees; should be negative when south of the equator and positive when north of the Equator

Storage: Repetitions: 1

Y coordinate or longitude of the center of the sentinel site. This should be in Decimal Degrees and negative (-) for values south of the Equator and positive (+) north of the Equator

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Indexing: None

SSXCoord Normal, Number

Validation: Only during data entry Range: -180 - 180 Maximum data length: 9

Storage: Repetitions: 1 Indexing: No

X coordinate or latitude of the centre of the sentinel site. This should be in Decimal Degrees which should be negative (-) for values west of the Greenwich meridien and positive (+) east of Greenwich meridien

Country Normal, Text



Storage: Repetitions: 1 Indexing: Minimal Automatically create


Name of the country where sentinel site is located

RnDist Normal, Text


Validation: Only during data entry Strict validation

Storage: Repetitions: 1 Indexing: None

Rainfall distribution pattern in the sentinel site. Entry governed by value list “rainfall distribution”

MAR Normal, Number



Mean annual rainfall (in mm) in a sentinel site

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S1Rain Normal, Number




Average rainfall (mm) during the first (or the only) crop growing season in the area of the sentinel site

S2Rain Normal, Number




Average rainfall (mm) during the second crop growing season (leave blank if area has only one growing season in an year)

Tmax Normal, Number




Mean maximum daily temperature (Celcius)

Tmin Normal, Number




Mean minimum daily temperature (Celcius)

Windspd Normal, Number


Average windspeed within the sentinel site

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Tmean Normal, Number




Mean daily temperature (Celcius)

The experimental field – location and site characteristics (the Field_loctop table) This table contains the data related to the location and site characteristics of the particular field where the diagnostic trial is implemented. Value lists are used to improve the uniformity in which the data is entered and to improve the functionality of the database in terms of querying the database, for example in relation to the topographic position or slope. As the data in the field may not have been recorded in this way or according to the classification system specified, this may require some ‘translation’ or ‘mapping’ o the values as recorded in the field onto the classes as specified. However, the values in those fields may be edited or overwritten allowing still entering the data as it has been recorded. To some extent and as is the case in relation to the distance of the field to the homestead, the class of distance to homestead is assigned automatically and the formula could be changed in case a different classification system is used and subsequently to automatically update this field. A similar approach could be used for other fields as well, like the slope for example. Table 4. Data fields for the Field_loctop table

Field Name Type Options Comments In Relationships In Value Lists

FieldID Normal, Text


Validation: Only during data entry Unique Value list: FieldID Maximum data length: 9

Unique identifier for the experimental field. The FieldID consists of the SScode, the cluster number and number of the field and is automatically generated during data entry in the Field_ident table and for data entry and validation the

Field_loctop=Field_ident

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Storage: Repetitions: 1 Indexing: All Index Language: English

corresponding value list is used. Therefore the data should have been entered in the Field_ident table prior to entering the related data in this table

Village Normal, Text




Name of the village within which experimental field is located

Frmr_name Normal, Text




Name of farmer in whose farm the diagnostic field is placed (Consider putting the farmer’s name in a separate table and linking it to the field and restrict access to that table; the name of the farmer is private and confidential and should not be accessible to everyone)

Dist_home Normal, Number



Storage: Repetitions: 1 Indexing: None Automatically create


The distance from the farmer homestead to their field being used for diagnostic trials (in km)

Dist_home_cls Calculated, Number

Calculation: Context table:

Field_loctop

Classification of the field based on distance from the homestead (Homefield<0.3km, Midfield=0.3-1.0,

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If ( Field_loctop::Dist_home < .05 ; "Homefield" ; If ( Field_loctop::Dist_home < .14 ; "Midfield"; If ( Field_loctop::Dist_home > .14 ; "Outfield")))


Outfield>1.0)

FLat Normal, Number




Y-coordinate or latitude of the field. This should be in decimal degrees, which should be negative (-) for values south of the Equator and positive (+) North of the Equator.

FLong Normal, Number




X-coordinate or longitude of the field; This should be in decimal degrees and negative (-) for values west of the Greenwich Meridian and positive (+) East of the Greenwich Meridian.

Elev Normal, Number




Elevation in masl

Water_supply Normal, Text


Indicate whether water supply characteristics for the field (i.e.

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Validation: Only during data entry Value list: Water supply


rainfed, post-flooding [residual moisture] or irrigated)

Irrig_type Normal, Text


Validation: Only during data entry Value list: Irrigation


The irrigation method used on this field: basin, furrow (i.e. types of surface irrigation), sprinkler or drip

TopPos Normal, Text


Validation: Only during data entry Value list: topography


Topographic position of the field (summit, upper and middle slope, lower slope, bottomland, plain, terrace)

Slope Normal, Text


Validation: Only during data entry Value list: Slope


Slope class according to USDA SSM (0-2% - level, 2-8% - gently sloping, 8-16% - sloping, 16-30% - moderately steep, 30-65% - steep, > 65% very steep [percetnage by approximation])

Soil Normal, Text


Validation:

Soil type according to FAO classification system

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Only during data entry Value list: Soil

Classification (FAO) Storage:

Repetitions: 1 Indexing: None

Drainage Normal, Text


Validation: Only during data entry Value list: drainage


Drainage class according to USDA SSM (modified)[poor - requires drainage for crop to grow, imperfect - evidence of gleying [mottles], well - no mottles or other evidence of imperfect drainage, excessive - sandy and porous soils)

Harvest data and yield measurements: the Yld_msr table This table contains data related to observations regarding the harvest obtained on a particular plot in a particular experimental field. It stores data on the nominal size of the plot harvested: the size of the net plot or the whole plot in case the whole plot has been harvested. This is the nominal value and does not need to reflect the actual or effective size of the plot as determined by the number of rows and number of plants per rows in the (net) plot. E.g. for the diagnostic trials we measure a plot of 5 meter by 5 meters but if the distance between rows is 75cm and between planting stations within the row is 25 and you have 7 rows and 20 planting stations per row in a plot, the effective plot size is 26.25 meter squared rather than the nominal value of 25 meter squared. The inter row and intra row spacing based on which the effective plot size can be determined is specified elsewhere. Here the harvested area is given as recorded which may reflect the nominal or actual size of the plot. Further it allows for entering data on the number of plants, the number of ears (heads, or cobs or panicles) harvested, the fresh weight of the stover harvested and of the total of ears (heads, cobs or panicles) harvested as well as of the fresh weight and dry weight of the subsample of both the stover and ears to determine the dry weight percentages. In certain cases a fixed number of the best plants are harvested for each plot separately and provision has been made to enter the relevant data in that case. The number of plants sampled in this case is specified in the table where information on the design of the experiment is provided. This applies as well to the number of ears (heads, cobs or panicles) in the subsample, or the number of plants in the subsample if that is done in a systematic way. Furthermore it allows storing weight data on a fixed number of grains. For maize, for example, this refers to the weight of a 100 grains is often used as reference, whereas for sorghum this would refer to the weight of a 1000 grains. So the number is dependent on the test crop used which is specified in the table containing the data on the specific type of experiment.

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The yield is given as total grain yield and total stover yield in tons per ha related to what actually has been harvested from the (net) plot, and in an adjusted value for the grain and stover yield where corrections have been made for the number of plants that have been lost in the (net) plot harvested. The yield data is independent and entered separately from the other data stored in these records. That is, the yield data may not be automatically derived from the other data provided. This is because not all relevant data may be registered; e.g. in some case the moisture content of the grain from the subsample is measured (or provided) rather than the dry weight of the subsample and dry weight will thus not be available. Second, it may be that the value for the grain or stover yield has been corrected because of errors in the input data and that these corrections have been calculated in directly rather than being reflected in the input data and then calculated afresh. I.e. we assume that assume that quality control on the data and corrections on the yield data have been done prior to entering the data in the database and we cannot always assume that corrections have been made on the original or input data (like the dry weight of the subsample for example). Table 5. Specification of fields for the Yld_msr table


PlotID Normal, Text


Validation: Only during data

entry Not empty Maximum data

length: 16 Storage:

Repetitions: 1 Indexing: All Index Language:

English

Identification number for an experimental field plot (unique ID, defined by SScode, year and season of the experiment the field was part of, the cluster and field number and the plot number). The corresponding layout displays fields from related tables that indicate the sentinel site cod, year of the experiment, the test crop used and the field identification as a means to check that the correct PLotID is entered and to validate the relationships between the concerning tables. It assumes that the corresponding records in the related tables like Plot_ident have been entered first

Yld_msr=Plot_ident

TGrnYld Normal, Number



Total grain yield (dry weight) in tons per hectare, based on actual yield obtained from harvested area within the plot without any further corrections for

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entry Storage:


number of plants or ears/heads harvested or other compensation for any damage to crop or harvest

TStvYld Normal, Number



entry Storage:


Total stover yield in tons per ha (not corrected for any plant loss or other damage to crop and plant)

TGrnYld_adj Normal, Number



entry Storage:


Total grain yield in t/ha corrected for plant loss and other possible damage to crop and harvest

TStvYld_adj Normal, Number



entry Storage:


Total stover yield (dry weight) in t/ha corrected for possible loss of plants

Harea Normal, Number



entry

Area harvested for this particular plot in meter squared

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PLantno Normal, Number



entry Storage:


Number of plants harvested in this particular plot

Earno Normal, Number



entry Storage:


Number of ears (maize) or heads (sorghum) harvested in this particular plot

StvFWp Normal, Number



entry Storage:


Stover fresh weight in kg harvested form this particular plot

CobFWp Normal, Number



entry

Fresh weight harvested cobs or heads in kg from this particular plot (for maize this is the total weight of the cobs harvested, for sorghum the weight of heads harvested)

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StvFWsplts Normal, Number



entry Storage:


Stover fresh weight of the selected best plants from the plot in g (number of best plants harvested should be indicated in the DT design table)

StvDWsplts Normal, Number



entry Storage:


Stover dry weight from selected best plants in g (number of plants selected should be indicated in the design of the trial; if not measured this value should be calculated from FW/DW of the subsample)

CobFWsplts Normal, Number



entry Storage:


Cob fresh weight in g of selected plants (number of plants selected is indicated in the DT design table; may refer to head/panicle in case of sorghum, millet or other)

GrnDWsplts Normal, Number



entry Storage:

Grain dry weight of selected plants in g (number of selected plants should be specified in the DT design data)

22


StvFWSS Normal, Number



entry Storage:


Stover fresh weight of the sub-sample in g (sub-sample of stover is merely to determine fresh weight - dry weight ratio; how the sub-sample is taken is specified in the design of the DT)

StvDWSS Normal, Number



entry Storage:


Stover dry weight of the sub-sample in g

CobFWSS Normal, Number



entry Storage:


Fresh weight of the cobs/heads in the subsample in g (number of cobs/heads in the sub-sample is indicated in the DT design)

GrnDWSS Normal, Number



entry Storage:

Repetitions: 1

Dry weight of the grain of the sub-sample in g (related to the grain harvested from the cobs/heads only)

23

Indexing: None

GrnFW100 Normal, Number



entry Storage:


Fresh weight of 100 or 1000 (depending on the crop) grains in g

GrnDW100 Normal, Number



entry Storage:


Dry weight of 100 or 1000 (depending on the crop) grains in g (maize only)

Crop growth data (the Crop_grwth table) The crop growth table contains data related to development of the crop in terms of height, base area, number of leaves and panicle length. For some crops like rice and millet the number of tillers is an important indicator and provisions have been made to enter data on those measurements as well. Rather then allowing entering records for the measurement on each individual plant this table has been constructed such that one record is entered for each plot and one record for each measurement in time (that is for each growth stage). The latter is done because not in all instances the measurements in time are done in a uniform way. This has implication for the importing of data depending on how the data is organized in the original data files. In case where there are separate records for measurement on each individual plant sampled in the plot, it requires the table in the original data file to be pivoted before the data can be entered. There may be other solutions to this problem, but this seems to be the most straightforward. The base area should be given in cm2 whereas generally either the basal diameter (diameter only or diameter and width of the stem at the base) or basal circumference is being measured. Because of the different measurements in the various experiments we only allow the unifying feature to be entered in the database. It should be specified in the design of the experiment what exactly has been measured, because the results will be different whether diameter is measured only or whether diameter and width of the stem is being measured, for example. So it is assumed that the conversions are done on the original data before it is being entered into the database.

24

The average plant height, number of leaves, number of tillers, average base area, etc. are automatically calculated. Table 6. Data fields for the Crop_grwth table


PlotID Normal, Text


Validation: Only during data entry Maximum data length: 10


Unique Identification for the plot inder consideration (defined by the field ID and the plot number and the experiment the corresponding field is part of). In the corresponding layout fields from related tables to indicate the sentinel site, year and season of the experiment, the test crop used, the field identification and the treatment corresponding to this plot should be specified as a means to check that the correct PLotID is entered and to validate the relationships between the concerning tables. It assumes that the corresponding records in the related tables like Plot_ident have been entered first

Crop_grwth=Plot_ident

Date Normal, Date




Date of plant parameter measurement

Period Normal, Number



Storage:

Targeted period of sampling for crop growth measurements, given in days after planting (this does not need to coincided with the actual number of days after planting that the measure took

25

Repetitions: 1 Indexing: None Automatically create indexes

as needed Index Language: English

place, which can be determined from the data of measurement; it is rather used to indicate the growth stage the measurement refers to.

Hght_plt1 Normal, Number




Plant height; The height is measured in cm from soil surface to tip of the tallest panicle





Height (cm) of the second plant measured in this plot





Height of the third plant measured in this plot (cm)





Height of the fourth plant measured in this plot (cm)

26





Height of the fifth plant measured in this plot (cm)





Height of the sixth plant measured in this plot (cm)





Height of the seventh plant measured in this plot (cm)





Height of the eighth plant measured in this plot

Plt_hght Calculated, Number

Calculation: Context table: Crop_grwth Average (

Crop_grwth::Hght_plt1;Crop_grwth::Hght_plt2;Crop_grwth::H

Average height of the plants measured in this plot (calculated)

27

ght_plt3;Crop_grwth::Hght_plt4;Crop_grwth::Hght_plt5;Crop_grwth::Hght_plt6;Crop_grwth::Hght_plt7;Crop_grwth::Hght_plt8)


BArea_plt1 Normal, Number




Basal area of stem of the first plant measured in this plot (cm2)





Basal area of second plant measured in this plot during this period (cm)





Basal area of the stem of the third plant measured in this plot (cm2)



Validation:

Basal area of stem of fourth plant mearued in this plotduring this period (cm2)

28

Only during data entry Storage:






Basal area (cm2) of the fifth plant measured in this plot





Basal area of the sixth plant measured in this field in this particular period (cm2)





Basal area of the seventh plant measured in this plot in this period (cm2)





Basal area of the stem of the eighth plant measured in this field during this period (cm2)

Plt_barea Calculated, Calculation: Average of the base area of the plants

29

Number Context table: Crop_grwth Average (

Crop_grwth::BArea_plt1;Crop_grwth::BArea_plt2;Crop_grwth::BArea_plt3;Crop_grwth::BArea_plt4;Crop_grwth::BArea_plt5;Crop_grwth::BArea_plt6;Crop_grwth::BArea_plt7;Crop_grwth::BArea_plt8)


measured in this field during this period (calculated)

Lfno_plt1 Normal, Number




Number of leaves counted of first plant measured in this plot





Number of leaves of the second plant measured in this plot during this period





Number of leaves of third plant measured in this plot during this period

30





Number of leaves counted on the fourth plant in this plot during this period





Number of leaves counted on the fifth plant in this plot during this period





Number of leaves counted on the sixth plant in this plot during this period





Number of leaves counted on the seventh plant in this plot during this period




Storage:

Number of leaves counted on the eighth plant in this plot during this period

31


Plt_lfno_avr Calculated, Number


Crop_grwth::Lfno_plt1;Crop_grwth::Lfno_plt2;Crop_grwth::Lfno_plt3;Crop_grwth::Lfno_plt4;Crop_grwth::Lfno_plt5;Crop_grwth::Lfno_plt6;Crop_grwth::Lfno_plt7;Crop_grwth::Lfno_plt8)

Storage: Repetitions: 1 Indexing: None Automatically create indexes


Average number of leaves per plant in this plot during this period (calculated)

Tillno_plt1 Normal, Number




Number of tillers for the first planting station sampled in this plot during this period





Number of tillers in second planting station sampled in this plot during this period

Tillno_plt3 Normal, Auto-Enter: Number of tillers in third planting station

32

Number Allow editing Validation:




sampled in this plot during this period





Number of tillers in fourth planting station sampled in this plot during this period





Number of tillers in fifth planting station sampled in this plot during this period





Number of tillers in sixth planting station sampled in this plot during this period



Validation:

Number of tillers in seventh planting station sampled in this plot during this period

33







Number of tillers in eight planting station sampled in this plot during this period

Plt_tillno_avr

Calculated, Number


Crop_grwth::Tillno_plt1;Crop_grwth::Tillno_plt2;Crop_grwth::Tillno_plt3;Crop_grwth::Tillno_plt4;Crop_grwth::Tillno_plt5;Crop_grwth::Tillno_plt6;Crop_grwth::Tillno_plt7;Crop_grwth::Tillno_plt8 )


Average number of tillers developed per planting station for this plot during this period

PanLgth_plt1

Normal, Number




Length (in cm) of individual panicle of first plant sampled in this plot at harvest

PanLgth_plt2

Normal, Number


Length (in cm) of individual panicle of second plant sampled in this plot at

34



harvest

PanLgth_plt3

Normal, Number




Length (in cm) of individual panicle of third plant sampled in this plot at harvest

PanLgth_plt4

Normal, Number




Length (in cm) of individual panicle of fourth plant sampled in this plot at harvest

PanLgth_plt5

Normal, Number




Length (in cm) of individual panicle of fifth plant sampled in this plot at harvest

PanLgth_plt6

Normal, Number




Length (in cm) of individual panicle of sixth plant sampled in this plot at harvest

35

PanLgth_plt7

Normal, Number




Length (in cm) of individual panicle of seventh plant sampled in this plot at harvest

PanLgth_plt8

Normal, Number




Length (in cm) of individual panicle of eighth plant sampled in this plot at harvest

Plt_panlgth_avr

Calculated, Number


Crop_grwth::PanLgth_plt1;Crop_grwth::PanLgth_plt2;Crop_grwth::PanLgth_plt3;Crop_grwth::PanLgth_plt4;Crop_grwth::PanLgth_plt5;Crop_grwth::PanLgth_plt6;Crop_grwth::PanLgth_plt7;Crop_grwth::PanLgth_plt8 )


Average panicle length at harvest for this plot

Rainfall and temperature data (The Rainfall_temp table) This table is intended store data on precipitation as measured by rain gauges installed in the field by the project. In case there is an existing weather station in or near to the sentinel site that measures rainfall and temperature this data can be used as well. In this table

36

only records are entered if there is a rainfall event recorded (i.e. days with zero rainfall are omitted). Information on the location of the station and the organization responsible for the measurements are stored elsewhere. Further editing needs to be done on the specification of the fields in this table, with respect to storage of the data (indexing, which is not really needed and other) Table 7. Specification of the fields in the Rainfall_temp table


WSName Normal, Text




Name or code for the of the weather station even if only a rain gauge placement (by name of the village where it is located and sequential number for example)

Rainfall_temp∞WeatherStations

WDate Normal, Date

Auto-Enter: Validation:

Only during data entry Strict data type: 4 digit

year Storage:


Date of observation of weather parameter

DRain Normal, Number


Validation: Only during data entry Strict data type: Numeric Strict validation


Daily rainfall amount (mm) observed in a specific weather station

DTMax Normal, Number


Maximum temperature observed during a specific day

37



DTMin Normal, Number




Minimum temperature observed during a specific day

Soil moisture data (The SoilMoisture table) This table has been added provisionally to provide opportunity for entering data on soil moisture. So far, soil moisture has not been measured in any of the diagnostic trials, but it has been recognized as an important variable, especially to assess the effect of drought (or dry spells) using simulation models and that would allow us to compare yields between different seasons under different rainfall conditions. It is assumed the measurements are done using a Time Domain Reflectrometry soil moisture sensor or a Frequency Domain soil moisture sensor. Both measure volumetric soil moisture content. However, to increase flexibility and reduced dependence on particular measurement techniques, the table should be redefined to allow entering soil moisture data and specification whether soil moisture is measured using volumetric or gravimetric (based on weight) measurement techniques. This table needs to be worked on further to establish relationship with other tables and in specification on the data fields (auto-enter, storage and validation criteria). Table 8. Fields tentatively defined for the soil moisture measurements


PlotID Normal, Text


Identification key for the particular plot

Relationship should be established with the

38

Storage: Plot_ident table

Date Normal, Date

Auto-Enter: Validation: Storage

Date of soil moisture measurement in the field

MDuration Normal, Number

Auto-Enter: Validation: Storage:

Measurement period in Microseconds. This parameter is necessary to enable soil-specific calibrations in case of TDR and FD methods

ProbeDth Normal, Number

Auto-Enter: Validation: Storage: Repititions: 2

Refers to the depth to which probe rods are inserted, or the depth interval for which the measurement is valid (in which case a repeated field should be defined

VWC1 Normal, Number


Volumetric soil water content; first measurement out of three for the particular plot.

VWC2 Normal, Number


Volumetric soil water content; second measurement out of three for the particular plot

VWC3 Normal, Number


Volumetric soil water content; third measurement out of three for the particular plot

SVolume Normal, Number


The volume of soil sampled (equivalent to the auger volume or ring used for sampling)

SMassFW Normal, Number


The fresh weight of sampled soil (this is taken in the field

39

Storage:

immediately the soil is sampled – this could be the fresh weight of a bulked soil sample of three – if three is the standard number of samples taken for each measurement)

SMassDW Normal, Number


The dry weight of sampled soil [could refer to the dry weight of the bulked soil sample of three (this is taken after the soil is dried at 105 deg Celsius for 24 hours)

Identification of the experimental field (the Field_ident table) This table only serves to establish the relation between the sentinel site, the cluster and the field and to generate a unique identifier for the field that is subsequently used as the link to and primary key for all those tables that contain data specifically and only related to the field (like the Field_loctop table). Rather then using a unique sequential or random number for each field we have chosen to construct a code consisting of the SScode, the cluster number and number of the field within that cluster to identify the field. In this way the user will be able to identify the sentinel site and cluster directly from the code for the field, and it will aide the user in checking on and keeping control of the various data handling and processing steps, but it is a the expense of creating some redundancy in the data though not by repeating data fields across different tables that would compromised maintaining the integrity of the data base. The cluster number is not used to build any relationship because this database does not contain any data that is specific to the cluster only, but it provides for an important link to the LDSF database where data is organized per cluster. A ‘physical’ relationship could be established the moment it these databases will be integrated. For the moment the cluster number can be used as a find criteria and the data subsequently exported for integrated analyses of data from the diagnostic trials and data from the LDSF. It is as such important that the referential integrity is assured (i.e. the cluster number in the LDSF data base should refer to the same cluster as the one referred to by the same number in the DTA database). The code for the sentinel site is used to identify the sentinel site rather than it’s full name and it provides for the link between the tables that contain specific information on the sentinel site and sentinel site only. Table 9. Assigning a specific and unique identifier for the experimental field.

Field Name

Type Options Comments In Relationships In Value Lists

SScode Normal, Auto-Enter: Four letter code for the sentinel site SentinelSite=Field

40

Text Allow editing Lookup:

SentinelSite::SScode, do not copy, Don’t copy contents if empty

Validation: Not empty Value list: SScode Maximum data length: 4

Storage: Indexing minimal Automatically create index

as needed

(first for letters of the SSname) Validation is by value list, but it requires that first the 4 letter code needs to be added to the value list before any new sentinel site can be added, or if the value list is linked to SentinelSite::SScode the name of the sentinel site should be added to the SentinelSite table

_ident WeatherSations=

Field_ident

Cluster Normal, Number


Range: 1-99 Maximum data length: 2

Storage:

Number of the cluster This is entered as a number but when integrated in the code for the FieldID this is converted to text of two characters (e.g. 01 to 99)

Field Normal, Text

Validation: Not empty Maximum data length: 3 Error message: Specify the

number assigned to the field within the cluster (a number from 1 to 99 and a letter suffix for each test crop used in the same field, if more than one crop is used for the various plots in the field

Storage: Indexing: None

Number of the field within the cluster The number of fields within a cluster is generally less than 10. In case the experimental fields are not clustered a field number can be assigned using up to 4 digits, but entry should still be done using both fields. In experiments with crop rotations it can be that more than one crops is used in the same field. In that case the field is split, using the same number for the field but with a letter suffix to denote

41

the different crops and a new record is created for each additional crop in the field.

FieldID Calculated, Text

Calculation: Context table: Field_ident Field_ident::SScode & If (

Field_ident::Cluster < 10; “0” & Field_ident::Cluster; Field_ident::Cluster) & Field_ident::Field

Unique code for the identification of the field (automatically generated Features on the Yld_msr layout and should feature on any other layout where the FieldID should be displayed for internal control and validation

Field_loctop=Field_ident

Field_hist+Field_ident Field_Ident=DT_ident

FieldID

Pest and diseases (the CropStressFActors table) This table is meant to contain data on pest and diseases as it is observed at the plot level. Often incidence of pest and diseases are recorded at a more detailed level than can be captured in this table. In particular cases the damage to ears (counting the number of ears effected for the different categories of severity of the damage to the ears) can be recorded at harvesting and the data can be used to correct yield figures accordingly. Therefore, it is assumed that those recordings in the field that are intended to adjust or correct the yield data are accounted for in the yield data that is being entered. In this table the more general data is stored which generally refers to a score for the frequency or incidence of a particular pest and disease and the severity of the damage caused by the pest or disease. Value lists govern the entry for the frequency and severity score as well as the type of pest or disease. It often requires some kind of mapping of the scores used for the recording in the field to the scoring system used for data entry in this table. Also observations are done at particular points in time and at a particular growth stage (depending on the pest or disease under consideration). More details can be found in the protocol for the AfSIS diagnostic trials. In this table the dates of the observation are not recorded as it is assumed that these are done at the proper time and reflect the actual damage done to the crop. The data from this table can be used for further statistical analyses and evaluation of yields obtained for the various treatments. Incidence of pest and diseases can be correlated with the treatment as is often observed in the field. In this table the major or most frequently observed pest and diseases are recorded and it is not intended to provide an exhaustive record of incidence of all kinds of plant pest and diseases. It follows a rather practical approach to record the damage done to the crop rather than that is focuses on the recording of the exact nature of the plant pest or disease. The primary key for the records is the PlotID, since recording is done on a plot level basis. Table 10. Recording of incidence of pest and diseases at plot level


PlotID Normal, Auto-Enter: Id of the plot Pest&Diseases=P

42

Text Allow editing Validation:




Consider displaying the FieldID, DT_exper, and treatment description form related tables in the layout for visual checking

lot_ident

BirdDam_freq Normal, Number


Validation: Only during data entry Value list: Frequency


Frequency (percentage class) of the number of cobs damaged by birds (feeding) ([1] 'very few', [2] 'few', [3] 'common', [4] 'frequent', [5] 'very frequent'), asserted at physiological maturity or at harvest Validation options still need to be defined as well as the control style for this field in the related layout

BirdDam_sev Normal, Text


Validation: Only during data entry Value list: Severity


Majority or average score of severity of damage to cobs by birds asserted at physiological maturity or at harvest [(1) 'slight', (2) 'moderate', (3) 'severe', (4) 'complete'] Validation options still need to be defined as well as the control style for this field in the related layout

TermDam Normal, Number



Score or rating of termite damage to the crop at the plot level by frequency class of the number of plants that have lodged as result of

43

Value list: Frequency Storage:


termite attack ([1]=very few, [2]=few, [3]=common, [4]=frequent, [5]=very frequent) asserted at any stage before harvesting Validation options still need to be defined as well as the control style for this field in the related layout

StemBor_freq Normal, Number




Frequency class of the number of plants affected by stem/stalk borer ([1]=very few, [2]=few, [3]=common, [4]=frequent, [5]=very frequent) asserted at later vegetative growth stages or early reproductive growth stages Validation options still need to be defined as well as the control style for this field in the related layout

StemBor_sev Normal, Number




Severity of the damage by stem / stalk borer by majority class or average score ([1]=small amount shot-hole lesions on few leaves, [2]=shot-hole lessions common on several leaves, [3]=several leaves with shot-hole and elongated lesions, [4]=several leaves with elongated lesions, severe stem damage) Validation options still need to be defined as well as the control style

44

for this field in the related layout

DisName Normal, Text


Validation: Only during data entry Value list: Diseases_maize


The name of the disease affecting crop and whose data is being reported, e.g., maize streak virus (MSV)

Disaff_freq Normal, Number




Frequency class of the percentage of the number of plants affected by a viral or bacterial disease ([1]=very few, [2]=few, [3]=common, [4]=frequent, [5]=very frequent) Validation options still need to be defined as well as the control style for this field in the related layout

DisAff_sev Normal, Number




Severity of the affection by disease by majority class or average score ([1]=<10% of total plant leaf area affected, [2]=up to a third of total plant leaf area affected, [3]=up to two thirds of total plant leaf area affected, [4]=more than 2/3 of total plant leaf area affected) Validation options still need to be defined as well as the control style for this field in the related layout

CobRot_freq Normal, Number


Validation:

Frequency in percentage class of the number of cobs affected by cobrot (or termite feeding)

45

Only during data entry Value list: Frequency


([1]=very few - < 6%, [2]=few - between 6 and 16%, [3]=common - between 16% and 35%, [4]=frequent - between 35% and around 75%, [5]=very frequent - more than 75%) Validation options still need to be defined as well as the control style for this field in the related layout

CobRot_sev Normal, Number


Validation: Only during data entry Value list: Severity


Severity of the damage due to cobrot by majority class or average score ([1]=slight <10% of the cobs affected, [2]=moderate - up to a third of the cob affected, [3]=severe - up to two thirds of the cob affected, [4]=complete -more than 2/3 of the cob affected) Validation options still need to be defined as well as the control style for this field in the related layout

DSPeriod Normal, Number




Period or days after planting when drought scoring was undertaken (Drought Scoring Period).

DScore Normal, Number



Storage:

Scoring of drought effect on whole plot level using a scale of 1 (fully turgid leaves, green and unrolled) to 5 (severely wilted, yellow and rolled leaves)

46


Validation options still need to be defined as well as the control style for this field in the related layout

Plts_lodged Normal, Number




Total number of lodged plants due to windfall or other for the net plot or whole plot harvested at time of harvest

Plts_stand Normal, Number




Total number of plants standing (not lodged) for the plot (net plot or whole plot) harvested

Weather stations (the WeatherStations table This tables serves to store the coordinates of the weather stations whether this refers to an existing weather station or just a rain gauge established at a particular locations for the specific experiment. These records are linked to the sentinel site, rather than to a particular field because this will not always be the case. If the station is located in a particular field the link is to be established through the coordinates. Consider elaborating on this table further to include the organization responsible for managing the station, i.e. the data source. Details can be provided in the Partner table for which the relationship then needs to be established through the name of the organization as the match field. Further the storage options need to be reviewed. Indexing of these fields is not required and desirable. Table 11.


WSName Normal, Text


Name or code of the weather station or rain gauge placement (by


47



name of the village and sequential number for example)

WSXCoord Normal, Number




X coordinate or longitude of the weather station. This should be in decimal degrees which should be negative (-) for values west of the Greenwich meridian and positive (+) east of Greenwich meridian

WSYCoord Normal, Number




Y coordinate or latitude of the weather station; This should be in decimal degrees and negative (-) for values south of the Equator and positive (+) for values north of the Equator

SScode Normal, Text



Storage: Repetitions: 1 Indexing: Minimal Automatically create indexes


Four letter code for the sentinel site to which the weather measurements apply (location does not need to be inside the sentinel specifically)


WeatherStations=Field_ident

48

Nutrient application for the various treatment in the agronomic experiment (the Nutrient appl table) For each treatment in a particular experiment the nutrients applied need to be specified, as the crop performance is generally evaluated against the amount of the various nutrients applied. The nutrients referred to here are the macronutrients (N, P and K), the secondary (Ca, Mg and S) and the micronutrients. There should be one record for each treatment in a particular experiment and the identifier for the experiment and the treatment code therefore constitutes the primary key. The nutrients can come from the various sources, i.e. the different fertilizers used for that particular treatment. The fertilizers and the amount of fertilizer used is specified in a different table (the Treatment table); the composition of the fertilizers is specified in the Fert composition table. From these the amount of nutrients applied could be calculated automatically. However, this has not been implemented in this database so far, but should be considered for purpose of validation at least. Furthermore the relationships should be reviewed such that it is possible to retrieve data on the nutrients applied based on the plot identifiers such that results from the experiment and for each plot can be reported together with the nutrient applied on that particular plot for further analyses. That is some work on the layout for reporting still needs to be done Table 12.


DT Experiment Normal, Text

Auto-Enter: Allow editing Do not replace existing value

for field (if any) Context table: Nutrient appl

Calculation: /*SScode & Year & Season & Left ( Crop ; 1 )*/




Identifies the DT experiment conducted, by sentinel site, the year and season and test crop used The Auto-Enter option as currently implemented will not work, because an effective relationship between records is not established between the tables. Consider using the control style on the layout for entering data using the value list of this field from the related table and for validation.

Nutrient appl∞DT_treatment

Trt_code Calculated, Text

Auto-Enter: Context table: Nutrient appl Treatment::Trt_code

Number (two characters) that identifies the treatment. This is not a unique number, but together with

49

Storage: Repetitions: 1 Do not store calculation

results Index Language: English

the identifier for the DT experiment it provides a unique code for the treatment. Currently it has been programmed such that the Trt_code is entered automatically when the Trt_description is entered. It cannot be stored and is recalculated each time.

Trt_description Normal, Text





Code or abbreviation to describe the treatment (generally denoting the nutrients or substance added) In the layout in form view the entry is controlled by a drop down list that lists all the treatment descriptions entered in the database

Nutrient appl∞DT_treatment

N Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 200 Error message: A maximum

application rate of 200 kg/ha is assumed; if more is applied change the validation criteria for data entry in this field


The amount of nitrogen (nutrient element in kg/ha) targeted in the treatment; is calculated from the amounts of fertilizers used ('Treatment' table) and the composition of the fertilizer(s) (specified in the 'Fert_comp' table) Review indexing options

50

Indexing: None

P Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 120 Error message: It is assumed

that the P applicaton rate contributed from possible several fertilizer (organic and inorganic) amounts does not exceed 120 kg/ha. If it does, change criteria for validation of this field.


The amount of phosphorus (nutrient element in kg/ha) targeted in the treatment (calculated from the application rates of the possibly different organic and inorganic fertilizer sources used in the treatment) Review storage options

K Normal, Number



application rate of 100 kg/ha of K is allowed


The amount of potassium (nutrient element in kg/ha) targeted in the treatment from possible various fertilizer sources

Ca Normal, Number


Validation:

Amount of calcium element (kg/ha) targeted in the application

51



Mg Normal, Number




Amount of magnesium element (kg/ha) targeted in the application

S Normal, Number



of 50 kg/ha of S can be entered


Amount of sulfur element (kg/ha) targeted in the treatment, may originate from the various types of fertilizers used

Zn Normal, Number



of 20 kg/ha of Zn is allowed for this data field

Storage:

Amount of Zn element (kg/ha) targeted in the treatment

52


B Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 20 Error message: 20 kg/ha of B

is maximum allowed amount forthis data field


Amount of B element (kg/ha) targeted in the treatment

Mn Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 10 Error message: 10 kg/ha is set

as the maximum allowed value for this element in the validation of data entry for this field


Amount of Mn element (kg/ha) targeted in the treatment

Mo Normal, Number



Storage:

Amount of Molybdenum element (kg/ha) added in this treatment

53


Cu Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 10 Error message: 10 kg/ha is set

as the maximum allowed entry for this field


Amount of Cu element (kg/ha) targeted in the treatment

OM Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 10000 Error message: 1o tons of dry

organic matter has been set as the maximum allowed value for data entry


Amount of dry organic matter applied in this treatment (kg/ha)

Cultivar characteristics (the Cultivar Characteristics table) This allows recording the various characteristics of the cultivars used for the experiment. The name or code by which the cultivar is known is the primary key. The crop it concerns is specified in the table where the design of the experiment is described. It is assumed that in many cases, whether it concerns maize varieties or sorghum or rice, more or less the same characteristics are used to describe the

54

cultivar. We did include some characteristics that apply only to certain crops, like the number of tillers produced. This table contains important reference data against which the performance of the crop in the field is evaluated, like the yield potential for example. Definition of the fields in this table needs to be reviewed. For example, the number of records in this table will be rather limited and as such there is no need for indexing to speed up the find processes. Also criteria for validation have so far not been set. Table 13. Recording of the cultivar characteristics


Variety Normal, Text


Validation: Storage:

Repetitions: 1

Name of the variety Cultivar Characteristics=DTexper_ident

FlwDays Normal, Number



Repetitions: 1

Number of days to flowering (50% flowering)

GrnFillDur Normal, Number



Repetitions: 1

Duration of the grain filling stage (in number of days)

HIndex Normal, Number



Repetitions: 1

Harvest index: the ratio of grain to above ground biomass production

Kernels Normal, Number



Repetitions: 1

Maximum number of kernels per cob/panicle

55

LAI Normal, Number



Repetitions: 1

Maximum leaf area index achieved

LNmax Normal, Number



Repetitions: 1

Maximum number of leaves attained during the growth period

GrnYldPot Normal, Number



Repetitions: 1 Indexing: None Automatically create


Potential grain yield of the cultivar (t/ha)

Phyllo Normal, Number



Repetitions: 1

Average phyllocron interval of the variety

PMDays Normal, Number



Repetitions: 1

Number of days to reach physiological maturity

StnYldPot Normal, Number


Validation:

Potential stover yield of the cultivar (t/ha)

56


MaxTiller Normal, Number



Repetitions: 1

Maximum no of tillers per plant

MatGrp Normal, Number



Repetitions: 1

The maturity group of the variety

PltHeight Normal, Number



Repetitions: 1

The height of the plants (in cm)

DisResist Normal, Text



Repetitions: 1

Disease resistance level

DrghtResist Normal, Text



Repetitions: 1

Drought resistance level (class)

GrnColor Normal, Text



Storage:

Grain color

57


PhotoPer Normal, Text



Repetitions: 1

Sensitivity to photoperiod

LodgeResist Normal, Text



Repetitions: 1

Resistance to lodging

GrnAroma Normal, Text



Repetitions: 1

The scent characteristics of the grain

GrnSize Normal, Number



Repetitions: 1

Grain size (mm)

GrnWt Normal, Number



Repetitions: 1

The weight (g) of 1000 grains for rice and sorghum and 100 grains for maize

MillPrct Normal, Number



Repetitions: 1

Milling percentage

58

Origin_var Normal, Text



Repetitions: 1

The country where variety originated or was bred/developed

YrRel Normal, Number




Year when the variety was released to farmers

VComment Normal, Text



Repetitions: 1

Additional information about the variety

The cost for inputs used in the various experiments (the Costs table) In this table the data related to the cost of the inputs used in the experiment is recorded. However, also the farm gate price for the grain can be entered. It allows for calculating the cost adjusted yield for the experiment conducted (if prices for the inputs are provided and the farm gate price for the grain). And allows for further economic analyses if further data is provided, like the labour cost and cost for land preparation. In the latter case further information is required to come to a full costs assessment for production on farmers fields. The prices are linked to a specific experiment. That is the crop that is concerns and the year to which the prices apply is specified in the table that describes the experiment and provides the identification for the experiment. If in that particular sentinel site for that particular year experiments are conducted with different crops (i.e. they count as different experiments) separate records needs to be entered for each experiment. This will cause some duplication and redundancy, but is considered the more efficient solution at this moment. If in future the number of records will grow, one might consider a different design for the table in which each item will have its own record together with the specification of the year for which the cost specification applies. One could also consider listing the input prices in the table where the fertilizer composition is specified. In this table the currency needs to be specified in which the prices are recorded, which allows the option to specify the cost in the local currency, in which case the exchange rate to the USD needs to be specified, or in USD in which case the conversion needs to be done prior to entering the data. The definition of the fields need to be further reviewed in so far validation auto-entry etc are concerned. Since these are currently all the same for each field, they are not further specified in the table below.

59

Table 14. Cost specifications for inputs and outputs that apply to the experiment conducted in that particular year


DTexper Normal, Text





Identification code for the diagnostic trial experiment as determined by the sentinel site, year and season when the experiment is carried out and test crop used

Currency Normal, Text





The currency in which the prices/costs are specified

Exch_rate Normal, Text


Exchange rate to USD for the year and cropping season for which the prices are reported

Seed Normal, Number


Cost (per kilo) of seed of the test crop used for planting (in currency specified in the 'currency' data field)

MOP Normal, Number


Cost of 50kg bag of Murate of Potash (in currency as specified)

60

Storage:

SSP Normal, Number


Cost of 50kg bag of Single Super Phosphate (in currency as specified)

TSP Normal, Number


Cost of 50kg bag of Triple Super Phosphate (in currency as specified)

Urea Normal, Number


Cost of 50kg bag of urea (in currency as specified)

Manure Normal, Number


Cost of 50kg bag of manure (in currency as specified)

Lime Normal, Number


Cost of 50 kg bag of agricultural lime (in currency as specified)

MultiN Normal, Number


Cost of 50 kg bag of multinutrient fertilizer (or equivalent of the combination of chemical compounds used in the MN treatment, in the ratio as applied)

Herbic Normal, Number


Cost of herbicide commonly used in the sentinel site per liter (in currency as specified)

Labour Normal, Number


Labour cost for normal agronomic work (planting, weeding, harvesting etc) per day (in local currency as specified)

GrnTcrop Normal, Number


The farm gate price of grain of the test crop used in the trials at the time of harvest (per kg in currency as specified)

61

LPrep Normal, Number


The average cost of land preparation per ha

CComment Normal, Text


Calculation of the crop response variables for each experimental field and treatment (the Calc_Plot table) This table contains calculated (or derived) crop response variables to the various treatments for each experimental field that is part of the diagnostic trials conducted in a particular sentinel site, year and season. It does not contain any ‘original’ data, but draws upon data from other tables to calculate the derived variables. It is quite specific for the experiment conducted and the type of analyses that one aims to do on the data. Though specific for the diagnostic trials, it may serve as an example for other experiments on how data can be processed to make it suitable for further analyses. Rather than creating a separate table an alternative solution is to create a specific layout that automatically calculates these derived variables and where these processing steps are automated. Currently the processing steps are not fully automated. It is also possible to make use of summary fields in the tables where the original data is contained and to copy the summary field onto al layout or into a new table. For example, in the diagnostic trials the control treatment and the NPK treatment are replicated. In a few experiments the NPK treatment is even replicated twice. Using summary fields would automatically take care of these whether the treatment is replicated once or twice or even more often. A third solution would be to develop scripts that calculate the values for that particular fields and that are triggered when entering the field or opening the table. It would be possible to develop a set of scripts that can dynamically be linked to the layout to give the required output. Given the specific nature of the diagnostic trials these solutions have not been implemented, but could be considered in the future. The current solutions provided in the table below for calculating the average of the control and NPK yields are not working and the relationships between the relevant tables and the formulas used need to be reviewed. Typically, in this case, the response is determined by comparing the yield obtained for a particular treatment to the control yield. Secondly in order to normalize the data and make it independent of the particular crop and cultivar used (with different yield potentials) or make it independent of the climatic and rainfall conditions (it is differences associated with the season and the sentinel site) the data is normalized by considering the yield increase (or decline) compared to the control yield (or NPK or other reference) in relation to the control yield or in relation to the difference between attainable yield and the control yield. The strategy for analyses of yield - and crop growth data is explained in a separate document. Currently the response to the omission of nutrients of the effect of soil amendments is evaluated against the NPK yield as a reference. Table 15 Calculating the response to nutrient omission and soil amendments for the diagnostic trials

Field Name Type Options Comments In Relationships In Value

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Lists

DTID Normal, Text



Storage: Repetitions: 1 Indexing: None Automatically create indexes as

needed Index Language: English

Identifier of the particular diagnostic trial

Calc_Plot=DT_ident

Control Calculated, Number

Calculation: Context table: Calc_Plot Average ( Yld_msr::TGrnYld_adj and

Yld_msr::Plot = "03" ; Yld_msr::TGrnYld_adj and Yld_msr::Plot = "10")

Storage: Repetitions: 1 Do not store calculation results

Yield of the control treatment (mean of the 2 controls) This formula is not working because of the incorrect use of the ‘and’ operator (here is not one occurrence of a record where the Plot number is 3 and 10 at the same time). Rather consider using a summary field

NPK Calculated, Number

Calculation: Context table: Calc_Plot Average ( Yld_msr::TGrnYld_adj and

Yld_msr::Plot = "01"; Yld_msr::TGrnYld_adj and Yld_msr::Plot = "09" )


Yield of the NPK treatment (mean of the 2 NPKs) See above; consider using a summary field to calculate the average in a particular DT where the treatment description is “NPK”

PK Normal, Number

Auto-Enter: Allow editing Do not replace existing value for

Yield of the PK treatment Intended to copy the yield data

63

field (if any) Context table: Calc_Plot

Calculation: Yld_msr::TGrnYld_adj and Yld_msr::Plot = "07"



for the PK treatment from the Yld_msr table; revise – probably incorrect used of the logical operator. Consider removing this field altogether.

NK Normal, Number






Yield of the NK treatment Intended to copy the yield data for the NK treatment from the Yld_msr table; revise – probably incorrect used of the logical operator; otherwise check whether Plot is defined as a number or text value. Consider removing this field altogether

NP Normal, Number






Yield of the NP treatment Same comment as above for the NP treatment

64

NPKMN Normal, Number






Yield of the NPK+Multinutrients treatment Same comment as above: review

NPKManure

Normal, Number







Yield of the NPK+Manure treatment Same comment as above: review

NPKLime Normal, Number



Calculation: Yld_msr::TGrnYld_adj

Yield of the NPK+Lime treatment Same comment as above: review

65

and Yld_msr::Plot = "02" Validation:


Repetitions: 1 Indexing: None Automatically create indexes as


Yatt Calculated, Number

Calculation: Context table: Calc_Plot Max (Yld_msr::TGrnYld_adj)


Highest yield obtained within the specific diagnostic trial, which is considered to represent the attainable yield under the prevailing conditions

Yatt%Ypot Calculated, Number

Calculation: Context table: Calc_Plot Round(((Cultivar

Characteristics::GrnYldPot - Calc_Plot::Yatt)/Cultivar Characteristics::GrnYldPot);2)


Attainable yield as percentage of potential yield of the cultivar used

RespClass Calculated, Text

Calculation: Context table: Calc_Plot If ( DTexper_ident::Crop = "maize" ;

If(Round((Calc_Plot::NPK-Calc_Plot::Control)/(Calc_Plot::Yatt-Calc_Plot::Control);2) >.5 and (Calc_Plot::NPK - Calc_Plot::Control) > (2 * (Calc_Plot::Yatt/Cultivar Characteristics::GrnYldPot)); "Resp"

Assigns responsiveness class based on the response to NPK treatment compared to control yield using attainable yield and yield potential of the cultivar as reference (applies only to maize as test crop). It assumes the response to NPK being more than half of the difference

66

; If(Round((Calc_Plot::NPK-Calc_Plot::Control)/(Calc_Plot::Yatt-Calc_Plot::Control);2) ≤ .5 and Calc_Plot::Control > (Calc_Plot::Yatt - (4 * Calc_Plot::Yatt/Cultivar Characteristics::GrnYldPot)); "RichNonResp"; "PoorNonResp")) ; " " )

Storage: Repetitions: 1 Do not store calculation results Index Language: English

between the attainable yield and control yield and the difference between NPK and control yield being at least 2 tons per ha under favourable conditions (if attainable yield approaches the yield potential), a threshold which decreases in proportion to the ration of the attainable yield compared to yield potential to represent the less favourable conditions

Respcases Calculated, Number

Calculation: Context table: Calc_Plot Max

(Calc_Plot::NPKMN;Calc_Plot::NPKManure;Calc_Plot::NPKLime )>Calc_Plot::NPK


Gives the yield of that soil amendment treatment that gives the biggest increase in yield compared to the ‘NPK’ yield

Nom_resp Calculated, Number

Calculation: Context table: Calc_Plot Round((Calc_Plot::NPK-

Calc_Plot::PK)/Calc_Plot::NPK;2) Storage:

Repetitions: 1

Response to N-omission as ratio of the NPK yield

Pom_resp Calculated, Number

Calculation: Context table: Calc_Plot Round((Calc_Plot::NPK-

Calc_Plot::NK)/Calc_Plot::NPK;2) Storage:

Repetitions: 1

Response to the omission of P in comparison to NPK yield (as ratio)

Kom_resp Calculated Calculation: Response to the omission of K as

67

, Number Context table: Calc_Plot Round((Calc_Plot::NPK-

Calc_Plot::NP)/Calc_Plot::NPK;2) Storage:

Repetitions: 1 Do not store calculation results

fraction of the NPK yield

MNresp Calculated, Number

Calculation: Context table: Calc_Plot Round((Calc_Plot::NPKMN-

Calc_Plot::NPK)/Calc_Plot::NPK;2) Storage:


Response to the addition of multinutrients to the NPK relative to the NPK yield

Manresp Calculated, Number

Calculation: Context table: Calc_Plot Round((Calc_Plot::NPKManure -



Response to the addition of manure to the NPK application relative to the NPK yield

Limeresp Calculated, Number

Calculation: Context table: Calc_Plot Round((Calc_Plot::NPKLime -



Response to the lime application in addition to the NPK application and relative to the NPK yield

Identification of the diagnostic trials experiment (the DTexper_ident table) This table provides the characteristics of the diagnostic trials experiment. In our definition an experiment may consist of several trials (multi-locational set-up) conducted in a particular sentinel site, in a particular year and season and adopting a particular design, as defined by the test crop used, the size of the plots and the net plots being harvested, the plant density as determined by the plant spacing (though that may be part of the treatment) and the treatments that are implemented. Also the way in which particular variables are

68

measured (e.g. plant height, base area, number of ears in the subsample or number of best plants selected) is determined by the experiments and is assumed that the measurement protocols are applied consistently over the various fields that are part of the experiment. This table also specifies the code for the identification of the experiment that is subsequently used as the identifier in all related tables that provides specific information on the experiment. Rather than establishing relationships directly between the associated table all relationships are defined through the DTexper_ident table. Table 16. Identification of the experiment and specification of the experimental design


SScode Normal, Text


Validation: Only during data entry Not empty Maximum data length: 4 Error message: Enter first four

letters of the sentinel site name Storage:



Four letter code for the sentinel site (first four letters of the SSname)

Year Normal, Date


Validation: Only during data entry Strict data type: 4 digit year Not empty


Year in which the diagnostic trials are established

69

Automatically create indexes as needed

Index Language: English

Season Normal, Text


Validation: Only during data entry Value list: AB Maximum data length: 1


Cropping season in which the diagnostic trials are conducted (either "a" for the early or first season, or "b" for the late or second season)

Crop Normal, Text


Validation: Only during data entry Value list: crop list



Name of the crop used in the diagnostic trial

Variety Normal, Text





Crop variety used for the particular diagnostic trial

Cultivar Characteristics=DTexper_ident

70

Exper_nr Normal, Text

Auto-Enter

Constant data: 1

Allow editing Validation:

Only during data entry

Max data length: 1

Error message: give sequential number (<10) for each experiment carried out in this sentinel site during this season

Sequential and unique number for each experiment or study conducted during that season in

that sentinel site as defined by the test crop used and type of trial ("1" is entered by default, give sequential number (2 or more) if more than one study is conducted)

Exper_name Normal, Text

Name of the study or experiment by which the type of trials is also generally denoted and by which the experiment in known by the organization carrying out the experiment (e.g. diagnostic, ISFM, student trials or other)

DT Experiment

Normal, Text

Calculation: Context table: DTexper_ident DTexper_ident::SScode &

DTexper_ident::Year & DTexper_ident::Season & DTexper_ident::Exper_nr)




Identifies the DT experiment by sentinel site, the year and season and test crop used

Fert composition=DTexper_ident

DTexper_ident=DT_ident

DT_treatment=DTexper_ident

Project_info=DTexper-indent

Costs+DTexper_ident

InterR Normal, Auto-Enter: Inter-row spacing in centimeters

71


Always Validate Strict data type: Numeric Strict validation Range: 40- 99 Maximum data length: 2


(if varying plant densities are part of the treatment leave blank and specify in the description of the treatment)

IntraR Normal, Number


Validation: Always Validate Strict data type: Numeric Strict validation Range: 5 - 60 Maximum data length: 2


Intra-row spacing in centimeters (if varying plant densities are part of the treatment, leave this field blank but specify under the treatment description)

Rowno_p Normal, Number




Number of planting rows per plot (this determines together with InterR the length of one side of the plot)

Stnno_p Normal, Number



Storage:

Number of planting stations per row per plot (this determines together with IntraR the length of the other side of the plot)

72


Rowno_np Normal, Number




Number of planting rows in the net plot (determines the size of the net plot – one side of it)

Stnno_np Normal, Number




Number of planting stations in the net plot (determines the size of the net plot – the second side)

Pltno_bp Normal, Number




Number of best plants selected (in case many fields have been damaged a limited number of best plants is harvested systematically for all plots for comparative analyses)

Plntno_SS Normal, Number




Number of plants taken for determination of stover dry weight - fresh weight ratio (if this was not done systematically leave open)

Cobno_SS Normal, Number


Validation:

Number of cobs (or heads) in the sub-sample used for determining dry weight - fresh weight ratio

73



BArea_msr Normal, Text


Validation: Only during data entry Value list: BArea_meth


Method of measuring the basal area of the plant stem (from diameter only, from diameter and width measurement, from measurement of basal circumference, or different types of measurement used)

Composition of the fertilizer used in the experiment (the Fert_composition table) Provides opportunity to specify the composition of the fertilizers used in the experiment. The fertilizer used in the different experiments may be sourced from different providers, even though it may relate to experiments conducted in the same sentinel site. Even if sourced from the same provider, it may be a different brand and have a slightly different composition. This is certainly true for the manure, the quality of which needs to be established for each experiment it is used for. They way the table is designed it requires separate records for each fertilizer used in each experiment. If a standard fertilizer is used (like Urea) with the standard composition, the records simply needs to be duplicated for each experiment conducted in that year for which the fertilizer is used and again for each year when an experiment is conducted and each sentinel site. It may create some redundancy in the data set especially for fertilizers like Urea or TSP, but it would complicate the design of the database further if a more efficient solution should be implemented (it would require an additional table to provide only for the link between the experiment and the specific fertilizer used). However, such solution should be considered when at a certain moment the size of the database would be come an issue. It should still be checked whether the relationships as currently defined allows for the automatic calculation of the nutrient application rates for the various treatments considering the amount of fertilizer applied (from the Treatment table) and the fertilizer composition as defined in this table. It would probably require the use of summary fields in one way or the other. The composition in P is given in P2O5 and for K in K2O, which would require a conversion to obtain the nutrient application rates in P and K. Table 17. Composition of the fertilizers used in the various experiments


DT Experiment Normal, Auto-Enter: Identifies the DT experiment Fert

74

Text Allow editing Do not replace existing value for

field (if any) Validation:




conducted by sentinel site, the year and season and test crop used. This value cannot be copied automatically, but data entry can be controlled by drop down list or other

composition=DTexper_ident

Fertilizer Normal, Text




Review storage options (for all fields in this table)

Name of the fertilizer used in this experiment (should be the same for all treatment where that type of fertilizer is used) treatment (include "manure", "lime”, chemical compound or other). This can be a generic name (e.g. Urea or Manure) as the primary key is constituted by the experiment identifier and the fertilizer name

N_comp Normal, Number


Validation: Only during data entry Maximum data length: 2


N composition (weight % of N) in fertilizer

75


P2O5_comp Normal, Number




Weight percentage of P2O5 in fertilizer (multiply P by 2.3 to get P2O5)

K2O_comp Normal, Number




Weight percentage of K2O in fertilizer (multiply K by 1.2 to get K2O, if value given in K)

Ca_comp Normal, Number




Composition of CA in the fertilizer

Mg_comp Normal, Number




Weight percentage of Mg in the fertilizer, agricultural lime of manure applied

S_comp Normal, Number


Validation:

Weight percentage of S in fertilizer

76



Zn_comp Normal, Number




Concentration of Zn (weight %) in fertilizer, manure or other

B_comp Normal, Number




Concentration of B in weight percentage in fertilizer, manrue or chemical compound applied

Mn_comp Normal, Number




Concentration of Mn (weight %) in fertilizer, manure or chemical compound applied

Mo_comp Normal, Number




Concentration of Mo (weight %) in fertilizer, manure or other applied

Cu Normal, Auto-Enter: Concentration of Cu (weight %)

77




in fertilizer, manure or other applied

Ash_comp Normal, Number




Amount of ash in the manure applied

Moisture_cont Normal, Number


Validation: Only during data entry Strict data type: Numeric Range: 0 - 99


Moisture content in the manure or other organic resource applied

Description of the treatments in terms of fertilizer type and amounts applied (the Treatment table) This defines the treatment in terms of the type of fertilizer and amount of fertilizer used for the particular treatment. Entering data in this table is optional. When and if the nutrient application rates are filled in the Nutrient appl table this information is not required. However, in case of manure, for example, the amount applied is generally known and the composition of the manure may be known. This data is then entered in this table and the Fert_composition table. The amount of nutrients applied can then be calculated automatically. This in principle applies to any fertilizers used and therefore the entry of this type of data is facilitated for sake of consitency. Table 18. Specification of the treatments in terms of fertilzer type and amounts used


78

DT_exper Normal, Text





Identifies the DT experiment conducted by sentinel site, the year and season and test crop used

Treatment∞DT_design

Nutrient appl∞Teatment

Trt_code Normal, Text



Storage: Repetitions: 1 Indexing: All Minimal

Unique ID (number as text) that identifies each individual treatment as defined by the type and amount of fertilizers used. E.g. if the treatment the treatment consist of the application of N, P and K but in different amounts it should be defined as different treatments with its own Trt_code

Treatment=DT_design

Trt_description Normal, Text





Code or abbreviation to describe the treatment (generally denoting the nutrients or substance added, like ‘NPK’ or ‘N100P30K60’) It is advisable to give a specific name to every specific treatment

Nutrient appl∞Treatment

79

Fertilizer Normal, Text




Name of the fertilizer (generic name) used in this treatment (include "manure", "lime'", name of the chemical compound or other)

Fert_application Normal, Text


Validation: Only during data entry Strict data type: Numeric Range: 0 - 20000 Error message: A maximum of

20000 kg/ha is set, based on the maximum allowed rate for manure application. The application rates for the other fertilizer types is not effectively validated in this manner



Application rate of fertilizer in kg/ha

Partners associated with the experiment and project (the Partner table) This table serves to specify the partner institution who has been responsible for the implementation of the trials in the sentinel site. It provides for the entry of the details of the contact person and other specifics. It is linked to the Project info table where the partners are listed in relation to the experiments. This could be elaborated further as the contact database and it specifies to some extend the data source

80

Table 19. Partner details and contact information


Partner_inst Normal, Text





Partner institute responsible for implementing the trials (diagnostic and other)

Partner=Partner_SS

Contact Normal, Text




Contact person responsible for implementation of the trials

Email Normal, Text




Eamil address of the contact person in partner institue

Mob Normal, Text



Storage:

Mobil telephone number of contact person

81


Address Normal, Text




Physical address of office where contact person is located

Project information (the Project_info table) This table is include to provide details on the project through which the trials have been implemented and provides a link where other relevant information can be found (for example technical specification or other). Currently the ‘project’ is linked to the experiment and through the experiment to the sentinel site. This section of the database, i.e. the tables related to the project and to the partner institutions needs to be further elaborated. It is included here in a rudimentary form Table 20. Project information


DT_exper Normal, Text





Identification for the DT experiment

Project_info=DTexper_ident

Inst_impl Normal, Text

The name of the institution responsible for the implementation of the project

Partner=Project_info

82

Partner_inst Normal, text

Name of the partner organization or institute responsible for the implementation of the diagnostic or other trials in the sentinel site concerned. This generally refers to the national or local organization


Project Normal, Text





Name or acronym of the project that has implemented the diagnostic trials.

Land use and management history of the field where the trials is conducted (the Field_hist table) The land use history and how the field has been managed in the past may have a profound impact on the soil fertility status and it is therefore important to capture that data in order to evaluate the results from the agronomic trials. In this table the data from the 5 years immediate prior to the establishment of the diagnostic trials are considered and refers to the more general information. That is if data is gathered for each of those five years specifically it needs to be translated into the data categories that are applied for these data fields. This table requires needs to be further elaborated and documents in terms of the value list used for validation and other. Ths particular table is linked to the FieldID rather then to the experiment Table 21. Land use and management history of the concerning experimental field


FieldID Normal, Number


Code to identify the field (SScode & Cluster & Field

Field_hist=Field_ident

83



number)

Year_ref Normal, Number




Year to which the information of the land use history should be referenced (i.e. year in which the data was collected)

YrsCult Normal, Number





Land use history (i.e. years since conversion to cultivation)

FalTyp Normal, Text


Validation: Only during data entry Value list: Fallow Type


Type of fallow practiced by the farmer in the last 5 year period: permanent cultivation (no fallowing), improved fallow or natural fallow

FalPeriod Normal, Text


Validation:

Fallow period in the last 5 years: 'short' when 1 - 2 cropping seasons out of the

84

Only during data entry Value list: Fallow period


6 are fallowed, 'medium' when 2-3 out the 6 cropping seasons are fallowed and long if it is 3 seasons or more.

CropSys Normal, Text


Validation: Only during data entry Value list: Cropping system


Type of cropping system used for the last 5 years in this field: tree crop, banana or other perrenial herbaceous, cereal, root&tuber, pulses&vegetable, fodder&fiber, yes or no intercropped or in crop rotation

Crop_prev Normal, Text


Validation: Only during data entry Value list: crop list


The crop used in the season immediately preceding the establishment of the diagnostic trial

TilIage Normal, Text


Validation: Only during data entry Value list: Tillage


Type of tillage generally done or the most “heavy” type of tillage applied by the farmer in this field in the last 5 years

Fert_appl Normal, Text


Was fertilizer applied in last 5 years (seldom when once

85

Validation: Always Validate Value list: Frequency (needs

reviewing) Storage:


in the 5 years or less, regular when 2 - 3 times in the last 5 years and frequent when 4 or 5 times in the 5 years [double the numbers when two cropping seasons a year])

Fert_type Normal, Text




Type of fertilizer or combination of fertilizer types used [manures only, chemical fertilizers only, organic and inorganic fertilizers combined]

FertMet Normal, Number


Validation: Only during data entry Value list: Fert_appl_meth


The method used in fertilizer application (e.g. broadcasting, strip, or spot application by hand or using any kind of meausring device like bottle top - also applies to manures whether applied in pockets, strip or broadcast)

Fertchem_type Normal, Text


Validation: Only during data entry Value list: Chem fert clss


as needed

Type of chemical fertilizers used: 'straight' (only one type of straight fertilizer), 'mixture incomplete' (applying combined elements through use of different (straight) fertilizers or incomplete mixture fertilizer (like DAP), 'mixture complete' when one or

86

Index Language: English several fertilizers are used to provide NPK and other plant nutrients)

FertAmt Normal, Number


Validation: Only during data entry Value list: Fertilizer amounts



Average amount of inorganic fertilizer applied per season, i.e. averaged total of the various types of fertilizers is used; This is given as either low (<20 kg fertilizer/ha), medium (20-60 kg fertilizer/ha), high (60-120 kg/ha) and very high (>120 kg fertilizer/ha)

ManType Normal, Text


Validation: Only during data entry Value list: Manure types



Type of manure generally used during the last 5 seasons) by the farmer in the experimental field

Man_amt Normal, Number



(consider defining classes for amount of manure used) Storage:

Average amount of manure (kg/ha) applied per season during the last 5 seasons by approximation (in fresh weight)

87


CropRes_mngt Normal, Text




Crop residue management as done by the farmer in previous seasons (ploughed back, burned or surface applied)

Fert_s1l Normal, Text




Fertilizer types in season 1 (immediate last season). Type in all the fertilizers used, separating them with a semi colon (;). (Check how repetitions work in Filemaker Pro and what is used for separation of the values)

Yield_frmfld Normal, Number




Average seasonal yield (kg/ha) of the test crop observed by the farmer during the 3 last immediate seasons

Data on the management of the experimental field (the DT_mngt table) This table is used to store all relevant information on the management of the (diagnostic) trial. This refers to information about how and when the field was prepared for planting, the various dates for weeding, gapping, thinning, fertilizer application etc. Also observations on the flowering date and date of germination are recorded in this table. Further the dates when ear leaf (or flag leaf) samples are taken are

88

kept in the table. Some of this data is very important to evaluate and explain possible abnormalities observed in the plant growth and yield data. Table 22. Data on the management of the experimental field


DTID Normal, Text

Auto-Enter: Allow editing Lookup: <Missing

Field>, Do not copy, Don't copy contents if empty

<Needs reviewing> Validation:


Repetitions: 1 Indexing: None Automatically create

indexes as needed Index Language:

English

Identification for the diagnostic trial by SScode & Year & Season & Cluster and Field number This data is automatically generated form data entered in another table. It can however not be copied. Solution could be found when a specific layout is created for entering data in the various tables simultaneously. Otherwise mechanisms need to be implemented that if entered manually that the correct DTID are being entered and that proper links are established to the related tables.

DT_mngt=DT_ident

LPMethod Normal, Text




Method of land preparation to establish diagnostic trials

LPDate Normal, Date



Date of land preparation to establish diagnostic trials

89


MADate Normal, Date




Date of manure application in respective treatment of the diagnostic trial

PKAppln Normal, Date




Date when P and K fertilizers are applied

FNAppln Normal, Date




First N split application date (i.e., date of first top dressing)

SNAppln Normal, Date




Second N split application Date (i.e., date of second top dressing)

PlntDa Normal, Auto-Enter: Planting date

90

Date Allow editing Validation:



HarvDa Normal, Date




Harvest date for determination of crop yield

GappDa Normal, Date




Gapping Date

ThinDa Normal, Date




Thinning date

WdgFDa Normal, Date



Storage:

Date of first weeding

91


WdgSDa Normal, Date




Date of second weeding

WdgTDa Normal, Date




Date of third weeding

PMethod Normal, Text





English

Method of planting (flat, ridges) used in the diagnostic trial field

TPlntDa Normal, Date




Transplanting date (for rice sowed in nursery)

92

Indexing: None Automatically create


English

EmDate Normal, Date




50% emergence date. Date on which 50% of the plants have emerged from the soil

FDate Normal, Date




75% flowering date. Date on which at least one flower has opened on 75% of the plants

EarDate Normal, Date




Date of ear leaf sampling

Comment Normal, Text




Any additional information to explain condition of field or activity detail

93

Design of the diagnostic trials experiment (the DT_design table) This table specifically serves to assign a particular treatment to the various plots in the experimental field. The name of the table (DT_design) is as such somewhat misleading. Most of the information on the design of the experiment is contained in the DTexper_ident table where it actually belongs. Much of the design parameters are namely not dependent on the particular plot. It should therefore be considered to change the name of the DTexper_ident table as well. It is generally advised to use names that have a connotation that reflects the contents of that particular table. The Identifcation for the experiment together with the plot number constitute the primary key for this table (i.e. the match fields) Table 23. Linking the plot number to the treatment for any particular experiment and specification of plant densities if this varies per plot.


DT Experiment

Normal, Text

Auto-Enter: Allow editing Do not replace existing value

for field (if any) Context table: DT_design

Calculation: /*SScode & Year & Season & Left ( Crop ; 1 )*/

<copying of the data from the DTexper_ident table will not work and other means should be put in place for entering the data such that the referential integrity is maintained; consider using a layout for entering data simultaneously for corresponding fields in the different tables>in the

Validation:

Identifies the DT experiment conducted by sentinel site, the year and season and test crop used The identifier for the experiment is specified in the DTexper_ident table and the values should be consistent as this is a match field

DT_design=DTexper_ident Treatment∞DT_design

94


Repetitions: 1 Indexing: Minimal Automatically create indexes


Plotnr Normal, Text


Validation: Only during data entry Not empty Range: 1 - 99 Maximum data length: 2 Error message: Plot number

should be in text format and be of two characters ranging from "01" to "99"

<redesign field to make type ‘number’> Storage:

Repetitions: 1 Indexing: Minimal Automatically create indexes


Number of the plot in the diagnostic trials (in a DTexperiment the layout of the field is standard and treatments are assigned to a specific plot number)

PlotID

Treatment code

Normal, Text



Storage: Repetitions: 1 Indexing: Minimal

Two digit number as code for the treatment (see treatment table)

Treatment∞DT_design

95

Automatically create indexes as needed

Index Language: English

Rep Normal, Number


Validation: Only during data entry Value list: YesNo



Indicates whether the treatment is replicated within the diagnostic trial ("yes" when replicated and "no" when not replicated)

InterR Normal, Number


Validation: Always Validate Strict data type: Numeric Strict validation Range: 70 - 90 Maximum data length: 2

<consider changing the range of allowed values for the field> Storage:



Inter-row spacing in centimeters This field is intended to be used only if experiments are conducted that involve different plant densities; consider changing the name of this field to avoid confusion with the inter row spacing in the DTexper_ident table.

IntraR Normal, Number


Validation:

Intra-row spacing in centimeters This field is intended to be used

96

Always Validate Strict data type: Numeric Strict validation Range: 20 - 55 Maximum data length: 2

<change the range of allowed data values to be consistent with the range specified for this data field in the DTexper_indent table> Storage:



only if experiments are conducted that involve different plant densities; consider changing the name of this field to avoid confusion with the intra row spacing in the DTexper_ident table.

InputC Calculated, Number

Calculation: Context table: DT_design /*If ( Trt = 1; 55102; If ( Trt = 2;

111102;If ( Trt = 3; 85899;If ( Trt = 4; 95837;If ( Trt = 5; 95571;If ( Trt = 6; 103297;If ( Trt = 7; 141967;115167 )))))))*/

<Check formula and relationships to make sure the right context table is referenced to and can be accessed> Storage:

Repetitions: 1 Do not store calculation

results Index Language: English

Total cost of inputs

97

Identification for the individual diagnostic trial (experimental field) (the DT_ident table) This table serves to provide an unique identifier for each experimental field (in case of the diagnostic trials for each individual diagnostic trial) that is part of an experiment conducted in a particular sentinel site and in a particular year and season. It references to the Field_ident table for the cluster number and the field number and it reference to the DTexper_ident table for the specific experiment, i.e the year, season and crop used in the experiment. The DTID field references to tables where specific information related to that particular experimental field is kept, like the table that contains information on he management of the field, or the table where the results for the experiment in that that particular field are stored, like the crop response to the various treatments. Table 24. Generating the unique code for identification of the particular experimental field

Field Name

Type Options On Layouts Comments In Relationships In Value Lists

FieldID Normal, Text





Yld_msr DT_ident

The field identifier should match with the field identifier of the Field_ident table

Field_ident=DT_ident

DTexper Normal, Text




Yld_msr DT_ident

DTexper_ident=DT_ident

98


DTID Normal, Text

Auto-Enter: Allow editing Do not replace existing

value for field (if any) Context table: DT_ident

Calculation:

Field_ident::SScode

& DTexper_ident::Year

&

DTexper_ident::Seas

on & Field_ident::Cluster

& Field_ident::Field Validation:


Repetitions: 1 Indexing: All Index Language: English

DT_ident Unique code for each individual diagnostic trial

DT_mngt=DT_ident Calc_Plot=DT_ident Plot_ident=DT_ident

Unique identifier for the plot as part of an experimental field (the Plot_ident table) This table serves to generate a unique identifier for each individual plot. The PlotID references to the tables where the yield observation, the crop growth characteristics and the pest and diseases are recorded and serves as the primary key and match field for the records in these tables. Table 25. Generating a unique identifier for the plot in the experimental field

Field Name

Type Options Comments On Layouts In Relationships In Value Lists

DTID Normal, Auto-Enter: Identification of the Plot_ident Plot_ident=DT_ident

99

Text Allow editing Validation:


Repetitions: 1 Indexing: All Index Language: English

particular diagnostic trial (experimental field) associated with one particular field, experiment and one particular cropping season

Plotnr Normal, Text


Validation: Only during data entry Range: 1-10



Number of the plot in the particular diagnostic trial

Yld_msr Plot_ident

PlotID Calculated, Number

Calculation: Context table: Plot_ident

Calculation: Plot_ident::DTID & If ( Plot_ident::Plotnr < 10 ; "0" & Plot_ident::Plotnr ; Plot_ident::Plotnr )



Unique identification for the plot, i.e. as part of a particular diagnostic experiment with reference to the field and the year and season when this particular trial was implemented

Yld_msr=Plot_ident Pest&Diseases=Plot_id

ent Crop_grwth=Plot_iden

t

100

Backing up (the Backingup table) This table was created solely for the purpose of backing up. It contains a field “Count Field” that is a counter that is incremented with one every time the database file is closed. The field is part of a script that creates a copy of the database upon closing the database when this is done for the fifth time after the last backup was made. However the script does not execute because the talbe is not linked to any other table in the database. This should be looked into such that the backing up system works properly. Currently the backup is stored on the computer, but it would provide better security if the backup was stored on the server of the local area network.

Relationships Table occurrences In the table below the table occurrences are specified; that is occurring as part of a relationship, in the definition of fields (if defined by calculation), in value list and the layout the table is associated with. Currently only these are all internal relationships, meaning that only the current database file (Diagnostic Trials_develop.fp7) is considered as source file. In immediate future it should however be considered to include other source files as well, as we want to establish links with the databases that contain the spectra of the soil and plant samples and the databases that contain the results for the wet chemistry analyses of the plant and soils samples as well as with the database in which the derived soil and plant characteristics are described, in order to be able to retrieve data from these databases simultaneously to allow for integrated analyses. Table 26. Table occurrences Table Occurrence

Source Table

Source File

In Relationships In Field Definitions In Value Lists Associated with layouts

Plot_ident Plot_ident Diagnostic Trials_develop.fp7

Plot_ident=DT_ident Yld_msr=Plot_ident Pest&Diseases=Plot_ident

Crop_grwth=Plot_ident

Plot_ident::PlotID Yld_msr Plot_ident

Treatment Treatment Diagnostic Trials_develop.fp7

Treatment∞DT_design Nutrient appl∞Treatment

Nutrient appl::Trt_code

Nutrient appl::Trt_description

Treatment select_desc

Treatment code_by desc

Treatment

DT_design DT_design Diagnostic Trials_develop.fp7

DT_design=DTexper_ident Treatment∞DT_design

PlotID Yld_msr DT_design

Fert composition

Fert composition

Diagnostic Trials_develop.fp7

Fert composition= DTexper_ident

Fert composition

101

Yld_msr Yld_msr Diagnostic Trials_develop.fp7

Yld_msr=Plot_ident Yld_msr::TGrnYld Calc_Plot::Yatt

Sentinel site Field_loctop Yld_msr

Nutrient appl Nutrient appl


Nutrient appl∞Treatment Nutrient appl

DT_mngt DT_mngt Diagnostic Trials_dev

elop.fp7

DT_mngt=DT_ident DT_mngt

SentinelSite SentinelSite Diagnostic Trials_develop.fp7

SentinelSite=Field_ident SentinelSite::SScode

Yld_msr::TGrnYld Field_ident::SScode

SentinelsiteID

SScode

Sentinel site

Partner Partner Diagnostic Trials_develop.fp7

Partner=Project_info Partner

Project_info Project_info Diagnostic

Trials_develop.fp7


Project_info= DTexper_ident

Project_info

Cultivar Characteristics



Cultivar Characteristics= DTexper_ident

Calc_Plot::Yatt%Ypot

Calc_Plot::RespClass

DTexper_ident::Variety

Variety Cultivar Characteristics

Costs Costs Diagnostic Trials_dev

elop.fp7

Costs=DTexper_ident Costs

DTexper_ident

DTexper_ident


Fert composition= DTexper_ident

DTexper_ident=DT_ident Cultivar Characteristics=

DTexper_ident DT_design=DTexper_ident Project_info=

DTexper_ident Costs=DTexper_ident

Calc_Plot::RespClass

DTexper_ident::Exper_ID

DT_ident::DTID

Experiment identifier

Experiment identification

Yld_msr DTexper_ident

Field_loctop Field_loctop Diagnostic Trials_dev

Field_loctop=Field_ident Field_loctop::Dist_home_cls

Sentinel site Field_loctop

102

elop.fp7 FieldManagement

Field_hist Field_hist Diagnostic Trials_develop.fp7

Field_hist=Field_ident Field_hist

Rainfall_temp Rainfall_temp



Rainfall_temp

WeatherStations

WeatherStations



WeatherStations= Field_ident

Sentinel site WeatherStations

Pest&Diseases

CropStressFactors


Pest&Diseases=Plot_ident Pest&Diseases

Crop_grwth Crop_grwth Diagnostic Trials_develop.fp7

Crop_grwth=Plot_ident Crop_grwth::Plt_hght

Crop_grwth::Plt_bar

ea Crop_grwth::Plt_lfn

o_avr Crop_grwth::Plt_till

no_avr

Crop_grwth::Plt_panlgth_avr

Crop_grwth

DT_ident DT_ident Diagnostic Trials_develop.fp7

DTexper_ident=DT_ident Field_ident=DT_ident DT_mngt=DT_ident Calc_Plot=DT_ident Plot_ident=DT_ident

Yld_msr DT_ident

Calc_Plot Calc_Plot Diagnostic Trials_develop.fp7

Calc_Plot=DT_ident Calc_Plot::Yatt%Ypot

Calc_Plot::RespClas

s Calc_Plot::Respcase

s Calc_Plot::Nom_res

p Calc_Plot::Pom_res

p

Calc_Plot

103

Calc_Plot::Kom_resp

Calc_Plot::MNresp

Calc_Plot::Manresp Calc_Plot::Limeresp

Field_ident Field_ident Diagnostic Trials_develop.fp7

SentinelSite=Field_ident Field_loctop=Field_ident Field_hist=Field_ident Field_ident=DT_ident WeatherStations=

Field_ident

Field_ident::FieldID DT_mngt::DTID DT_ident::DTID

FieldID Field_ident Yld_msr

Backingup Backingup Diagnostic Trials_develop.fp7

Backingup

Relationship details In this section each individual relationship is described; one table for each relationship. Currently, for most of the relationships the ‘allow creation of records via this relationship’ and the ‘Delete related records in this table when a record is deleted in the other table’ as well as ‘Sort records’ is set to “Off”. This should be reviewed in view of maintaining the consistency and integrity of the database. For example, it should be investigated whether it is possible to create new records in the destination table each time a PlotID is generated and copy the value for the PlotID in the related field even if this is a match field. For as yet the consequences of the settings are not fully understood.

Relationship: Rainfall_temp∞WeatherStations Table Occurrence Rainfall_temp WeatherStations

Field <Missing Field> = SScode

and WSName = WSName

Source Table Rainfall_temp WeatherStations

Source File Diagnostic Trials_develop.fp7 Diagnostic Trials_develop.fp7

Allow creation of records via this relationship On On

Delete related records in this table when a record is deleted in the other table

Off Off

Sort records Off Off

Relationship: Fert composition=DTexper_ident Table Occurrence Fert composition DTexper_ident

Field DT Experiment = Exper_ID

104

Source Table Fert composition DTexper_ident


Allow creation of records via this relationship Off Off


Off Off


Relationship: SentinelSite=Field_ident Table Occurrence SentinelSite Field_ident

Field SScode = SScode

Source Table SentinelSite Field_ident




Off Off


Relationship: Partner=Project_info Table Occurrence Partner Project_info

Field Partner_inst = Partner_inst

Source Table Partner Project_info




Off Off


Relationship: Field_loctop=Field_ident Table Occurrence Field_loctop Field_ident

Field FieldID = FieldID

Source Table Field_loctop Field_ident




Off Off


Relationship: Field_hist=Field_ident

105

Table Occurrence Field_hist Field_ident


Source Table Field_hist Field_ident




Off Off


Relationship: DTexper_ident=DT_ident Table Occurrence DTexper_ident DT_ident

Field Exper_ID = DTexper

Source Table DTexper_ident DT_ident




Off Off


Relationship: Field_ident=DT_ident Table Occurrence Field_ident DT_ident


Source Table Field_ident DT_ident



Delete related records in this table when a record

is deleted in the other table

Off Off


Relationship: DT_mngt=DT_ident Table Occurrence DT_mngt DT_ident

Field DTID = DTID

Source Table DT_mngt DT_ident




Off Off


106

Relationship: WeatherStations=Field_ident Table Occurrence WeatherStations Field_ident

Field SScode = SScode

Source Table WeatherStations Field_ident




Off Off


Relationship: Cultivar Characteristics=DTexper_ident Table Occurrence Cultivar Characteristics DTexper_ident

Field Variety = Variety

Source Table Cultivar Characteristics DTexper_ident




Off Off


Relationship: DT_design=DTexper_ident Table Occurrence DT_design DTexper_ident

Field DT Experiment = Exper_ID

Source Table DT_design DTexper_ident




Off Off


Relationship: Treatment∞DT_design Table Occurrence Treatment DT_design

Field Trt_code = Treatment code

and DT_exper = DT Experiment

Source Table Treatment DT_design



Delete related records in this table when a record Off Off

107

is deleted in the other table


Relationship: Calc_Plot=DT_ident Table Occurrence Calc_Plot DT_ident

Field DTID = DTID

Source Table Calc_Plot DT_ident




Off Off


Relationship: Plot_ident=DT_ident Table Occurrence Plot_ident DT_ident

Field DTID = DTID

Source Table Plot_ident DT_ident




Off Off


Relationship: Yld_msr=Plot_ident Table Occurrence Yld_msr Plot_ident

Field PlotID = PlotID

Source Table Yld_msr Plot_ident


Allow creation of records via this relationship On Off


On Off


Relationship: Pest&Diseases=Plot_ident Table Occurrence Pest&Diseases Plot_ident


Source Table CropStressFactors Plot_ident



108


Off Off


Relationship: Crop_grwth=Plot_ident Table Occurrence Crop_grwth Plot_ident


Source Table Crop_grwth Plot_ident




Off Off


Relationship: Project_info=DTexper_ident Table Occurrence Project_info DTexper_ident

Field DT_exper = Exper_ID

Source Table Project_info DTexper_ident




Off Off


Relationship: Costs=DTexper_ident Table Occurrence Costs DTexper_ident

Field DTexper = Exper_ID

Source Table Costs DTexper_ident




Off Off


Relationship: Nutrient appl∞Treatment Table Occurrence Nutrient appl Treatment

Field Trt_description = Trt_description

and DT Experiment = DT_exper

Source Table Nutrient appl Treatment

109




Off Off


Layouts All layouts need reviewing. In many layouts the labels do actually not reflect any more the fields that are contained in the associated tables. Also the use of buttons to go back to the previous layout or the next layout or a specific layout should be reviewed. These assume manual entry of the data, which might not be applicable in all cases. As mentioned earlier with the current structure of the data base specific layout should be designed for data entry, that allows importing data from a particular file where records are entered in the different tables simultaneously , to prevent repeated importing especially of match fields in the various tables (e.g. the PlotID and the FieldID for example) and as such decrease the chance of errors being made. Likewise layouts should be created that facilitate the exporting of data in a format that suits the intended analyses. In the table below the existing layouts are listed. Subsequently details of the layouts are provided, but only for those layout where some specific work has been done rather than just standard listing of the fields.

Layout Name Include

In Menu

Quick Find

# of Objects Show Records From

Save record changes automatically

Show field frames when record is active

Custom Menu Set

Field_ident Yes Yes 4 Regular Fields Field_ident On On [File Default]

Sentinel site Yes Yes 16 Regular Fields 2 Buttons 1 Portals 1 Web Viewer Controls 1 Charts

SentinelSite On On [File Default]

Field_loctop Yes Yes 14 Regular Fields 2 Buttons 1 Charts

Field_loctop On On [File Default]

Yld_msr Yes Yes 25 Regular Fields

3 Buttons

Yld_msr On On [File Default]

Crop_grwth Yes Yes 8 Regular Fields 2 Buttons

Crop_grwth On On [File Default]

110

Rainfall_temp Yes Yes 5 Regular Fields 1 Buttons

Rainfall_temp On On [File Default]

FieldManagement Yes Yes 2 Regular Fields 3 Buttons 10 Graphic Objects

Field_loctop On On [File Default]

Pest&Diseases Yes Yes 15 Regular Fields 1 Buttons

Pest&Diseases On On [File Default]

WeatherStations Yes Yes 4 Regular Fields WeatherStations On On [File Default]

Nutrient appl Yes Yes 15 Regular Fields Nutrient appl On On [File Default]


Yes Yes 26 Regular Fields Cultivar Characteristics

On On [File Default]

Costs Yes Yes 14 Regular Fields Costs On On [File Default]

Calc_Plot Yes Yes 18 Regular Fields Calc_Plot On On [File Default]

DTexper_ident Yes Yes 18 Regular Fields DTexper_ident On On [File Default]

Treatment Yes Yes 5 Regular Fields Treatment On On [File Default]

Fert composition Yes Yes 15 Regular Fields Fert composition On On [File Default]

Partner Yes Yes 5 Regular Fields Partner On On [File Default]

Project_info Yes Yes 1 Regular Fields Project_info On On [File Default]

Field_hist Yes Yes 3 Regular Fields Field_hist On On [File Default]

DT_mngt Yes Yes 20 Regular Fields DT_mngt On On [File Default]

DT_design Yes Yes 5 Regular Fields DT_design On On [File Default]

DT_ident Yes Yes 3 Regular Fields DT_ident On On [File Default]

DT_yld_parder Yes Yes 4 Regular Fields <Missing Table Occurrence>

On On [File Default]

Plot_ident Yes Yes 3 Regular Fields Plot_ident On On [File Default]

Backingup Yes Yes 1 Regular Fields Backingup On On [File Default]

Layout Objects: Sentinel site Regular Fields Field Name: SentinelSite::Country

Field Properties Coordinates Field Format Field Behavior Quick Find

Layout Name: Sentinel site

Top: 142 px Left: 119 px

Bottom: 159 px Right: 221 px Anchoring: Left, Top

Field Format: Drop-down List

Display values from: Country

Field Behavior: Allow field to be entered: In Find

mode, In Browse mode Go to next field using: Tab key

Yes

111

Field Name: SentinelSite::RnDist



Top: 263 px Left: 188 px Bottom: 280 px

Right: 291 px Anchoring: Left, Top

Field Format: Drop-down List Display values from: Rainfall

Distribution Include arrow to show and

hide list


mode, In Browse mode

Go to next field using: Tab key

Yes

Field Name: SentinelSite::S2Rain



Top: 331 px Left: 188 px Bottom: 347 px Right: 267 px Anchoring: Left, Top

Field Format: Edit Box



Yes

Field Name: SentinelSite::Tmax








Yes

Field Name: SentinelSite::Tmin







Yes

112

Field Name: SentinelSite::Windspd









Yes

Field Name: SentinelSite::MAR







Yes

Field Name: SentinelSite::S1Rain









Yes

Field Name: SentinelSite::SSname







Yes

113

Field Name: SentinelSite::SSXCoord







Yes

Field Name: SentinelSite::Tmean








Yes

Field Name: SentinelSite::SSYCoord








Yes

Field Name: WeatherStations::WSName


Layout Name: Sentinel site Visibility:

Not visible in Table View







Yes

Field Name: WeatherStations::WSXCoord

Field Properties Coordinates Field Format Field Behavior Quick

Find

Layout Name: Top: 464 px Field Format: Field Behavior: Yes

114

Sentinel site Visibility: Not visible in Table View

Left: 179 px Bottom: 480 px Right: 245 px

Anchoring: Left, Top

Edit Box Allow field to be entered: In Find mode, In Browse mode


Field Name: WeatherStations::WSYCoord


Layout Name: Sentinel site Visibility: Not visible in Table View

Top: 464 px Left: 261 px Bottom: 480 px Right: 339 px





Yes

Field Name: SentinelSite::SScode


Find

Layout Name: Sentinel site Visibility: Only visible in Table View






Yes

Buttons Button Properties Coordinates Script/Script Step

Type: Text: Go to weather layout Layout Name: Sentinel site

Additional Properties: Change to hand cursor over button Rounded



Go to Layout [ “Rainfall_temp” (Rainfall_temp) ] Layouts: Rainfall_temp

Button Properties Coordinates Script/Script Step

Type: Text:


Go to Layout [ “Field_loctop” (Field_loctop) ] Layouts:

115

Layout Name: Sentinel site Additional Properties:

Change to hand cursor over button Rectangular


Field_loctop

Portals Portal Properties Coordinates Fields Options

Table: WeatherStations Layout Name:

Sentinel site



WeatherStations::WSName WeatherStations::WSXCoord WeatherStations::WSYCoord

Sort records: Off Filter calculation: None Initial Row: 1

Number of Rows: 9

Web Viewer Controls Web Viewer Control Properties Coordinates Web Address

Layout Name: Sentinel site Additional Properties: Allow interaction with web viewer content

Display progress bar Display status messages



Charts Chart Properties Coordinates Axis Info

Layout Name: Sentinel site Additional Properties:

Type: Bar Title: "Grain yield (t/ha) of the control treatment" Include Legend: Yes Use Data From: Related Records Related Table: Field_loctop

Sort records: OnField: <Missing Field> Ascending order Reorder based on summary field: Off



X Axis Title: "Cluster" Series:

Value: Field_loctop::<Field Missing> Y Axis Title: Series 1: Value: Yld_msr::TGrnYld

Title:

116

Override field's language for sort: Off

Layout Objects: Field_loctop Regular Fields Field Name: Field_loctop::Village


Layout Name: Field_loctop





Yes

Field Name: Field_loctop::Frmr_name







Yes

Field Name: Field_loctop::Soil








Yes

Field Name: Field_loctop::FLong







Yes

117

Field Name: Field_loctop::FLat








Yes

Field Name: Field_loctop::Dist_home


Find

Layout Name: Field_loctop Tooltip: "Infield is considered to be located less

than 0.3 x distance of outfield, while midfields are between 0.3 and 0.6 x distance of outfield"



Field Behavior: Allow field to be entered: In

Find mode, In Browse mode


Yes

Field Name: Field_loctop::FieldID








Disable visual spell-checking Go to next field using: Tab key

Yes

Field Name: Field_loctop::Irrig_type







Yes

118

Field Name: Field_loctop::TopPos







Yes

Field Name: Field_loctop::Dist_home_cls








Yes

Field Name: Field_loctop::Water_supply








Yes

Field Name: Field_loctop::Slope









Yes

Field Name: Field_loctop::Drainage






Yes

119

Bottom: 724 px

Right: 391 px



Field Name: Field_loctop::Elev



Top: 726 px Left: 138 px Bottom: 742

px

Right: 217 px Anchoring:

Left, Top




Yes


Type: Text: Go to plot layout Layout Name: Field_loctop Additional Properties: Change to hand cursor over button Rounded


Go to Layout [ “Yld_msr” (Yld_msr) ] Layouts: Yld_msr


Type: Text: Go to previous layout Layout Name: Field_loctop Additional Properties: Change to hand cursor over button Rounded


Go to Layout [ “Sentinel site” (SentinelSite) ] Layouts: Sentinel site

Charts

120

Chart Properties Coordinates Axis Info

Layout Name: Field_loctop Additional Properties: Type: Bar Title: Include Legend: Yes Use Data From: Related Records Related Table: Yld_msr Sort records: OnField: <Missing Field> Ascending order Reorder based on summary field: Off

Override field's language for sort: Off


X Axis Title: Series: Value: Yld_msr::<Field Missing> Y Axis Title: "Grain Yield (t/ha)" Series 1: Value: Yld_msr::TGrnYld Title:

Layout Objects: Yld_msr Regular Fields Field Name: Yld_msr::PlotID


Layout Name: Yld_msr







Yes

Field Name: Yld_msr::TGrnYld








Yes

Field Name: Yld_msr::TStvYld







Yes

121



Field Name: Plot_ident::Plotnr







Yes

Field Name: DT_ident::DTexper









Yes

Field Name: DTexper_ident::Crop







Yes

Field Name: Field_ident::FieldID







Yes

Field Name: DT_design::Treatment code

122







Yes

Field Name: Yld_msr::Harea









Yes

Field Name: Yld_msr::PLantno








Yes

Field Name: Yld_msr::Earno







Yes

Field Name: Yld_msr::StvFWp


Layout Name:

Yld_msr

Top: 282 px


Field Format:

Edit Box

Field Behavior:

Allow field to be entered: In Find mode, In Browse mode


Yes

123


Field Name: Yld_msr::CobFWp







Yes

Field Name: Yld_msr::StvFWsplts








Yes

Field Name: Yld_msr::StvDWsplts







Yes

Field Name: Yld_msr::CobFWsplts







Yes

Field Name: Yld_msr::GrnDWsplts


124








Yes

Field Name: Yld_msr::StvFWSS








Yes

Field Name: Yld_msr::StvDWSS







Yes

Field Name: Yld_msr::CobFWSS







Yes

Field Name: Yld_msr::GrnDWSS








Yes

125

Field Name: Yld_msr::GrnFW100








Yes

Field Name: Yld_msr::GrnDW100







Yes

Field Name: Yld_msr::TGrnYld_adj


Layout Name:

Yld_msr

Top: 510 px

Left: 167 px Bottom: 526 px Right: 246 px Anchoring: Left, Top

Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Yld_msr::TStvYld_adj








Yes


126

Type: Text: Go to pest and diseases layout Layout Name:

Yld_msr Additional Properties: Change to hand cursor over button Rounded



Go to Layout [ “Pest&Diseases” (Pest&Diseases) ] Layouts: Pest&Diseases


Type: Text: Go to previous layout Layout Name: Yld_msr Additional Properties: Change to hand cursor over button Rounded


Go to Layout [ “Field_loctop” (Field_loctop) ] Layouts: Field_loctop


Type: Text: Go to plant layout Layout Name:

Yld_msr Additional Properties: Change to hand cursor over button Rounded



Go to Layout [ “Crop_grwth” (Crop_grwth) ] Layouts: Crop_grwth

Layout Objects: Crop_grwth Regular Fields Field Name: Crop_grwth::Hght_plt1


Layout Name: Crop_grwth






Yes

127

Field Name: Crop_grwth::Date







Yes

Field Name: Crop_grwth::BArea_plt1








Yes

Field Name: Crop_grwth::Lfno_plt1








Yes

Field Name: Crop_grwth::PlotID









Yes

Field Name: Crop_grwth::Tillno_plt1






Yes

128



Field Name: Crop_grwth::Period







Yes

Field Name: Crop_grwth::PanLgth_plt1








Yes


Type: Text: Go to soil layout Layout Name: Crop_grwth Additional Properties: Change to hand cursor over button Rounded


Go to Layout [ <unknown> ] Layouts: <Missing Layout>


Type: Text: Go to previous layout

Layout Name: Crop_grwth Additional Properties:



Go to Layout [ “Yld_msr” (Yld_msr) ] Layouts:

Yld_msr

129

Change to hand cursor over button Rounded

Layout Objects: Pest&Diseases Regular Fields Field Name: Pest&Diseases::BirdDam_freq


Layout Name: Pest&Diseases






Yes

Field Name: Pest&Diseases::BirdDam_sev







Yes

Field Name: Pest&Diseases::TermDam


Layout Name:

Pest&Diseases

Top: 275 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Pest&Diseases::StemBor_freq







Yes

130

Field Name: Pest&Diseases::StemBor_sev








Yes

Field Name: Pest&Diseases::Disaff_freq







Yes

Field Name: Pest&Diseases::DisAff_sev


Layout Name:

Pest&Diseases

Top: 444 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Pest&Diseases::CobRot_freq








Yes

Field Name: Pest&Diseases::CobRot_sev



131

Pest&Diseases Left: 554 px Bottom: 566 px Right: 603 px




Field Name: Pest&Diseases::PlotID







Yes

Field Name: Pest&Diseases::DisName


Layout Name:

Pest&Diseases

Top: 438 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Pest&Diseases::DSPeriod







Yes

Field Name: Pest&Diseases::DScore









Yes

132

Field Name: Pest&Diseases::Plts_lodged








Yes

Field Name: Pest&Diseases::Plts_stand







Yes


Type:

Text: Go back Layout Name: Pest&Diseases Additional Properties:

Change to hand cursor over button Rectangular

Top: 150 px


Go to Layout [ “Yld_msr” (Yld_msr) ]

Layouts: Yld_msr

Layout Objects: Nutrient appl Regular Fields Field Name: Nutrient appl::Trt_code


Layout Name: Nutrient appl






Yes

133

Field Name: Nutrient appl::N








Yes

Field Name: Nutrient appl::P







Yes

Field Name: Nutrient appl::K


Layout Name:

Nutrient appl

Top: 144 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Nutrient appl::Ca








Yes

Field Name: Nutrient appl::Mg



134

Nutrient appl Left: 276 px Bottom: 136 px Right: 355 px




Field Name: Nutrient appl::S







Yes

Field Name: Nutrient appl::Zn


Layout Name:

Nutrient appl

Top: 98 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Nutrient appl::DT Experiment




Field Format: Drop-down List Display values

from: Experiment identifier

Include arrow to show and hide list



Yes

Field Name: Nutrient appl::Trt_description




Field Format: Drop-down List


Yes

135


Display values from: Treatment

select_desc Include arrow to

show and hide list


Field Name: Nutrient appl::B








Yes

Field Name: Nutrient appl::Mn









Yes

Field Name: Nutrient appl::Mo







Yes

Field Name: Nutrient appl::Cu







Yes

136



Field Name: Nutrient appl::OM







Yes

Layout Objects: Costs Regular Fields Field Name: Costs::Seed


Layout Name: Costs





Yes

Field Name: Costs::MOP


Layout Name:

Costs

Top: 217 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Costs::SSP


Layout Name: Costs





Yes

137

Field Name: Costs::TSP


Layout Name: Costs






Yes

Field Name: Costs::Urea


Layout Name: Costs





Yes

Field Name: Costs::Manure


Layout Name:

Costs

Top: 313 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Costs::Lime


Layout Name: Costs






Yes

Field Name: Costs::MultiN



138

Costs Left: 279 px Bottom: 377 px Right: 424 px




Field Name: Costs::Herbic


Layout Name: Costs





Yes

Field Name: Costs::Labour


Layout Name:

Costs

Top: 162 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: Costs::GrnTcrop


Layout Name: Costs





Yes

Field Name: Costs::Currency


Layout Name: Costs







Yes

139

Field Name: Costs::LPrep


Layout Name: Costs






Yes

Field Name: Costs::CComment


Layout Name: Costs





Yes

Layout Objects: DT_mngt Regular Fields Field Name: DT_mngt::DTID


Layout Name: DT_mngt





Yes

Field Name: DT_mngt::LPMethod








Yes

Field Name: DT_mngt::MADate

140







Yes

Field Name: DT_mngt::PKAppln









Yes

Field Name: DT_mngt::FNAppln








Yes

Field Name: DT_mngt::SNAppln







Yes

Field Name: DT_mngt::PlntDa


Layout Name:

DT_mngt

Top: 71 px


Field Format:

Edit Box

Field Behavior:



Yes

141


Field Name: DT_mngt::HarvDa







Yes

Field Name: DT_mngt::GappDa








Yes

Field Name: DT_mngt::ThinDa







Yes

Field Name: DT_mngt::LPDate







Yes

Field Name: DT_mngt::WdgFDa


142








Yes

Field Name: DT_mngt::WdgSDa








Yes

Field Name: DT_mngt::WdgTDa







Yes

Field Name: DT_mngt::PMethod







Yes

Field Name: DT_mngt::TPlntDa








Yes

143

Field Name: DT_mngt::EmDate








Yes

Field Name: DT_mngt::FDate







Yes

Field Name: DT_mngt::EarDate


Layout Name:

DT_mngt

Top: 145 px


Field Format:

Edit Box

Field Behavior:



Yes

Field Name: DT_mngt::Comment








Yes

Value lists The value lists that are currently in use in this database are captured in the table below. These also need to be review in order to improve consistency. For example, there is a value list for the ‘test crop’ and the ‘crop list’. These should basically be the same, that is if not

144

explicitly needed the same convention should be used for naming crops. Also, the formatting, in so far the use of capital letters is concerned should be done consistently. I prefer to have the values in the value list all without the use of capital letters if possible. This will ease any search being done that is defined as capital sensitive. The naming of the value lists themselves could make use of capital letters for the first letter of the first word in the name. Furthermore the use of forward or backward slashes should be prevented in the value list. Lastly it should be checked whether the value list are actually used and if used, they maybe should be updated according to the latest convention in naming sentinel site codes for example. The PlotID2, FieldID2 and ClusterID value list are no longer valid. Value List Name Source Values On Layouts

Latitude bearing Custom N, S

Longitude bearing Custom E, W

Cropping system Custom

tree, tree intercrop, banana, banana intercrop, cereal

continuous, cereal-legume rotation, cereal-legume intercrop, root & tubers, pulses & vegetables, fodder & fiber, grasses

fertility gradient Custom Infield, Midfield, Outfield Field_loctop

Manure types Custom Goat, Cattle, Chicken, FYM, Compost

Residue management Custom Burned on site, Removed, Incorporated/buried, Surface

applied,

YesNo Custom Yes, No

Soil Classification (FAO) Custom Acrisol, Andosol, Arenosol, Cambisol, Ferralsol, Fluvisol,

Histosol, Lixisol, Luvisol, Nitisol, Planosol, Solonchak, Vertisol Field_loctop

FieldID Field

Primary Field Field: Field_ident::FieldID Sort by first field

PlotID Field

Primary Field

Field: DT_design::Plotnr Sort by first field

Rainfall Distribution Custom Unimodal, Bimodal, Sentinel site

SentinelsiteID Custom Finkholo, Ibi, Kandara, Kasungu, Kiberashi, Koloko, Kontela,

Mbinga, Nkhata Bay, Pampaida, Sidindi, Thuchila

SLDepth Custom 0-20 cm, 20-50 cm,

PlotID2 Custom P01, P02, P03, P04, P05, P06, P07, P08, P09, P10

FieldID2 Custom F1, F2, F3

ClusterID Custom C01, C02, C03, C04, C05, C06, C07, C08, C09, C10, C11,

C12, C13, C14, C15, C16

Country Custom Kenya, Nigeria, Malawi, Mali, Tanzania Sentinel site

145

SentinelSite2 Custom KE1, KE2, KE3, ML1, ML2, MA1, MA2, NG1, NG2, NG3, TZ1,

TZ2, TZ3

PlantNumber Custom 1, 2, 3, 4, 5, 6, 7, 8

PointID Custom 1, 2, 3

Land preparation Custom Hand-hoe, Ox-plough

Land use history Custom 0-5 yr, 5-15 yr, 15-30 yr, >30 yr

Fertilizer amounts Custom low (<20 kg fertilizer/ha), medium (20-60 kg fertilizer/ha),

high (60-120 kg fertilizer/ha), very high (>120 kg fertilizer/ha),

Manure amount Custom Low (<5 t ha-1), Medium (5-10 t ha-1), High (>10 t ha-1)

Tillage Custom no-tillage, manual, animal traction, mechanical,

Tillage depth Custom Shallow (<5cm depth), Medium (5-15cm depth), Deep

(>15cm depth),

Irrigation Custom Basin, Furrow, Drip, Sprinkler, Field_loctop

Fallow Type Custom permanent, natural, improved,

TestCrop Custom Maize, Sorghum, Rice,

PlantingMethod Custom On Flat, On Ridges

Climatic zone Custom Humid, Sub-humid, semi-arid, Arid, ,

AB Custom a, b

crop list Custom maize, sorghum, upland rice, wetland rice, cassava, soybean,

cowpea, groundnut DTexper_ident

Water supply Custom rainfed, post-flooding, irrigated

topography Custom summit, slope, lower slope, bottomland, plain, terrace

Slope Custom level, gently sloping, sloping, moderately steep, steep, very

steep

drainage Custom poor, imperfect, well, excessive

Fallow period Custom short, medium, long

Frequency Custom seldom, regular, frequent

Fert_appl_meth Custom spot measured, spot hand, strip, broadcast

Chem fert clss Custom straight, incomplete mixture, complete mixture

BArea_meth Custom diam, diam&width, bascirc, various DTexper_ident

Diseases_maize Custom Brown spot, Downy mildews, Maize rusts, Blight, Tar spot

complex, Maize streak virus

SScode Field

Primary Field Field: SentinelSite::SScode

Sort by first field

Variety Field Primary Field DTexper_ident

146

Field: Cultivar Characteristics::Variety Sort by first field

Experiment identifier Field

Primary Field Field: DTexper_ident::Exper_ID Secondary Field Field: DTexper_ident::Exper_name Sort by first field

Nutrient appl Treatment Fert composition DT_ident

Experiment identification Field

Primary Field Field: DTexper_ident::Exper_ID Secondary Field Field: DTexper_ident::Crop Sort by first field

DT_design

New Value List Custom

Treatment select_desc Field

Primary Field Field: Treatment::Trt_description

Sort by first field

Nutrient appl

Treatment code_by desc Field

Primary Field Field: Treatment::Trt_code Secondary Field

Field: Treatment::Trt_description Show values only from second field Sort by second field

DT_design

Accounts

Account Name Password Status Type Privilege Set Description User must change password on next login

[Guest] ********* Inactive FileMaker [Read-Only Access] Off

Admin ********* Active FileMaker [Full Access] Off

jhuising ********* Active FileMaker [Full Access] Off

Extended Privileges

147

Keyword Description Privilege Set

fmiwp Access via Instant Web Publishing

fmxdbc Access via ODBC/JDBC [Full Access] [Data Entry Only] [Read-Only Access]

fmapp Access via FileMaker Network [Full Access] [Data Entry Only] [Read-Only Access]

fmmobile Access via FileMaker Mobile

fmxml Access via XML Web Publishing - FMS only

fmxslt Access via XSLT Web Publishing - FMS only

fmphp Access via PHP Web Publishing - FMS only

File Access

File Access Options

Block access by old clients (before version 11) Off

Block access by unauthorized files Off

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