research article estimating rice yield under changing...

Research ArticleEstimating Rice Yield under Changing Weather Conditions inKenya Using CERES Rice Model

W O Nyangrsquoau1 B M Mati1 K Kalamwa1 R K Wanjogu2 and L K Kiplagat3

1 Jomo Kenyatta University of Agriculture and Technology PO Box 62000 Nairobi 00200 Kenya2Mwea Irrigation and Agricultural Development Centre PO Box 210 Wangrsquouru 00103 Kenya3Western Kenya Irrigation Schemes PO Box 1010 Kisumu 40100 Kenya

Correspondence should be addressed to W O Nyangrsquoau oenganyangauyahoocom

Received 22 July 2013 Revised 26 December 2013 Accepted 21 January 2014 Published 26 March 2014

Academic Editor Bernd Lennartz

Copyright copy 2014 W O Nyangrsquoau et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Effects of change inweather conditions on the yields of Basmati 370 and IR 2793-80-1 cultivated under Systemof Rice Intensification(SRI) in Mwea andWestern Kenya irrigation schemes were assessed through sensitivity analysis using the Ceres rice model v 45 ofthe DSSAT modeling system Genetic coefficients were determined using 2010 experimental data The model was validated usingrice growth and development data during the 2011 cropping season Two SRI farmers were selected randomly from each irrigationscheme and their farms were used as research fields Daily maximum and minimum temperatures and precipitation were collectedfrom the weather station in each of the irrigation schemes while daily solar radiation was generated using weatherman in theDSSAT shell The study revealed that increase in both maximum and minimum temperatures affects Basmati 370 and IR 2793-80-1 grain yield under SRI Increase in atmospheric CO

2concentration led to an increase in grain yield for both Basmati and IR

2793-80-1 under SRI and increase in solar radiation also had an increasing impact on both Basmati 370 and IR 2793-80-1 grainyield The results of the study therefore show that weather conditions in Kenya affect rice yield under SRI and should be taken intoconsideration to improve food security

1 Introduction

Agriculture is always vulnerable to unfavorable weatherevents and climate conditions Despite technological advan-ces such as improved crop varieties and irrigation systemsweather and climate are important factors which play asignificant role in agricultural productivity [1] The impactsof climate change on agricultural food production are globalconcerns and for that matter Kenya is not an exceptionClimatic factors such as temperature rainfall atmosphericcarbon dioxide and solar radiation among others are closelylinked to agricultural production

An analysis of the trends in temperature rainfall sealevels and extreme events points to clear evidence of climatechange inKenya Studies indicate that temperatures have gen-erally risen throughout the country primarily near the largewater bodies [2 3] Other projections also indicate increasein mean annual temperature of 1 to 35∘C by the 2050s [4]

The countryrsquos arid and semiarid lands (ASALs) have also wit-nessed a reduction in extreme cold temperature occurrences[5]

In recent years Kenya has experienced food shortagesarising from declining farm productivity owing to low fer-tility levels high input costs and unreliable weather in theface of a rising population Being one of stable foods inKenya rice productivity is a major concern Understandingrice production in relation to weather changes is of greatimportance to boost food productivity

The system of rice intensification (SRI) offers the oppor-tunity to improve food security through increased riceproductivity by changing the management of the plantssoil water and nutrients while reducing external inputs likefertilizer and herbicides [6] The system proposes the use ofsingle very young seedling with wider spacing intermittentwetting and drying and use of mechanical weeders whichalso aerates the soil and enhances soil organic matter [7]

Hindawi Publishing CorporationInternational Journal of AgronomyVolume 2014 Article ID 849496 12 pageshttpdxdoiorg1011552014849496

2 International Journal of Agronomy

Crop growth simulation models provide the means toqualify the effects of climate soil and management oncrop growth productivity and sustainability of agriculturalproduction [8]These tools can reduce the need for expensiveand time-consuming field trials and could be used to analyzeyield gaps in various crops including rice [9] This studytherefore focuses on the assessment of the effects of changein weather conditions (temperature solar radiation andatmospheric CO

2concentration) in Kenya on Basmati 370

and IR 2793-80-1 grain yield cultivated under system of riceintensification using the CERES modeling system

2 Methodology

21 Description of the Study Area The study was conductedin four national irrigation schemes in Kenya namely Mweain Kirinyaga county Ahero in Kisumu county Bunyala inBusia county and West Kano in Kisumu county The fourirrigation schemes were chosen to allow for comparativeanalysis since they occur in different regions of the countryof diverse variations and also to make model calibration andvalidation possible Mwea irrigation scheme is situated inKirinyaga county of Kenya It lies within latitude 37∘131015840 E and37∘301015840 E and longitude 0∘321015840 S and 0∘461015840 S The West Kanoirrigation scheme is bounded to the west by Lake Victoria tothe north and south by Nyando and Nyabondo escarpmentsrespectively and to the east by the footsteps of Tinderethighlands It occupies the major part of Kano plains which islocated between longitudes 3410158401015840481015840 and 3510158401015840021015840 and betweenlatitudes 0010158401015840041015840 and 0010158401015840201015840 south [10] and lies to the easternside of the shores ofWinamGulf of Lake Victoria It occupies841 hectares (ha) at an altitude of 1137m above sea level

Bunyala irrigation scheme is located in Busia county ofKenya It lies in an area with alluvial soils in an altitudeof 1135ndash1200m above sea level and it draws its water fromNzoia River and is situated in two locations namely Bunyalacentral which is in Busia district and Usonga in Siaya Aheroirrigation scheme is located at 0∘ 081015840 0310158401015840 S 34∘ 581015840 0710158401015840 E1168m above sea level and in the middle of the Kano plain25 km southeast of Kisumu town The climate of the Kanoplain is relatively dry and the average temperatures are highduring the day and the soil of the scheme is of the black cottontype and is rather fertile [11]

22 Material Methods and Data Collection

221 Plant Material Basmati 370 and IR 2793-80-1 ricevarieties were used in this study This is because they are thetwo commonly grown varieties in Kenya

222 Field Selection and Design From each of the four irri-gation schemes under study two SRI farmers were randomlyselected and their farms were used as research fieldsThe riceprofile and management practices from nursery till harvestwere monitored

223 SRI Management Practices Adopted The crop man-agement data (ie agronomic data) required by the model

include planting date planting density row spacing plantingdepth irrigation amount and frequency fertilizer applicationdates and amounts The major crop management input dataused in the model for simulations in each irrigation schemeare shown in Table 1 which represent typical practices underthe system of rice intensification in these irrigation schemesunder consideration The system of rice intensification fun-damentals as described by Uphoff [13] comprises (i) early (8ndash15-day-old seedling) and quick shallow (1-2) transplanting(ii) transplanting single seedling per hill (iii) wider spacingin a grid pattern (iv) alternate wetting and drying of the soil(v) use of push rotary weeder and (vi) enhancing soil organicmatter

224 Data Collection Theminimumdata sets for the systemanalysis and crop simulation described in Technical Reportof IBSNAT [14] were used as a guide Data set for this studywas obtained from surveys interviews with the farmersobservations sample analysis and use of existing data frommeteorological stations and administration offices in MweaBunyala West Kano and Ahero irrigation schemes

The following data was collected as follows

(i) daily weather data maximum and minimum airtemperature precipitation and solar radiation (calcu-lated using weatherman)

(ii) soil data involved collection of set of input data onsoil characteristics at 5 cm and 25 cm depths beforeand during the cropping season (July to December2011) for Mwea Ahero West Kano and Bunyala irri-gation schemes on soil classes bulk density organiccarbon () sand silt clay () soil texture pH of soilin water organic carbon cation exchange capacitytotal nitrogen potassium and phosphorus

(iii) management practices variety plant density plantingdate irrigation weeding row spacing sowing depthand nitrogen fertilization

(iv) plant profile data soil data related to date of sowingdate of emergence date of floral initiation dateof synthesis date of physiological maturity panicleinitiation date (when 50 of the crop had reachedthose stages) plant population plant height grainweight and grain yield per area of production

(v) latitude of production area to evaluate day lengthduring the cropping season

The following six input files were created to run themodel

(i) weather file (FILEWTH) with annual daily solarradiation maximum air temperature minimum airtemperature and precipitation

(ii) soil file (FILES) with soil properties of the fourirrigation schemes under study

(iii) rice management file (FELEX)(iv) experimental data file (FILEA) with measured data

International Journal of Agronomy 3

Table 1 Crop management data used in the model

Serialnumber Simulation parameter Mwea Ahero West Kano Bunyala1 Planting method Nursery Nursery Nursery Nursery2 Cultivar Basmati 370 IR 2793-80-1 IR 2793-80-1 IR 2793-80-13 Transplanting date July 22 2011 August 18 2011 August 27 2011 August 2 20114 Planting distribution Hill Hill Hill Hill5 Row spacing 25 cm by 25 cm 25 cm by 25 cm 25 cm by 25 cm 25 cm by 25 cm6 Planting depth 2 cm 2 cm 2 cm 2 cm7 Transplanting age 8 days 12 days 14 days 10 days8 Plant per hill 1 1 1 19 Plants per m2 16 16 16 16

10Fertilizer application

14 DAP 125 kg haminus1 125 kg haminus1 125 kg haminus1 125 kg haminus1


11 Irrigation application(mm)

410mm in 16applications

410 in 15applications



(v) genetic coefficients file (FILEC) with thermal timefrom emergence to the end of juvenile stage (P1)rate of photoinduction (P2R) optimum photoperiod(P2) thermal time for grain filling (P5) conversionefficiency from sunlight to assimilates (G1) tilleringrate (TR) and grain size (G2)The cultivar coefficientswere determined by Gencalc in the DSSAT version45 using experimental data from Mwea irrigationscheme on Basmati 370 and IR2793-80-1 during theJuly to December 2010 cropping season

23 Data Analysis The CERES rice model version 45 ofthe DSSAT modeling system which is an advanced phys-iologically based rice crop growth simulation model wasused to predict rice (Basmati 370 and IR2793-80-1) growthdevelopment and response to various climatic conditionsprevailing in the four irrigation schemes This was throughdetermination of duration of growth stages dry matterproduction and portioning root system dynamics effectof soil water and soil nitrogen contents on photosynthesiscarbon balance and water balance [15] followed by sensitivityanalysis to assess the effects of change in weather conditionson Basmati 370 and IR 2793-80-1 grain yield under system ofrice intensification

24 Model Calibration The model was calibrated usingMwea irrigation scheme trials July to December 2010 crop-ping season SRI experimental data for both Basmati 370and IR 2793-80-1 for the main cropping season 2010 asreported by Ndiiri et al [16]This was through determinationof genetic coefficients for both Basmati 370 and IR 2793-80-1 using Gencalc 45 software in the DSSAT 45 softwareand assumed to apply to Ahero West Kano and Bunyalairrigation schemes

25 Model Validation The model was validated using therice growth and development data under SRI from Mwea

Bunyala Ahero and West Kano irrigation schemes duringthe cropping season July to December 2011 This was doneby comparing the observed results with simulated yield Inthis study combination of graphical tabular and statisticalanalysis was applied Model performance evaluation waspresented by the absolute Root Mean Square Error (RMSE)and Root Mean Square Error normalized (RMSEn) Bothcharacteristics are common tools to test the goodness of fit ofsimulation modelsThe RMSE (1) between the simulated andobserved values for a data set with 119899measured points and theRMSEn (2) are defined as

RMSE = [119899

sum

119894=1

(Si minusOb)2

119899

)]

05

(1)

RMSE119899= 100

[sum119899

119894=1(Si minusOb)2119899 )]

05

Obavg (2)

where Si = simulated value Ob = observed value and 119899 =number of observationsThe 119899 observed data points may be from one treatment ormultiple treatments [17] Goodness was evaluated visuallyand by computing index of agreement (119863) The index ofagreement is defined by [18] as shown in (3) The com-puted values of RMSE and 119889 value determine the degree ofagreement between the predicted values with their respectiveobserved values and a low RMSE value and a 119889 value thatapproaches 1 are desirable Consider

119863 = 1 minus

sum119899

119894=1(Si minusOb)2

sum119899

119894=1(

10038161003816100381610038161003816Si minusObavg)

10038161003816100381610038161003816Obi minusObavg

10038161003816100381610038161003816)

2 (3)

Normalized RMSE (RMSEn) was used to give a measure ()of the relative difference of simulated versus observed dataThe simulation was considered excellent with a normalizedRMSE less than 10 good if the normalized RMSE wasgreater than 10 and less than 20 fair if the normalizedRMSE


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Rain (mm)SRAD (MJm )2

Tmax (∘C)Tmin (∘C)

Figure 1 Mwea weather for 2011

was greater than 20 and less than 30 and poor if thenormalized RMSE was greater than 30 [19]

3 Results and Discussion

31 Weather Conditions during the Cropping Season Accord-ing to Hay and Walker [20] the primary atmospheric vari-ables that impact on crop growth are solar radiation air tem-perature humidity and precipitation They mentioned thatextreme weather at critical periods of a croprsquos developmentcan have large effects on its productivity and yield

311 Mwea Irrigation Scheme Atmospheric and Hydrolog-ical Variables The climate of Mwea irrigation scheme istropical governed by seasonal monsoon rainfall patternswhich are bimodal During 2011 maximum and minimumtemperatures rainfall and solar radiation varied as shown inFigure 1 During the cropping season the mean maximumtemperature (119879max) and minimum temperature (119879min) were274∘C and 192∘C respectively Mean solar radiation was165MJm2 while total precipitation was 439mm

312 Ahero Irrigation Scheme Atmospheric and HydrologicalVariables Figure 2 shows the atmospheric and hydrologicalvariables for 2011 and part of 2012 in Ahero irrigation schemeThe mean maximum temperature (119879max) and minimumtemperature (119879min) during the cropping season were 301∘C

and 170∘C respectively Mean solar radiation was 211MJm2while total precipitation was 7816mm

313 West Kano Irrigation Scheme Atmospheric and Hydro-logical Variables During 2011 rainfall solar radiation max-imum temperature and minimum temperature varied asshown in Figure 3 Mean maximum temperature (119879max) andminimum temperature (119879min) during the cropping seasonwere 274∘C and 174∘C respectively On the other handmeansolar radiation was 182MJm2 with a total precipitation of7434mm

314 Bunyala Irrigation Scheme Atmospheric and Hydrologi-cal Variables Rainfall solar radiation maximum tempera-ture (119879max) and minimum temperature (119879min) for Bunyalairrigation scheme varied as shown in Figure 4 for 2011 andpart of 2012 The mean maximum and minimum growthtemperatures were 294∘C and 178∘C respectively while themean solar radiationwas 198MJm2 with a total precipitationof 4823mm during the entire cropping period

32 Genetic Coefficients Determination The genetic coef-ficients for both Basmati 370 and IR 2793-80-1 rice cul-tivars were determined (calibration) by Gencalc softwarein the decision support system for agrotechnology transfer(DSSAT) using experimental data on real plot research inMwea irrigation scheme 2010 and the results were assumed toapply to Ahero Bunyala and West Kano irrigation schemes


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Figure 2 Ahero weather for 20112012

These are the phenological and growth genetic coefficients asdescribed by Peng et al [21] Huntrsquos technique [22] of geneticcoefficient calibration was used This technique estimatesgenetic coefficients using field data The processes was finallyaccomplished by running the model with appropriate coef-ficients comparing model output with actual data adjustingcoefficients and repeating process until acceptable fits wereobtained Table 2 shows the calculated genetic coefficients forBasmati 370 and IR 2793-80-1

The simulation was considered excellent with a normal-ized RMSE less than 10 good if the normalized RMSE wasgreater than 10 and less than 20 fair if the normalizedRMSE was greater than 20 and less than 30 and poorif the normalized RMSE was greater than 30 [19] Witha normalized RMSE of 1528 the GRAIN YLD targetline coefficients were taken as the best coefficients usedin this study for Basmati 370 Percentage of RMSE wascalculated using the number of data values not the degreesof freedom With RMSE of 1145 the GRAIN YIELD targetline coefficients were taken as the best coefficients used incurrent study for IR 2793-80-1

The final values for the eight cultivar coefficients thatdetermine vegetative (P1 P5 P2O andP2R) and reproductive(G1 G2 G3 and G4) growth and development for Basmati370 and IR 2793-80-1 are presented in Table 2 The cultivarcoefficient P1 defines the time from seedling emergence tothe end of juvenile phase (GDD) The coefficient P2O is theextent at which the development occurs at a maximum rateThe coefficient P2R is the extent to which phasic developmentfromvegetative to panicle initiationwas delayed for eachhourincrease in photoperiod above P2O The coefficient P5 is thetime from grain filling to physical maturity The coefficient

G1 defines the maximum spikelet number coefficient Thecultivar coefficient G2 is the maximum possible single grainsize under stress free conditions The coefficient G3 definesthe scalar vegetative growth coefficient for tillering relativeto IR64 The cultivar coefficient G4 defines the temperaturetolerance scalar coefficient

Genetic coefficients are sets of parameters that describethe genotype and environmental interactions [14] Theysummarize quantitatively how a particular cultivar respondsto environmental factors Estimation involves use of field orgrowth chamber studies many samples and exposure to dif-ferent photoperiods Genetic coefficients can be determinedin controlled or field conditions However plant growthin controlled environment chambers often differs markedlyfrom growth in the field Since most model users do not havecontrolled environmental facilities most determinations willuse field data [23 24]

33 Main Growth and Development Variables for Basmati370 and IR 2793-80-1 Rice Tables 3 and 4 show the meansimulated and observed main growth and developmentvariables for Basmati 370 and IR 2793-80-1 under system ofrice intensification in Mwea and Western Kenya irrigationschemes respectively

34 Model Validation The model was validated usingobserved growth and phenological data collected during the2011 cropping season for SRI in Mwea and Western Kenyairrigation schemes A good match was obtained betweenobserved and simulated grain yield with a RMSE of 0838 thaand a normalized RMSE (RMSEn) of 15027 An index ofagreement (119889) for grain yield closer to 1 (0875) also revealed


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Figure 3 West Kano Weather for 2011

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Figure 4 Bunyala weather for 20112012


Table 2 Calculated genetic coefficients for Basmati 370 and IR 2793-80-1

Cultivar Genetic coefficientP1 P2R P5 P2O G1 G2 G3 G4

Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)

Observed grain yield (tha)

y = 1375x minus 18326

R2 = 0786

Figure 5 Comparison ofmeasured and simulated grain yield of riceunder SRI 2011

Table 3 Main growth and development variables for Basmati 370under system of rice intensification in Mwea irrigation schemeKenya

Variable Simulated ObservedAnthesis 119 110Physiological maturity (dap) 139 137Yield at harvest maturity (tha) 5935 5915Unit weight at maturity (g) 0024 0023

that the model performed well in predicting the yield Theregression analysis gave a coefficient of determination (1198772)value of 0786 (Figure 5) In general the results from themodel validation indicate that CERES rice version 45 wasable to predict growth and development for Basmati 370and IR 2793-80-1 under SRI in Mwea and Western Kenyairrigation scheme in a good manner and therefore can beapplied as a study tool

The 119889-stat of a ldquogoodrdquo model should approach unityand the RMSE approach zero The RMSE is consideredas the ldquobestrdquo overall measure of model performance as itsummarizes the mean difference in the units of observed andpredicted values [25 26]

35 Sensitivity Analysis on Climatic Adaptations

351 Effects of Temperature Change Temperature regimegreatly influences not only the growth duration but also thegrowth pattern of the rice plant During the growing seasonthe mean temperature the maximum andminimum temper-ature rainfall distribution pattern and diurnal changes or acombination of these may be highly correlated with grainyields [27] Effects of increase in temperature on Basmati370 grain yield in Mwea irrigation scheme and IR 2793-80-1

rice grain yield in Ahero West Kano and Bunyala irrigationschemes were assessed by increasing the maximum andminimum temperatures by +1 +2 +3 +4 and +5 followedby subsequent simulations

The simulated results in Table 5 show that increase inboth maximum and minimum temperature led to a decreasein Basmati 370 grain yields planted under system of riceintensification in Mwea irrigation scheme As compared tomaximum temperature increase in minimum temperaturehadmore pronounced negative impacts on Basmati 370 yieldThis more pronounced negative impact of minimum temper-ature on rice yield could be explained by increased respirationlosses during the vegetative phase [28] and reduced grain-filling duration and endosperm cell size during the ripeningphase [29]

Temperature regimes greatly influence not only thegrowth duration but also the growth pattern and the pro-ductivity of rice crops The critical temperatures for thedevelopment of the rice plant at different growth phases arehighlighted by Yoshida [12] as shown in Table 6

Other studies on rice productivity under global warmingalso suggest that the productivity of rice and other tropicalcrops will decrease as global temperature increases Mohan-drass et al [30] using the Hadley-coupled model predicteda yield decrease of 145 percent for summer rice cropsacross nine experiment stations in India in 2005 Peng et al[28] reported that the yield of dry-season rice crops in thePhilippines decreased by as much as 15 percent for each1∘C increase in the growing season mean temperature InBangladesh the impact of climate change on high yield ricevarieties was studied by Karim et al[31] using the CERESrice model and several scenarios and sensitivity analysisThey found that high temperatures reduced rice yields in allseasons in most arid locations

At a mean maximum temperature of 274∘C and a meanminimum temperature of 174∘C under the SRI manage-ment practices in West Kano irrigation scheme the modelsimulated the grain yield for IR 2793-80-1 under SRI tobe 8299 tha for West Kano environment As shown inTable 7(a) increase in maximum temperature up to +3 led toan increase in IR 2793-80-1 grain yield and decreased beyond+3∘C Yield at minimum temperature also increased withincrease in minimum temperature up to +2 beyond which itwas not able to be simulated

The mean maximum and minimum temperatures for theentire cropping period for Ahero irrigation scheme 2011 were301∘C and 170∘C respectively These mean temperaturesresulted in a yield of 4459 tha Changing these values at aninterval of plus 1∘C resulted in changes in the simulated yieldas shown in Table 7(b) The simulated results in Table 7(b)show that increase inmaximum temperature led to a decrease


Table 4 Main growth and development variables for IR 2793-80-1 under system of rice intensification in Ahero West Kano and Bunyalairrigation schemes Kenya

Variables Ahero West Kano BunyalaSimulated Observed Simulated Observed Simulated Observed

Anthesis 134 120 110 102 120 108Physiological maturity (dap) 155 148 136 135 140 134Yield at harvest maturity (tha) 4459 5286 8299 6916 4662 4199Unit grain weight at maturity (g) 0026 0027 0026 0026 0026 0027

Table 5 Effects of plus maximum and minimum temperatures onsimulated Basmati 370 yield grain under SRI in Mwea Kenya

Plustemperature(∘C)

Grain yield atmaximum

temperature (tha)

Grain yield atminimum

temperature (tha)+1 5413 5258+2 5409 5128+3 4355 4538+4 4184 mdash+5 369 mdash

Table 6 Critical temperatures for the development of rice plant atdifferent growth stages

Growth stages Critical temperature (∘C)Low High Optimum

Germination 16ndash19 45 18ndash40Seedling emergence 12 35 25ndash30Rooting 16 35 25ndash28Leaf elongation 7ndash12 45 31Tillering 9ndash16 33 25ndash31Initiation of panicle primordial 15 mdash mdashPanicle differentiation 15ndash20 30 mdashAnthesis 22 35-36 33Ripening 12ndash18 gt30 20ndash29Source Yoshida [12]

in grain yield except for the increment of +1 and +4Minimum temperature increment also led to changes in yieldbut in a decreasing as well as increasing pattern

Increase in maximum temperatures in Bunyala irrigationscheme led to increase in IR 2793-80-1 grain yield up to+3 beyond which it led to a decrease in yield (Table 7(c))Minimum temperature increase up to +1 also led to increasein yield beyond which it could not be simulated as theminimum temperature becomes unfavorable Hardacre andTurnbulL [32] state that temperature affects the durationof crop growth and consequently the time during whichincident radiation can be intercepted and transformed to drymatter Temperature also affects final leaf number [33] andleaf canopy development [34 35] which defined crop leaf areaindex thereby determining the proportion of the incidentradiation intercepted [36] by the crop and accumulation ofdry matter At the same time while using ORYZA1 andINFOCROP rice simulationmodels at the current CO

2levels

Table 7 Effects of temperature change on simulated IR 2793-80-1grain yield under SRI in West Kano Ahero and Bunyala irrigationschemes

Plustemperature

(∘C)

Grain yield atmaximumtemperature

(tha)

Grain yield atminimumtemperature

(tha)

(a) WestKano

+1 9036 8924+2 9170 9170+3 9360 mdash+4 8689 mdash+5 8256 mdash

(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala

+1 5631 4772+2 5660 mdash+3 6542 mdash+4 6140 mdash+5 5824 mdash

of 380 ppm Krishnan et al [37] predicted average rice yieldchanges of minus720 and minus666 respectively for every 18∘Cincrease in temperature

Temperature is considered to be one of the dominantfactors that affect the growth and yield of rice Each phasehas its low and high temperature thresholds The effect oftemperature on vegetative growth of rice plants was reviewedin relation to germination early growth rooting tilleringand the critical temperature common for different physiolog-ical plant properties that were 0ndash3∘C 15ndash18∘C 30ndash33∘C and45ndash48∘C respectively [38] Low temperature in early growthstages retards the development of seedling and dry matterproduction [12] In tropical regions the temperature increasedue to the climate change is probably near or above theoptimum temperature range for the physiological activitiesof rice [39] Such warming will thus reduce rice growthIn addition higher temperatures will cause spikelet sterilityowing to heat injury during panicle emergence [40]

Changes in mean temperatures can shorten the time tomaturity of a crop thus reducing yield Other experimental


0123456789

10

1 2 3 4 5

MweaWest KanoBunyala

Plus solar radiation (MJm

Gra

in y

ield

(th

a)

2)

Figure 6 Effects of plus solar radiation on Basmati 370 grain yieldin Mwea and IR 2793-80-1 grain yield in West Kano and Bunyalaunder SRI

studies have also shown that even a few days of temper-ature above a threshold value if coincident with anthesiscan significantly reduce yield through affecting subsequentreproductive processes [41] Generally the effect of increasingtemperature above the tolerance limit on rice potentialproduction is generally negative Temperature beyond theoptimum level reduces the photosynthesis increase the res-piration and shorten the vegetation and grain-filling periodsRice yield is negatively correlated with high (gt35119900C) temper-ature during the reproductive phase [40]

352 Effects of Change in Solar Radiation The mean solarradiation levels recorded in Mwea irrigation scheme were165MJm2 during the entire cropping period Throughsensitivity analysis the effects of solar radiation on Basmati370 grain yields under SRI in Mwea irrigation scheme wereassessed by addition of solar radiation up to 5MJm2 atan interval of 1 unit and their yield simulated as shown inFigure 6

Increase in solar radiation led to an increase in grainyield for Basmati 370 cultivated under SRI in Mwea irriga-tion scheme At a mean solar radiation of 165MJm2 themodel simulated Basmati 370 grain yield under SRI to be5935 tha Increase in solar radiation up to 5MJm2day at aninterval of 1 unit (Figure 6) resulted in 185ndash20 increase inBasmati 370 grain yields under SRI Van Keulen [42] usinga simulation model predicted that an increase of 20 intotal global radiation resulted in 10ndash20 increase in grainyield of rice Similarly an overall decrease in solar radiationby 20 depressed the yield by 30 due to incompletelight interception during the postanthesis phase Figure 7shows the solar radiation requirements of rice at differentstages of growth and development Monteith [43] found therelationship between solar radiation and grain yield of wheatThe study indicated that grain yield was the product of the

Criticalsunlightperiod

Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth

Figure 7 Solar radiation requirements of rice at different stages ofgrowth and development (adapted from Stansel [51])

intercepted light the efficiency of conversion of interceptedlight to dry matter and partitioning of dry matter to grains

Themodel simulated results showed that increase in solarradiation led to an increase in grain yield for IR 2793-80-1under SRI inWest Kano irrigation scheme but up to a certainlimit which in this case was 212MJm2 (Figure 6) This maybe attributed to the fact that vegetative growth of most plantsincreases linearly with solar radiation up to a limit beyondwhich no further increase occurs [44] In a simulation studyon the effect of solar radiation on growth of wheat and rice itwas revealed that themaximum Leaf Area Index was reducedby 76 in wheat and 59 in rice when the solar radiationwas decreased by 100 from normal On the other handwith increase in radiation by 10 LAI increased in wheatby 71 [45] Further the grain yield of wheat increased from07 to 68 and rice from 12 to 13 when solar radiationwas increased up to 10 and the grain yield declined underdecreasing amounts of solar radiation

Increase in solar radiation in Bunyala irrigation schemehad an increasing impact on IR 2793-80-1 grain yield underSRI as shown in Figure 6This is attributed to favourable solarradiation levels during the growth and development stagesStansel et al [46] state that solar radiation requirements ofrice differ from one stage to another Shading during thevegetative stages affects yield and yield components slightlyDuring the reproductive stages however shading has a verypronounced effect on spikelet number and yields Shadingduring ripening periods also decreases the percentage of filledspikelets and reduces grain yields considerably

353 Effects of Change in Atmospheric CO2Concentration

The standard CO2concentration for the current study was

380 ppm Sensitivity analysis was done to determine theeffects of change in CO

2concentration by increasing it at


0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)

AheroWest KanoBunyala

Change in CO2 concentration (ppm)

Figure 8 Effects of increase in CO2concentration on IR 2793-80-1

grain yield in Ahero and West Kano irrigation schemes Kenya

Table 8 Variation in basmati 370 grain yield in Mwea irrigationscheme under SRI with increase in atmospheric CO2 concentration

Plus CO2 concentration100 200 300 400

Mwea (grain yield (tha)) 6459 714 7176 8458

an interval of 100 ppm up to 500 ppm from the standardconcentration of 380 ppm

Increasing the CO2concentration by 100 ppm 200 ppm

300 ppm and 400 ppm increased the Basmati 370 grain yieldunder SRI in Mwea irrigation scheme by 91 203 209and 425 respectively (Table 8) According toMott [47] thispositive performance can be attributed to four key parame-ters a decreased stomatal aperture enhanced photosynthesisincreased total biomass and changed biomass partitioning

At a standard CO2concentration of 380 ppm in West

Kano irrigation scheme the simulated IR 2793-80-1 grainyield under SRI was 8299 tha Increase in CO

2concentra-

tion in West Kano irrigation scheme led to an increase in IR2793-80-1 grain yield under SRI Compared to the yield atstandard CO

2concentration increase in CO

2concentration

by 100 ppm 200 ppm 300 ppm and 400 ppm led to a 4387 94 and 122 increase in IR 2793-80-1 grain yieldrespectively under SRI (Figure 8)

Carbon dioxide is the prime substrate for photosynthesisMajority of plants including rice fixed CO

2via C3 pathway

At ambient CO2levels C3 pathway is less efficient than C4

pathway due to the enzyme Rubisco has dual and competingaffinity to both O

2and CO

2 At elevated CO

2the carboxyla-

tion rate increases which will increase photosynthesis of C3plants Studies with rice have indicated that elevated CO

2

generally increases tiller number photosynthesis biomassand grain yield as well as plant nitrogen (N) uptake andbiological N fixation [48]

Simulations of IR 2793-80-1 grain yield under differentconcentrations CO

2in Ahero irrigation scheme are shown

in Figure 8 These predictions were made using a standardconcentration of atmospheric CO

2of 380 ppm and then

increased at a level of 100 ppm 200 ppm 300 ppm and400 ppm Increasing the CO

2concentration by 100 ppm and

400 ppm from the standard CO2concentration of 380 ppm

led to 168 and 542 increase in grain yield respectivelyAt 380 ppm standard CO

2concentration in Bunyala

irrigation scheme during 2011 the model simulated a yieldof 4662 tha for IR 2793-80-1 under SRI Increasing the con-centration by 100 ppm 200 ppm 300 ppm and 400 ppm fromthe standard concentration the effects of change in CO

2on

IR 2793-80-1 under SRI were assessed and the correspondingyields were simulated as shown in Table 8 The results inTable 8 reveal that increase in CO


irrigation scheme led to an increase in IR2793-80-1 grainyield cultivated under SRI Increasing the CO

2concentration

by 100 ppm led to 390 increase in grain yield 200 ppmby 556 and 300 ppm by 671 and 400 ppm by 819Similar studies indicate that rice is particularly responsiveto increased carbon dioxide concentration According toHunsaker et al [49] high carbon dioxide concentrationsincrease water use efficiency In addition high carbon dioxidelevels increase plantsrsquo resistance to salinity and drought andincrease nutrient uptake [50]

4 Conclusion

Weather changes affect Basmati 370 and IR 2793-80-1 yieldunder SRI in Kenya Increase in maximum and minimumtemperatures beyond optimum temperatures for rice produc-tion led to a decrease in yield and minimum temperaturechanges had more profound negative impacts as comparedto maximum temperature changes Change in atmosphericCO2concentration led to an increase in Basmati 370 and IR

2793-80-1 grain yield Increase in solar radiation in Mweairrigation scheme led to an increase in Basmati 370 grainyield and also an increase in IR 2793-80-1 grain yield in WestKano and Bunyala irrigation schemes Therefore to improverice production under system of rice intensification in Kenyaproper understanding of the prevailing weather conditionsand regular monitoring is necessary

Conflict of Interests

The authors declare that they have no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors are grateful to the National Irrigation Board(NIB) for their financial support towards this project underthe SRINIB upscaling project Special thanks go to ProfessorGerrit ofWashingtonUniversity USA for his comprehensivesupport towards acquisition of the DSSAT software and hisenviable advice during model simulation MIAD and JKUATlaboratories are acknowledged for their assistance on soil


and plant analysis and the authors also acknowledge all thestaff and farmers in Mwea Ahero West Kano and Bunyalairrigation schemes for their support during data collection

References

[1] J K Basak M A Ali N M Islam and A M RashidldquoAssessment of the climate change on boro rice production inBangladesh using DSSAT modelrdquo Journal of Civil Engineering(IEB) vol 38 no 2 pp 95ndash108 2010

[2] SM Kingrsquouyu L AOgallo and E K Anyamba ldquoRecent trendsof minimum and maximum surface temperatures over EasternAfricardquo Journal of Climate vol 13 no 16 pp 2876ndash2885 2000

[3] GoK National Climate Change Response Strategy ExecutiveBrief Ministry of Environment and Mineral Resources Gov-ernment of Kenya (GoK) Nairobi Kenya 2010

[4] SEI The Economics of Climate Change in Kenya StockholmEnvironment Institute Oxford UK 2009

[5] M K Kilavi ldquoAnalyzing the temporal characteristics of extremetemperature events overASALs and the coastal regions ofKenyaas an indicator of climate changerdquo 2008

[6] D Berkelaar ldquoSRI the system of rice intensification less can bemorerdquo ECHO Development Notes vol 10 no 70 pp 1ndash7 2001

[7] N Uphoff and A Kassam ldquoCase study system of rice intensi-fication in agricultural technologies for developing countriesrdquoFinal Report Annex 3 European Technology AssessmentGroup Karlsruhe Germany 2009

[8] A S Nain and K C Kersebaum ldquoCalibration and validation ofCERES model for simulating water and nutrients in GermanyrdquoinModellingWater andNutrient Dynamics in Soil-Crop Systemschapter 12 pp 161ndash181 Springer Amsterdam The Netherlands2007

[9] H Pathak C Li and RWassmann ldquoGreenhouse gas emissionsfrom Indian rice fields calibration and upscaling using theDNDC modelrdquo Biogeosciences Discussions vol 2 no 1 pp 77ndash102 2005

[10] V DrsquoCosta Characterization and interpretation of the soils of theKano plains for irrigation agriculture [MS thesis] University ofNairobi 1973

[11] R Niemeijer and J Hoorweg ldquoCommercialization of riceand nutrition a case from West Kenyardquo in AgriculturalCommercialization Economic Development and NutritionJ von Brann and E Kennedy Eds p 267 The JohnsHopkins University Press Baltimore Md USA 1994httpwwwifpriorgsitesdefaultfilespubspubsbooksvonbraun94vonbraun94ch17pdf

[12] S Yoshida ldquoTropical climate and its influence on ricerdquo IRRIResearch Paper Series 20 IRRI Los Banos Philippines 1978

[13] N Uphoff ldquohigher yields with fewer external inputs Thesystem of rice intensification and potential contributions toagricultural sustainabilityrdquo International Journal of AgriculturalSustainability vol 1 no 1 pp 38ndash50 2003

[14] IBSNATTheIBNETDecade Department ofAgronomy and SoilScience College of Tropical Agriculture andHuman ResourcesUniversity of Hawaii Honolulu Hawaii USA 1993

[15] J T Ritchie E C Alocilja U Singh and G Uehara ldquoIBSNATand the CERES-rice modelrdquo inWeather and Rice Proceedings ofthe InternationalWorkshop on the Impact ofWeather Parameterson Growth and Yield of Rice 7ndash10 April 1986 pp 271ndash281International Rice Research Institute Manila Philippines 1987

[16] J A Ndiiri BMMati P GHome B J Odongo andNUphoffldquoComparison of water saving of paddy rice under system of riceIntensification (SRI) growing in Mwea Kenyardquo InternationalJournal of Current Research and Review vol 4 no 6 2012

[17] L Ma and H M Selim ldquoPredicting atrazine adsorption-desorption in soils a modified second-order kinetic modelrdquoWater Resources Research vol 30 no 2 pp 447ndash456 1994

[18] L R Ahuja LMa and T A HowellAgricultural SystemModelsin Field Research andTechnologyTransfer CRCPressNewYorkNY USA 2002

[19] K Loague and R E Green ldquoStatistical and graphical methodsfor evaluating solute transport models overview and applica-tionrdquo Journal of Contaminant Hydrology vol 7 no 1-2 pp 51ndash73 1991

[20] R K Hay and A J Walker An Introduction to the Physiologyof Crop Yield Longman Scientific and Technical Harlow EssexUK 1989

[21] U Sing D C Godwin J T Ritchie et al ldquoCeres-Rice 35(980)rdquo International Fertilizer Development Research Center(Ricer980EXE Program file) 1998

[22] L A Hunt J T Ritchie P S Teng and K J Boote ldquoGeneticCoefficients for the IBSNAT crop modelsrdquo in AgronomyAbstract pp 16ndash17 ASA Madison Wis USA 1989

[23] B Acock and M C Acock ldquoPotential for using long-termfield research data to develop and validate crop simulationrdquoAgronomy Journal vol 83 no 1 pp 56ndash61 1991

[24] L A Hunt and S Pararajaingham ldquoGENCALC genotypecoefficient calculatorrdquo in Users Guide Version 30 PublicationNo LAH-01-94 Crop Simulation Series No 3 Department ofCrop science University of Guelph 1994

[25] C J Willmott ldquoSome comments on the evaluation of modelperformancerdquo Bulletin of the American Meteorological Societyvol 63 no 11 pp 1309ndash1313 1982

[26] A S Toit and D L Toit ldquoShort description of the modelstatistical package and weather analogue programrdquo ReportUniversity of Florida Gainesville Fla USA 2003

[27] J C Moomaw and B S Vergara ldquoThe environment of tropicalrice productionrdquo 1965

[28] S Peng J Huang J E Sheehy et al ldquoRice yield declinewith higher night temperature from global warmingrdquo in RiceIntegrated CropManagement Towards a Ricecheck System in thePhilippines E D Redona A P Castro and G P Llanto Edspp 46ndash56 PhilRice Nueva Ecija Philippines 2004

[29] S Morita J Yonemaru and J Takanashi ldquoGrain growth andendosperm cell size under high night temperatures in rice(Oryza sativa L)rdquo Annals of Botany vol 95 no 4 pp 695ndash7012005

[30] S Mohandrass A A Kareem T B Ranganathan and SJeyaraman ldquoRice production in India under the current andfuture climaterdquo in Modeling the Impact of Climate Change onRice Production in Asia B Mathews M J Kroff D Bacheletand H H van Laar Eds pp 165ndash181 CABI Wallingford UK1995

[31] Z Karim M Ahmed S G Hussain and K B RashidldquoImpact of climate change on the production of modern ricein Bangladeshrdquo in Implications of Climate Change for Interna-tional Agriculture Crop Modeling Study C Rosenzweig and AIglesias Eds EPA 230-B-94-003 US Environmental ProtectionAgency Washington DC USA 1994

[32] A K Hardacre and H I TurnbulL ldquoThe growth and develop-ment of maize (Zea mays L) at five temperaturesrdquo Annuals ofBotany vol 58 pp 779ndash787 1986


[33] J C Stevenson and M M Goodman ldquoEcology of exotic racesof maize 1 Leaf number and tillering of 16 races under fourtemperatures and two photoperiodsrdquo Crop Science vol 12 no6 pp 864ndash868 1972

[34] M Tollenaar T B Daynard and R B Hunter ldquoEffect oftemperature on rate of leaf appearance and flowering date inmaizerdquo Crop Science vol 19 no 3 pp 363ndash366 1979

[35] M R Thiagarajah and L A Hunt ldquoEffects of temperature onleaf growth in corn (ZeamaysL)rdquoCannadian Journal of Botanyvol 60 no 9 pp 1647ndash1652 1982

[36] R C Muchow and P S Carberry ldquoDesigning improved planttypes for the semiarid tropics agronomists view pointsrdquo inSystems Approaches for Agricultural Development F W T P deVries Ed pp 37ndash61 Kluwer Academic Press Dordrecht TheNetherlands 1993

[37] P Krishnan D K Swain B C Bhaskar S K Nayak and R NDash ldquoImpact of elevated CO

2and temperature on rice yield

and methods of adaptation as evaluated by crop simulationstudiesrdquo Agriculture Ecosystems and Environment vol 122 no2 pp 233ndash242 2007

[38] I Nishiyama Climate and Rice IRRl 1976[39] K P Hogan A P Smith and L H Ziska ldquoPotential effects of

elevated CO2and changes in temperature on tropical plantsrdquo

Plant Cell and Environment vol 14 no 8 pp 763ndash778 1991[40] T Satake and S Yoshida ldquoHigh temperature-induced sterility in

indica rice at floweringrdquo Japanese Journal of Crop Science vol47 no 1 pp 6ndash17 1978 (Japanese)

[41] T RWheeler P Q Craufurd R H Ellis J R Porter and P V VPrasad ldquoTemperature variability and the yield of annual cropsrdquoAgriculture Ecosystems and Environment vol 82 no 1ndash3 pp159ndash167 2000

[42] HVanKeulen ldquoPotential wheat yields inZambiamdasha simulationapproachrdquo Agricultural Systems vol 14 no 3 pp 171ndash192 1984

[43] J L Monteith ldquoSolar radiation and productivity in tropicalecosystemsrdquo Journal of Applied Ecology vol 9 no 3 pp 747ndash766 1972

[44] httpwwwfaoorgdocrepx5648ex5648e0ehtm[45] S S Hundal and P Kaur ldquoEnvironment and the effect of

environmental stresses on potential production of major cerealcrops in Punjabrdquo in Proceedings of the International Conferenceon Sustainable Agriculture pp 11ndash12 Haryana AgriculturalUniversity Hisar India January 1995

[46] J Stansel C N Bollich J R Thysell and V L Hall ldquoTheinfluence of light intensity and nitrogen fertility on rice yieldsand components of ricerdquo Rice Journal vol 68 no 4 pp 34ndash351965

[47] K A Mott ldquoDo stomata respond to CO2concentrations other

than intercellularrdquo Plant Physiology vol 86 no 1 pp 200ndash2031988

[48] W G Cheng K Inubushi K Yagi H Sakai and K KobayashildquoEffects of elevated carbon dioxide concentration on biologicalnitrogen fixation nitrogen mineralization and carbon decom-position in submerged rice soilrdquo Biology and Fertility of Soilsvol 34 no 1 pp 7ndash13 2001

[49] D J Hunsaker B A Kimball P J Pinter Jr et al ldquoCO2enrich-

ment and soil nitrogen effects on wheat evapotranspiration andwater use efficiencyrdquo Agricultural and Forest Meteorology vol104 no 2 pp 85ndash105 2000

[50] C Kaya H Kirnak and D Higgs ldquoEffects of supplementarypotassium and phosphorus on physiological development andmineral nutrition of cucumber and pepper cultivars grown at

high salinity (NaCl)rdquo Journal of Plant Nutrition vol 24 no 9pp 1457ndash1471 2001

[51] J W Stansel ldquoEffective utilization of sunlightrdquo in Six Decadesof Rice Research in Texas vol 4 of Research Monograph pp 43ndash50 Texas Agricultural Experiment Station US Department ofAgriculture 1975

Submit your manuscripts athttpwwwhindawicom

Nutrition and Metabolism

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Food ScienceInternational Journal of

Agronomy


International Journal of



Microbiology

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

AgricultureAdvances in


PsycheHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BiodiversityInternational Journal of


ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GenomicsInternational Journal of


Plant GenomicsInternational Journal of


Biotechnology Research International


Forestry ResearchInternational Journal of


Journal of BotanyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EcologyInternational Journal of


Veterinary Medicine International


Cell BiologyInternational Journal of


Evolutionary BiologyInternational Journal of



Crop growth simulation models provide the means toqualify the effects of climate soil and management oncrop growth productivity and sustainability of agriculturalproduction [8]These tools can reduce the need for expensiveand time-consuming field trials and could be used to analyzeyield gaps in various crops including rice [9] This studytherefore focuses on the assessment of the effects of changein weather conditions (temperature solar radiation andatmospheric CO

2concentration) in Kenya on Basmati 370

and IR 2793-80-1 grain yield cultivated under system of riceintensification using the CERES modeling system

2 Methodology

21 Description of the Study Area The study was conductedin four national irrigation schemes in Kenya namely Mweain Kirinyaga county Ahero in Kisumu county Bunyala inBusia county and West Kano in Kisumu county The fourirrigation schemes were chosen to allow for comparativeanalysis since they occur in different regions of the countryof diverse variations and also to make model calibration andvalidation possible Mwea irrigation scheme is situated inKirinyaga county of Kenya It lies within latitude 37∘131015840 E and37∘301015840 E and longitude 0∘321015840 S and 0∘461015840 S The West Kanoirrigation scheme is bounded to the west by Lake Victoria tothe north and south by Nyando and Nyabondo escarpmentsrespectively and to the east by the footsteps of Tinderethighlands It occupies the major part of Kano plains which islocated between longitudes 3410158401015840481015840 and 3510158401015840021015840 and betweenlatitudes 0010158401015840041015840 and 0010158401015840201015840 south [10] and lies to the easternside of the shores ofWinamGulf of Lake Victoria It occupies841 hectares (ha) at an altitude of 1137m above sea level

Bunyala irrigation scheme is located in Busia county ofKenya It lies in an area with alluvial soils in an altitudeof 1135ndash1200m above sea level and it draws its water fromNzoia River and is situated in two locations namely Bunyalacentral which is in Busia district and Usonga in Siaya Aheroirrigation scheme is located at 0∘ 081015840 0310158401015840 S 34∘ 581015840 0710158401015840 E1168m above sea level and in the middle of the Kano plain25 km southeast of Kisumu town The climate of the Kanoplain is relatively dry and the average temperatures are highduring the day and the soil of the scheme is of the black cottontype and is rather fertile [11]

22 Material Methods and Data Collection

221 Plant Material Basmati 370 and IR 2793-80-1 ricevarieties were used in this study This is because they are thetwo commonly grown varieties in Kenya

222 Field Selection and Design From each of the four irri-gation schemes under study two SRI farmers were randomlyselected and their farms were used as research fieldsThe riceprofile and management practices from nursery till harvestwere monitored

223 SRI Management Practices Adopted The crop man-agement data (ie agronomic data) required by the model

include planting date planting density row spacing plantingdepth irrigation amount and frequency fertilizer applicationdates and amounts The major crop management input dataused in the model for simulations in each irrigation schemeare shown in Table 1 which represent typical practices underthe system of rice intensification in these irrigation schemesunder consideration The system of rice intensification fun-damentals as described by Uphoff [13] comprises (i) early (8ndash15-day-old seedling) and quick shallow (1-2) transplanting(ii) transplanting single seedling per hill (iii) wider spacingin a grid pattern (iv) alternate wetting and drying of the soil(v) use of push rotary weeder and (vi) enhancing soil organicmatter

224 Data Collection Theminimumdata sets for the systemanalysis and crop simulation described in Technical Reportof IBSNAT [14] were used as a guide Data set for this studywas obtained from surveys interviews with the farmersobservations sample analysis and use of existing data frommeteorological stations and administration offices in MweaBunyala West Kano and Ahero irrigation schemes

The following data was collected as follows

(i) daily weather data maximum and minimum airtemperature precipitation and solar radiation (calcu-lated using weatherman)

(ii) soil data involved collection of set of input data onsoil characteristics at 5 cm and 25 cm depths beforeand during the cropping season (July to December2011) for Mwea Ahero West Kano and Bunyala irri-gation schemes on soil classes bulk density organiccarbon () sand silt clay () soil texture pH of soilin water organic carbon cation exchange capacitytotal nitrogen potassium and phosphorus

(iii) management practices variety plant density plantingdate irrigation weeding row spacing sowing depthand nitrogen fertilization

(iv) plant profile data soil data related to date of sowingdate of emergence date of floral initiation dateof synthesis date of physiological maturity panicleinitiation date (when 50 of the crop had reachedthose stages) plant population plant height grainweight and grain yield per area of production

(v) latitude of production area to evaluate day lengthduring the cropping season

The following six input files were created to run themodel

(i) weather file (FILEWTH) with annual daily solarradiation maximum air temperature minimum airtemperature and precipitation

(ii) soil file (FILES) with soil properties of the fourirrigation schemes under study

(iii) rice management file (FELEX)(iv) experimental data file (FILEA) with measured data

















RMSE = [119899

sum

119894=1

(Si minusOb)2

119899

)]

05

(1)

RMSE119899= 100

[sum119899

119894=1(Si minusOb)2119899 )]

05

Obavg (2)


119863 = 1 minus

sum119899


sum119899

119894=1(

10038161003816100381610038161003816Si minusObavg)

10038161003816100381610038161003816Obi minusObavg

10038161003816100381610038161003816)

2 (3)



0

20

40

60

80

100

120

140

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date














0

10

20

30

40

50

60

70

80

90

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date












0

10

20

30

40

50

60

70

80

90

100

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date




0

10

20

30

40

50

60

70

80

90

100

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date







Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014








































RMSE = [119899

sum

119894=1

(Si minusOb)2

119899

)]

05

(1)

RMSE119899= 100

[sum119899

119894=1(Si minusOb)2119899 )]

05

Obavg (2)


119863 = 1 minus

sum119899


sum119899

119894=1(

10038161003816100381610038161003816Si minusObavg)

10038161003816100381610038161003816Obi minusObavg

10038161003816100381610038161003816)

2 (3)



0

20

40

60

80

100

120

140

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date














0

10

20

30

40

50

60

70

80

90

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date












0

10

20

30

40

50

60

70

80

90

100

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date




0

10

20

30

40

50

60

70

80

90

100

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date







Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014

























0

20

40

60

80

100

120

140

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date














0

10

20

30

40

50

60

70

80

90

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date












0

10

20

30

40

50

60

70

80

90

100

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date




0

10

20

30

40

50

60

70

80

90

100

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date







Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014

























0

10

20

30

40

50

60

70

80

90

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date












0

10

20

30

40

50

60

70

80

90

100

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date




0

10

20

30

40

50

60

70

80

90

100

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date







Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014

























0

10

20

30

40

50

60

70

80

90

100

010

120

1112

01

2011

230

120

1103

02

2011

140

220

1125

02

2011

080

320

1119

03

2011

300

320

1110

04

2011

210

420

1102

05

2011

130

520

1124

05

2011

040

620

1115

06

2011

260

620

1107

07

2011

180

720

1129

07

2011

090

820

1120

08

2011

310

820

1111

09

2011

220

920

1103

10

2011

141

020

1125

10

2011

051

120

1116

11

2011

271

120

1108

12

2011

191

220

1130

12

2011

Wea

ther

par

amet

ers

Date




0

10

20

30

40

50

60

70

80

90

100

010

120

1115

01

2011

290

120

1112

02

2011

260

220

1112

03

2011

260

320

1109

04

2011

230

420

1107

05

2011

210

520

1104

06

2011

180

620

1102

07

2011

160

720

1130

07

2011

130

820

1127

08

2011

100

920

1124

09

2011

081

020

1122

10

2011

051

120

1119

11

2011

031

220

1117

12

2011

311

220

1114

01

2012

280

120

1211

02

2012

250

220

1210

03

2012

240

320

12

Wea

ther

par

amet

ers

Date







Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014



























Basmati 370 5770 2095 1875 1275 4185 0024 100 100IR 2793-80-1 5404 1509 2404 1275 6381 0026 100 100

4

5

6

7

8

9

4 45 5 55 6 65 7 75

Sim

ulat

ed g

rain

yie

ld (t

ha)


y = 1375x minus 18326

R2 = 0786





















temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014































temperature (tha)








2levels


Plustemperature

(∘C)


(tha)


(tha)

(a) WestKano


(b) Ahero

+1 4583 4551+2 4496 4800+3 4378 4388+4 4608 4874+5 4181 4682

(c) Bunyala






0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014

























0123456789

10

1 2 3 4 5



Gra

in y

ield

(th

a)

2)






Firs

t till

er

Pani

cle in

itiat

ion

Firs

t hea

ding

Med

ium

dou

gh

Mat

urity

Seed

ing

emer

genc

e

50

75

100

Accu

mul

ativ

e sun

light

requ

irem

ents

(p

ossib

le (

))

Stage of growth










0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014

























0123456789

10

100 200 300 400

Gra

in y

ield

(th

a)













2concentra-



2concentration



2via C3 pathway



2and CO

2 At elevated CO

2the carboxyla-


2












2on




2concentration


4 Conclusion





Acknowledgments




References





























































Journal of




Agronomy





Microbiology




Volume 2014


























References





























































Journal of




Agronomy





Microbiology




Volume 2014




















































Journal of




Agronomy





Microbiology




Volume 2014
























research article estimating rice yield under changing...

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