Agricultural holdings typology construction using agricultural census data: what typology and what variables to be selected for robust typology?

ByEloi Ouédraogo: Eloi.Ouedraogo@fao.org

(Regional Statistician, FAO Office for Africa (RAF), Accra, Ghana)

Ankouvi Nayo: ankouvi@hotmail.com Lecturer-Researcher, ENSEA, Abidjan, Côte d’Ivoire,

Outline• Introduction and objective

• Methodology

• Data

• Findings

Introduction

• Agriculture is one of the sectors of economy which has to face numerous uncertainties (climate uncertainties – epizootic – land ownership issue – scarcity of resources …).

• Data collection in this sector is attempts to control part of these uncertainties and in the best possible way bring adequate solutions.

• One of the major source of information in the sector remains the General Census for Agriculture.

• One of the major changes in the 2010 round of agricultural censuses is that FAO has recommended to combine both population housing and agricultural censuses.

Introduction

• The main question in this approach is «Which criteria can be used to identify the core module containing structural information?» In other words, how to identify and choose the strongest structural variables to integrate into population census?

• This study aims at identifying the best typology for agricultural holdings and the strongest structural variables. It will consist in making an analysis based on the variables that describe the agricultural structure rather than the performance which change at a quick pace over the years.

Methodology

• The methodology used in this study combines two approaches. – The first consists in defining a typology of holdings based on

the more relevant variables – the second analyses the stability of the typology through

the analysis of the Markov chains.

• For defining the typology: a multivariate approach will be privileged. This approach permits to create homogeneous groups of holdings taking into consideration their agricultural practices and the characteristics (size, king farming,…).

Factor Analysis• Factor analysis methods such as

– the Component Analysis (APC), – the Factor Analysis of Correspondence (FAC) – the Multiple Correspondence Analysis (MCA) – the Multiple Factor Analysis (MFA).

• The use of these techniques aims at the definition of a typology of holdings through an ascending hierarchical classification.

• This choice is justified by the fact that this method allow to use qualitative (alphabetic or nominal) variables as well as quantitative in the same analysis without requiring that the quantitative variables are made nominal by creating classes of values.

• The advantage of using the MFA is that it allows to preserve all the richness of information in the analysis without affecting it by subjective and fallacious regroupings;

Markov chains

• A Markov chain (discrete-time Markov chain or DTMC), is a mathematical system that undergoes transitions from one state to another, among a finite or countable number of possible states.

• It is a random process usually characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it.

Source: http://en.wikipedia.org/wiki/Markov_chain, 24/10/2013: 6:36 am

Data description

• Data : 2004, National Census for Agriculture of Mali.

• 9.834 holders =>(extrapolation: 805.194 holders)

• The information contained in the NCA (National Census for Agriculture).

• The variables are about the localization of the holding in particular, the demographic and economic characteristics of the holding, its means of production (land, the animals and the equipment), access to water (irrigation), the use of inputs, the crops cultivated and the species of animals bred.

structural variables• In this analysis, the structural variables are:

– The type of holdings (traditional, modern, etc.)

– Land use and their size;

– Crop type (seasonal or perennial) ;

– Agricultural equipments ;

– type of used inputs (Seeds, fertilizers, etc.);

– access and the land’s mode of development ;

– demographic characteristics of agricultural household members

– agricultural labor ;

– Conversion of agricultural products;

– type and size of the livestock ;

– the environment (access to credit, to vulgarization services , to veterinary services, to information)

– Other income-generating activities (aquaculture and fishing, trade, etc.).

Findings

Criteria of selection or exclusion

• the overrepresentation of a modality (75 ).– they will be positioned as additional elements– This approach allowed therefore to be freed

(artificially) from variables which can have disruptive effects on the analysis.

• Missing information– Contribution of the missing information in the

factor analysis is more than 10%

Figure 1: Cloud of variables and of classes of partition (axis 1 and 2)

13

Typology of Holding

Intensive activityExtensive activity

C3: extensive BreedingVery little culture48% of holdings25,4% of sowed areas

C4: cash crop (Cotton)

17.5% of holdings37% of exploited areas

C1: practicing mainly livestock

17% of holdingslow mobilization of the

workforce

C2: extensive food crop 17.3 holding35.8% of sowed areas

well equipped

poorly equipped

Transition probabilities estimation

• The transition probabilities are estimated from a generalized orderly logistic modeling after having tested the hypothesis of proportional odd ratio (or of parallelism)

• the class 4th was considered as reference class in the logistic modeling

• Some classes of the typology can remain stable whatever the consideration of certain variables. Indeed, variables relative to the size of the holding (Sown area with cereal and a tuber), to the use of amendment of the ground (manure) and of improved seeds, as well as the access to credit have no effect on the class 1.

• This situation can simply be explained by the fact that this kind of holder who farms mainly for their livelihood does not allow an easy and immediate mutation even when they have access to certain support (inputs, credit, technical support, advice, …).

Transition probabilities estimation • As for the holdings practicing breeding mainly (class 3),

the access to the land encourages a change in that class towards the class 1.

• The more the exploited area increase, the more the holding has chances to be classified in superior classes.

• This result is valid for the classes 1 and 2. In this case, the class 1 will have a non-null probability to mutate to the class 2 and that of the class 2 to the class 4. The ownership of land for the holdings in class 2 tends to make them mutate to class 1.

Transition probabilities estimation

Prediction state

initial state

Class 1 Class 2 Class 3 Class 4

Class 1 96.8 1.3 0.4 1.6Class 2 3.7 88.8 0.5 7.1Class 3 16.8 0.9 82.2 0.00Class 4 5.7 0.6 0.2 93.5

Initial distribution : λ0 = (48.1; 17.3; 17.1; 17.5)

Stability of the typology• At each step t we are interested by

• This chain is irreducible because all the states communicate. – This is a very important result because when the requirements are met

in the context of an intervention or project, it is possible to help a holding to change state from a State “i” to a State “j”. However changes observed will depend on the time required to observe the impacts of the intervention

– This reflects also the fact that there be no absorbent State. Indeed, when a holding reaches a State "i" assumed the best one (resp. the worst one), when the initial conditions which allowed to achieve the current status are not met, the holding could downgrade (resp. to a lower State).

• the class “i“ of the typology is assumed to be stable at the step (period) “t” when

Transition probabilities at step 2

State j

State i

Class 1 Class 2 Class 3 Class 4

Class 193.9 2.4 0.7 3.1

Class 27.4 78.9 0.9 13.0

Class 330.1 1.8 67.6 0.3

Class 410.9 1.2 0.4 87.6

P(X2= j∨X 0=i)

Transition probabilities at step 4

State j

State i

Class 1 Class 2 Class 3 Class 4

Class 1 88.9 4.2 1.2 6.0Class 2 14.4 62.7 1.4 21.9Class 3 48.8 3.3 46.0 1.7Class 4 20.0 2.2 0.7 77.2

P(X2= j∨X 0=i)

period of mutationCategory time of

transferVariables or Modality

Variables or Modality with low stability

Up to 3 step-Exclusive Breeding practice-Size of herd of small ruminants ;-Size of herd of cattle ;

Variables or Modality with medium stability Up to 7 step

-Practice of food crops ;-Short term breeding practice (poultry breeding)- medium and high size holding (surface of more than two hectares) ;

Variables or Modality with strong stability

At least 12 step

-Traditional agricultural practice (no manure, no treatment)-No equipment-Low workforce-small size holding (very low exploited surfaces)-practice of cash crops-equipment endowment-modern farming (inputs, improved seed)

Conclusion• On the basis of a typology to four classes three large groups of

variables have been created. The least stable groups are those whose changes were observed at the end of three years.

• For moderately stable variables, mutations are observed at the end of seven years while for the most stable variables, changes were observed over twelve year. These conclusions are valid assuming a State of the markov chain is equivalent to a crop year.

• On the other hand, to confirm the results obtained in this study, it would have been useful to use data from at least two GCA in the same country and observe mutations occurring during the intercensal period. In this case, the annual mutations could be provided by an analysis based on data from the annual agricultural surveys.

Thank you for your kind attention