Introduction Rice cultivation is fundamental for rural Thailand. Rice is the major staple food and one of major exports. It is an important source of household income in rural areas. Understanding of farmers’ ability to cope with production shocks and existing constraints on poor farmers is key for rural development. Objectives 1. Detailed and unbiased modeling of the process of rice cultivation which would allow for accurate analysis of factors behind farmer’s decisions and take into account sequential nature of farmer’s decision- making within production cycle. 2. Rice is a very water-dependent plant and rice cultivation in Thailand is mainly rainfed. Our second objective is analysis of the sensitivity of rice crop to rainfall at different stages of cultivation cycle and ability of farmers to respond to rainfall shocks. 3. Use the developed detailed model of rice cultivation to examine the importance of perceived socio- economic constraints on poor farmers. Modeling Crop Production Characteristics of Crop Production Multistage Structure and Importance of Timing: Cultivation is dictated by crop’s biological growth, which consists of distinct phases. Crop’s need for and responsiveness to a given physical input changes across growth phases. As a result, timing of input application is a crucial characteristic of production input..  Quantities of a given input used in several different phases throughout production process cannot be lumped together and applied all at once at a random point in time.  Same physical input applied during two different production phases constitutes two distinct production inputs Sequential Nature of Crop Production: Phases of crop’s biological growth are sequential in time: growth during later phases builds upon results achieved in previous phases and depends on shocks that affected crop in earlier phases. Input choices at later phases incorporate responses to realized production shocks and are therefore endogenous with respect to both inputs in earlier phases and final output. Production shocks from previous phases affect input decisions in later phases through:  Their direct effect on the level of output in previous phase;  By expanding producer’s information set and therefore by altering his expectations of future shocks. © California Rice Research Board, Traditional Single-Stage Production Function Approach Results in Biased Estimates Single-stage modeling of production does not account for the dynamic sequential nature of crop growth. Entire production process occurs in one time interval, during which all inputs are utilized simultaneously. Strong assumption that all input decisions are finalized before the start of production process. Measures of physical inputs are aggregated over the whole production process.  Importance of timing of input application is disregarded. The fact that input application in later phases is chosen as a response to the progress of plant development is not accounted for. As a result, endogeneity of later input applications with respect to earlier input applications and earlier production shocks is not taken into account. Information feedback between stages is ruled out. Production function is a single equation where final output (amount of grain harvested) depends on aggregate measures of physical inputs Multistage Production Function Approach Accounts for Both Timing Significance in and Sequential Nature of Rice Cultivation Separate entire production process into N sequential, non-overlapping stages. Each stage is a separate production sub-process with its own production function, and output from previous stage is an input into next stage production function.  This allows treating multiple quantities of the same physical input which are applied at different times as multiple production inputs. In other words, production inputs are given both physical and timing characteristics. Within each stage, multiple operations can be performed simultaneously. For each stage i, inputs choices are made at the start of the stage, after intermediate output from previous stage, i – 1, is observed and before production shocks for the starting stage i are realized. Production process is described by a system of simultaneous equations, with one equation representing final output (amount of grain harvested) as a function of stage-specific inputs with both timing and physical characteristics, and all other equations describing intermediate input decisions. Methods Data Use data on rice-growing households in Sisaket for five production cycles, Observation unit is rice plot, typically with several plots per household. Timing of stages is individual for each plot. Several implications:  Timing and duration of stages and of overall cycle vary across households and plots.  Aggregate production shocks such as rainfall have different effect on different plots because they may hit these plots during different stages of production.  The effect of an idiosyncratic shock will change depending on timing and duration of stages. Use village-level rainfall and soil moisture data to construct plot-specific expectations of future production shocks. Crop Production: Combining Socio-Economic and Biophysical Data and Models John Felkner Kamilya Tazhibayeva and Robert M. Townsend The University of Chicago, Department of Economics, 1126 E. 59th St., Chicago, IL 60637,

Use DSSAT Simulations to Approximate Intermediate Outputs DSSAT software simulates, day by day, biological growth of a plant. DSSAT ignores variations in farmers’ experiences and responses to crop state. Rather, it assumes that plant development proceeds under optimal, laboratory-like conditions. DSSAT inputs:  Environmental data such as soil quality, daily rainfall and temperature.  Non-labor and non-equipment inputs such as seeds and fertilizer. DSSAT allows for variations in both physical quantities and timing of application of these inputs. DSSAT does not account for:  Labor inputs.  Farmer’s decisions with regards to some operations involved in rice cultivation (e.g., weeding, watering).  Idiosyncratic production shocks. Because DSSAT settings are more refined than reality, DSSAT simulations should overpredict actual crop state.  As a correction, we use DSSAT simulations together with labor and other inputs (not accounted for by DSSAT) used in previous production stage to measure intermediate output from this previous stage. Comparing Accuracy of Estimated Economic Model and DSSAT We have four measures of final output (measured in kg of rice grain): Actual Farmer’s prediction during production cycle (made before harvest stage) Economic model prediction DSSAT simulation. We construct three prediction errors as differences between actual output and farmer’s, model, and DSSAT predictions, and plot the densities of these three error measures: Solid lines are error densities. Dotted lines are normal densities with the same mean and standard error as the correspondingly colored error density. Farmers are Most Accurate; DSSAT Substantially Overpredicts; Model Slightly Underpredicts Results are very intuitive: Farmers have the most accurate information on both the state of their crops and realized production shocks. DSSAT simulates plant growth under assumption of no adverse individual production shocks or mismanagement. Because the economic model models farmers’ input decisions and takes into account human input, it is more accurate than DSSAT. However, because of limited information, it underestimates farmers’ ability to respond to production shocks. Estimation Results Overall statistical significance of estimated production functions. Results confirm the importance of timing characteristic of inputs, as same physical input has statistically different effect on final output depending on when it is applied. Rainfall results:  Rainfall expectations have statistically significant effect on farmers’ input decisions.  Both direction and magnitude of the effect of expected rainfall on input usage vary from stage to stage, as well as depending on the purpose of the operation in which the input is used. Coefficients on both DSSAT simulations and measures of previous inputs are statistically significant. This shows that these two groups of indicators of intermediate output from previous stage capture different components of previous output. Correlation of Prediction Errors with Socio-Economic Variables We look at four categories of socio-economic variables: BORROWING by household during production cycle from different sources; also includes the POVERTY indicator. OCCUPATION variable indicates whether rice cultivation is the main income source for the household head. Years of EDUCATION. AGE variables measure percent of household members in difference age groups. This accounts for both availability of labor input by household members and presence of income earners. Perceived Socio-Economic Constraints Do Not Bind Note: * indicates significance at 5% or better. Correlations with actual output illustrate importance of socio-economic constraints:  Significance of credit constraints: actual output increases with access to borrowing and decreases with poverty.  Significance of education constraint: actual output increases with more education. Model error is not significantly correlated with any of the socio-economic variables.  Significance of credit and education constraints disappears when farmer’s production decisions are accurately modeled. Farmer’s harvest outlook depends on the source of borrowing. Conclusions Multistage approach enables us to obtain unbiased estimates of state-specific production functions. These provide additional insights into factors influencing input choices at various stages of rice cultivation. Rainfall expectations have significant effect on farmers’ decisions. Accordingly, the following may have beneficial effect for farmers:  Ability to insure against rainfall shocks.  Greater flexibility when responding to rainfall shocks (e.g., access to irrigation).  Access to accurate prediction data. Preliminary error correlation results indicate that perceived constraints on poor farmers – such as credit and education constraints – might actually have little significant effect on farmer’s productivity. This finding has significant implications for rural development policies. (Please note that this result is tentative and work is being currently undertaken on the more substantial analysis of this question.)