Measuring the Effect of Heavy Spring Rainfall on Technical Efficiency of Tomato Production in Arsanjan

Agricultural performance depends not only on the availability of physical resources and agricultural technologies, but also on the climate and environmental conditions prevailing on the farm. As the Earth's temperature rises, precipitation has become an unpredictable variable and has led to phenomena such as floods, droughts, etc. in addition, such phenomena have created a new sense of uncertainty for farmers with increasing uncertainty about the prediction of the future climate, and have faced producers with new types of climate problems. It is predicted that the frequency and severity of climate change will increase the challenges facing risk management, as atmospheric conditions (sudden and heavy rains, hurricanes, severe changes in air temperature and acid rain) affect the efficiency of production inputs (fertilizer, seed, pesticides, and so on). ...) Influential. Severe climate change during the growing season can cause serious damage to agriculture and affect farmers' decisions to allocate farm inputs and lead to reduced production and efficiency; Therefore, in analyzing the efficiency and financial and physical performance of the farm, in addition to the production inputs, we should also consider the climatic conditions in which the effect of spring rains on the technical efficiency of tomatoes is investigated.

Data from the crop year of 1395-1396 and The model used is a Modified Stochastic Production Frontier Approach (MSPF),with changes in the model so that in addition to the physical inputs of production, heavy rainfall during tomato growth and farm restoration costs (partial re-planting, part of the farm damaged by rainfall) Have been added to the the traditional model of Stochastic Production Frontier Approach (SPF) to estimate their impact on technical performance (in conventional models due to the lack of atmospheric variables, the estimated parameters are biased and the correlation between efficiency and bias is neglected and the bias bias causes the farm manager to decide. Conflicts d Wrong resource attribute and failed to make optimal use of resources). In addition, if the explanatory variables in the production function are correlated with the variables affecting technical performance and this correlation is ignored in the estimation of the production function parameters, it leads to biased estimates of the parameters thus avoiding this kind of correlation, Betis and Coeli's one-step approach is used to determine the factors affecting production efficiency, in which the technical efficiency of the fields is related to the socio-economic conditions of the farmers, management skills and demographic characteristics, etc.Eventually the Modified Stochastic Production Frontier Approach have been compared and the results have been compared with the traditional model of Stochastic Production Frontier Approach.

According to the results of the correlation coefficients estimated between production inputs and rainfall and farm repair costs, there is a relatively strong correlation between heavy rainfall, seed rate and labor force. Heavy rains increase the need for labor to compensate and repair the farm. Rainfall is also negatively correlated with pesticides, since it is washed off by rainfall and instead of being absorbed by pesticides, it is washed out and eliminated, and the farmer is aware of this problem and uses less pesticide during heavy rainfall. Precipitation has a negative effect on pests. Rain is positively correlated with fertilizer as expected, as rainfall can absorb fertilizer faster into the soil and root of the plant. Rain is positively associated with seed because heavy rains destroy the transplants and the farmer must re-transplant. Although the relationship between repair costs and rainfall was not positive but significant, it may be because some of the repair costs (more fertilizer, manpower and seed consumption) were in the three inputs. To test the effect of atmospheric factors on production and technical efficiency, the model parameters were estimated once without the presence of these variables and once added to the model. Comparison of the results from the application of these two models confirms that ignoring the atmospheric variables in the estimation of the technical efficiency leads to bias in the estimation parameters. According to another part of the

1- Heavy rainfall caused 100% damage to the farm resulting in technical efficiency reduction, 2- Adding atmospheric variables to the model (MSPF pattern), all input coefficients and technical efficiency change This reflects the impact of atmospheric variables on efficiency and production that were overlooked in previous studies. 3. The high technical efficiency difference estimated in the two models means that in the rainless model, the technical efficiency estimates are more biased, 4. Toxins Chemical in the SPF pattern has a significant relationship with production but in the MSPF model, the coefficient of this variable is not significant because it is likely to wash away the heavy pesticide precipitation and eliminate its effects. 5 - Interesting findings of this study Interaction between inputs And there is heavy rainfall so that heavy rainfall not only damages the farm but also reduces the efficiency of other inputssignificant.6- Proper use of inputs can increase technical efficiency by 14%, resulting in a 19% increase in production and an 11.6% decrease in production costs

Atmospheric factors, especially severe and short-term rains, reduce the technical efficiency and tomato production.