Modelling Sunflower Yield Prediction at Field Scale

Abstract

Yield prediction at field scale based on biomass samples is an important issue for agriculture, because farmers may be able to change the development course of the agricultural process to get better final results. The aim of the present study was to stablish the basis for getting a sunflower yield prediction model, for that, this study investigated the partition of dry matter in sunflower plants, and also investigated if the dry mass of the second pair of leaves is a good predictor of the dry mass of the whole plant. Two commercial hybrids were planted for two years in two types of soils, and dry matter for the above ground biomass was determined for the second pair of leaves (top to down) and rest of the plant in the three first sampling each year, and also for capitulum in the other 5 samplings each year. Significant differences (P<0.05) were identified only for Soil Types for the variables Second Pair of Leaves Dry Mass and Capitulum Dry Mass in the sampling dates at 81, 92 and 103 days after planting. Dry mass of capitulum represented 30% of dry matter of the whole plant. Dry mass of Second Pair of Leaves was regressed on Dry Mass of the Whole plant and resulted for both types of soil in determination coefficients (R 2 ) higher than 0.95. By using of both results, an accurate model of yield prediction was obtained: at any age of the plant, dry mass of the second pair of leaves must be determined for predicting the whole biomass of the plant, and knowing that 30% of the dry mass of the whole plant is represented by capitulum, an accurate yield can be predicted. Short CommunicationAgriculture is an activity with high level of risk, due to it is subjected to many unpredictable factors, both natural [1] such as climate, and market-based such as prices [2]. For this reason, farmers face the problem to decide about the agricultural process at the same time that something occurs, such as insect attack, long dry period, or price fall; however, when farmers have to react to these conditions, they still do not have an accurate prediction about yield and total production in their fields, therefore decisions do not necessarily will be the best. Farmers could take the best decisions about the agricultural process, if they have good and reasonable predictions about the harvest. There are two approaches to help farmers to predict yield: without observation on the crop, and with observation on the crop. When prediction is carried out without observation on the crop, historical data of yield and weather are used to estimate future crop yield in agricultural processes that maybe have not started yet; there is no observation about the agricultural process, observation are about climatic conditions occurred in the past. This approach tries to find out climatic patterns to estimate crops yield, by means of several tools such as data mining [3,4], time series, panel, and cross-sectional models [5].On the contrary, yield prediction by means of observation on the crop is based on observations on the plant already established on the ...