Pre-harvest Forecast Research Articles

Pre-harvest forecasting of rice (Oryza sativa L.) yield based on weather variables and technological trend

Pre-harvest forecasting of rice (Oryza sativa L.) yield based on weather variables and technological trend

A goal programming approach for developing pre-harvest forecasts of crop yield

In this paper, an attempt has been made to develop a pre-harvest forecast of sugarcane yield. The forecast is based on plant biometrical characteristics such as plant height, girth of cane, number of canes per plot and width of third leaf from the top. Some of these characteristics are correlated and hence the conventional practice of fitting a regression model using least square technique for estimating the parameters does not lead to satisfactory pre-harvest forecasts. Keeping in view the difficulties relating to the violation of the assumptions of normality, independence and homoscedasticity of classical multivariate regression analysis, the present study proposes an alternative approach, which is free from assumptions. It employs a goal programming formulation to estimate the pre-harvest yield of sugarcane on the basis of measurements on biometrical characters of the plant. In order to assess the quality of forecasts, variance of residuals obtained from the proposed method has been compared with that obtained from the conventional regression analysis. The study reveals that there is no significant difference (P-value=0.43461) in the variances of the two residual series. Thus, without compromising the quality of forecast, the proposed alternative methodology can be adopted to estimate the sugarcane yield 3 months before harvest in situations, where the assumptions of conventional regression analysis are violated.

Statistical Modelling for Pre-Harvest Forecast:An Illustration with Rose

Crop yield forecast plays a vital role in arriving at pre-harvest yield estimate of a standing crop and to identify the stage at which reliable forecasting could be made before final harvest. In this paper, an attempt has been made to apply the regression technique for prediction of yield in rose. Rose, is an important flower crop not only for internal market but is also intended for export, and since it shrivels, estimation of yield of a standing crop before its actual harvest is essential. Based on results a model was developed, which showed that information from the first two pickings of a standing crop could be used to forecast rose yield to an extent of 77% two months before final harvest. It is also suggested to have a minimum sample size of 20 % to develop such a forecast model.

Use of CERES-Wheat model for wheat yield forecast in central Indo-Gangetic Plains of India

A methodology was developed for large area yield forecast using a crop simulation model and a discrete technology trend, and was applied to the coherent wheat yield variability zones of Eastern Uttar Pradesh, India. The approach consisted of three major steps: (a) prediction of technology trend yield using historical yield series of the region; (b) prediction of weather-induced deviation in wheat yield using CERES-Wheat simulation model and relating weather-induced deviation in simulated yield to deviation in observed yield deviations from technology trend; and (c) final yield forecast by incorporating predicted yield deviation in trend predicted yield. The regression coefficients for step (b) were generated using 10 years' data (1984/85–1994/95) and the reliability of the approach was tested on a data set of 5 years' independent data (1995/96–1999/2000). The results showed that this approach could capture year-to-year variability in large area wheat yield with reasonable accuracy. The Root Mean Square Error (RMSE) between observed and predicted yield was reported as 0·098 t/ha for the mean yield of 2·072 t/ha (4·72%). However, the RMSE was slightly higher in the forecasting period in comparison to the calibration period. The use of this methodology for issuing the pre-harvest forecast and the effect of upgrading the technology trend were also studied. The pre-harvest forecasts were made using in-season weather data up to the end of February and climatic-normal for the rest of the wheat-growing season, which showed good agreement with observed wheat yields. The forecasts of wheat yield for the season 1999/2000 were made using the technology trend up to 1994/95 and the updated technology trend up to 1998/99, which showed that the RMSE fell in the latter case, from 4·10 to 2·50%.

Sorghum yield modelling based on crop growth parameters determined from visible and near-IR channel NOAA AVHRR data

Abstract The normalized difference vegetation indices (NDVIs) deduced from multi-date NOAA-11 AVHRR/2 Local Area Coverage data have been used to generate growth profiles of 1989-90 Rabi (winter) sorghum in the tehsils of the three major sorghum growing districts, Ahmadnagar, Pune and Solapur, in Central Maharashtra, India. Spatially large and contiguous sorghum cultivation sites have been used to characterize sorghum. The crop growth equation suggested by Badhwar was used to model profiles. The tehsil average yields were regressed on the growth parameters, including senescence rate in early post-anthesis period for enabling pre-harvest forecasting (in model-1), and area under the complete growth profile (in model-2). The model-1 enables issuing of pre-harvest forecast of yields at least 45 days in advance.