Notes: Crop Yield Forecasts

The needs for forecasts are in the warning system. It requires crop simulation models CSMs, equational representation of the crop growth, and yields which plays important roles in the understandings of the agronomic results, and they are only the approximating the real world.

CSMs: 

statistical models

functional models

mechanistic models

simple models: across large land areas based on statistics of climate and historical yields, lesser details of soil plant system

CSMs relies on the meteorological data, agrometeorological data, soil data, remotely sensed data and agricultural statistics; different indices have been developed based on them. Standard regression techniques are applied which is called model calibration which results yield functions, which estimates the yield. The accuracies heavily relies on the input statistics.

Agrometeorological model:

Weather can have huge impact on all the process of crop growth and yields. Yield variability can be considered due to large impacts of the weather. Balaghi et al. (2013) used the rainfall as the main indicator for the crop yield variation in Morocco. In tropical countries, other factors such as soil moisture, and other meteorological factors should be considered. 

European Commission's Joint Research Center (JRC) used Biophysical Models Applications (BioMA) to simulate various crops in agricultural systems under different scenarios. Statistics and AAFC, Canada works on the model that can predict crops based on low resolution satellite imagery, historical crop survey estimates and agroclimatic information. Remote sensing information to predict wheat yields have already been proposed in 2014 for the insurance industry in Australia.

Remote Sensing Model;

Vegetative Indices (VI) have been used in monitoring of the green vegetation which shows the biophysical parameters in the crops. NDVI, proposed in 1978 by Deering is the most popular which directly relates with LAI and photosynthetic activity of the green vegetation and indirectly with Fraction of Absorbed Photosynthetically Active Radiation (fAPAR).  

FAO used the Agriculture Stress Index System to detect areas with water stress. The use was of Vegetation Health Index (VHI), derived from NDVI and Temperature Condition Index (TCI) derived from thermal infrared band from AVHRR. 

Mahalanobis National Crop Forecast Centre (MNCFC) of India combines agrometeorological models with remote sensing data for preharvest productions of the crops. Crop cutting experiments are also done to train the models. Brown et al. (2009) used NDVI to forecast the price of food using NDVI information. Similarly, NASS has attempted to used remote sensing for acreage estimation since 1972 and the applications of remote sensing has been expanded to other applcations. 

Abstracts from 
Handbook on remote sensing for agricultural statistics Chapter 6.4