Yield Gap Analysis Using Remote Sensing (Northwest of Iran)

The difference between the actual yield and the potential yield are the main concepts used in the yield gap analysis. The potential yield or the theoretical yield is the yield that could have been achieved in the optimum conditions whose  level upto 70% (called attainable yield) is considered to be possible to be achieved from the best practices. The actual yield, usually estimated at the end of the growth period of the crops, are the real or the observed yield. All factors, biotic or abiotic and crop management factors, are involved to limit the potential yield (estimated by the crop models). 

The objective of estimating the yield gap is to point the factors responsible for the difference between the yields, and for sure the other objective is the determination of the potential and the actual yields. The gap analysis methods involve ***calculation of the point ot regional based yield by the field experiments*** or ***by the simulation of the spatial and temporal surveys***. 

Current research have utilized the remote sensing techinques for the crop yield evaluation. The advantage remain in the spatial data at large scales and outputs to be processed as the models. The use case is Normalized Vegetation Index (NDVI) for the estimation of the actual yield.

A study entitled "Yield Gap Analysis Using Remote Sensing and Modelling Approaches: Wheat in the Northwest of Iran" utilizes boundary-line analysis to estimate the attainable yield and alter the yield response to the different factors. The research uses the SSM-iCrop2 model to estimate the potential yield, and also calculates the achievable yield by the boundary-line analysis and satellite imagery oriented procedures. 

Method

Study Area: 

Golestan Province, Iran (6 counties)

Data: Climate, Soil, crop management information, land use layer, Landsat 8 satellite images—for SSM-iCrop2 model—wheat fields by Supervised Classificatin Method (SVM)—generation of NDVI map.

*radiometeric correction*, *radiometric calibration* and *gap fill tools* done in ENVI 5.3,

*atmospheric correction* by transforming DN (digital value to refelection energy and reflectance using) FLASH algrorithm

~ 234 ground points (70% for the training and 30% validation),

~ four crop categories for detection of the wheat fields: wheat, barley, canola. other crops.

Classifaication accuracy by Kappa 

~ NDVI layer generated, relationship between actual recorded wheat yields and NDVI established

NDVI data (dependent variable) and actual yield as independent variable were plotted against each other; omission of the outliers were done, suitable function was fitted to the data—actual yield calculated using obtained function - yield gap by subtracting the average yield of the studied farm from potential yield. 

SSM-iCrop2 Model: requires daily meteorological data, soil map HarvestChoice Soil Map

~ attainable yield by two procedures: i) estimating by SSM-iCrop2 model and defining attainable yield as 70% of the potential yield  ii) estimation of the attainable yield by NDVI-actual yield regression line by boundary-analysis

Result

Actual yield were calculated, line fitted between actual yield and NDVI values found May to have the best regression relations. The relations between maximum yields of wheat and NDVI were explained by linear function. The maximum yields were chosen above the fitted line (yield points below were though to have been due to the crop management practices). The map of the attainable yield from relationship between actual yield and NDVI  (using boundary analysis) was generated. The SSM-iCrop2 model predicted higher compared to boundary-line analysis. 

The suggestion is that the actual yield could have been improved by two times, and author attempts in explaining the difference in dfferent yields based on the environmental reasons . Overall, the process summarizes with the NDVI and its relation was with actual yield was studied obtaining the equation using the boundary-line analysis (which estimated the attainable yield at the large scale). 

The author points the output of the simulated models to be discrete, plant parameters to be calibrated well, need of the meteorological data, requirements of the soil data, unreliability from unreliable inputs, and sometimes unrealistic yield estimates. The direst advantage of the satellite images is the possibility of the determination of the effect of the different factors and the detection of the area with the lower yield, and their causes. The use of the satelllite imagery based indices and the crop models can be valuable estimation tool for the agricultural systems.

In An Attempt to study and summarize:

Dehkordi, P.A., Nehbandani, A., Hassanpour-bourkheili, S. et al. Yield Gap Analysis Using Remote Sensing and Modelling Approaches: Wheat in the Northwest of Iran. Int. J. Plant Prod. 14, 443–452 (2020). https://doi.org/10.1007/s42106-020-00095-