Wheat Yield Calculation in Saharsa (Bihar) Based on Remote Sensing

Introduction:

Yield calculation of the crop is very important for assessing the production and it depends on many variables like soil, weather, agricultural practices (date of planting, amount of irrigation and fertilizer use), seeds, pests, weeds, biotic stress plant varieties etc.

Remote sensing provides an effective and efficient way to forecast yield. Remote sensing has been widely used by many institutions across world to calculate area and yield of a crop.

In this paper, we focus on forecasting yield of wheat based on data from Sentinel-2 satellite data. The study area was wheat crop in Saharsa (Bihar). The crop is in advanced stage and has vegetated fully and harvest is expected in next 20-30 days.

Methodology:

Data from Sentinel-2 was used and random fields were selected from the mapped area and yield was calculated using NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index).

Farmonaut platform was used to map fields to create bounded regions (fields) as shown in Figure-1. On the dates of observations the sample fields were free from clouds and normal data was observed. Crop classification was done manually by ground truthing to ascertain the crop is standing in the field.

Figure-1 Comparison of peak NDVI in 2022 Vs 2022 wheat crop

Figure-2 Comparison of peak NDVI in 2023 Vs 2021 wheat crop

NDVI was used to calculate yields for the given fields which means peak vegetative growth rate. Peak vegetative growth helps to forecast yields more accurately.

Below are the observations of NDVI of the various fields. The maximum NDVI was reached on 11-Feb-2023 compared to 8-March-2022 and 21-Feb-2021. From the NDVI, LAI was calculated which was subsequently used to calculate yield. This approach is being used after going through a lot of available texts and final model was considered.

In this procedure the estimated LAI was used with WTGROWS model for yield mapping. This model suggests direct correlation between grain yields and LAI. This relationship was applied to all the fields to calculate yields.

NDVI started to rise after planting of crop in November continued to rise until 11-Feb-2023 which is 10 days before normal peak NDVI, making the inappropriate for calculating yield. So, the next NDVI read on 16-Feb-2023 was used to calculate yields. The NDVI started falling after 11-Feb-2023 and will fall until harvest. Best results of yield are obtained when the NDVI is maximum. However, if it reaches its peak NDVI before time and falls sharply then max NDVI is not the best barometer and the next 2 NDVI reads are very important.

NDVI and LAI showed the best correlation for the estimated yields. The yields ranged from 3.35-3.64 compared to 2.87 Tons/ha to 2.98 Tons/ha last year (2021-22) and 3.5 Tons/ha to 3.7 Tons/ha in (2020-21).

Figure-3

  Area (sq. m.) 01-Feb-23 11-Feb-23 16-Feb-23 21-Feb-23 Maximum LAI Estimated Yield Kg/ha
Field 1 3600 0.7 0.7 0.6 0.4 1.827376043 2981
Field 2 7700 0.67 0.7 0.6 0.4 1.827376043 2981
Field 3 1500 0.7 0.69 0.6 0.39 1.80621064 2963
Field 4 3100 0.7 0.7 0.6 0.39 1.80621064 2963
Field 5 6600 0.68 0.65 0.59 0.34 1.684268057 2853
Field 6 2700 0.66 0.67 0.6 0.4 1.827376043 2981
Field 7 2000 0.65 0.62 0.6 0.34 1.704004521 2871
Field 8 4300 0.57 0.67 0.6 0.4 1.827376043 2981
Field 9 1700 0.65 0.63 0.68 0.35 1.892372025 3036
Field 10 3700 0.71 0.7 0.6 0.4 1.827376043 2981

Figure-4

  Area (sq. m.) 26-Feb-22 03-Mar-22 08-Mar-22 13-Mar-22 Maximum LAI Estimated Yield Kg/ha
Field 1 3600 0.4 0.5 0.5 0.44 1.827376043 2981
Field 2 7700 0.4 0.5 0.5 0.43 1.827376043 2981
Field 3 1500 0.4 0.48 0.5 0.45 1.827376043 2981
Field 4 3100 0.4 0.49 0.5 0.43 1.827376043 2981
Field 5 6600 0.4 0.44 0.49 0.4 1.785290384 2944
Field 6 2700 0.39 0.43 0.47 0.4 1.704004521 2871
Field 7 2000 0.4 0.48 0.49 0.42 1.785290384 2944
Field 8 4300 0.4 0.44 0.48 0.41 1.744173984 2908
Field 9 1700 0.4 0.46 0.5 0.44 1.827376043 2981
Field 10 3700 0.4 0.49 0.5 0.48 1.827376043 2981

 

Figure-5

  Area (sq. m.) 01-Feb-23 11-Feb-23 16-Feb-23 21-Feb-23 Maximum LAI Estimated Yield Kg/ha
Field 1 3600 0.6 0.7 0.7 0.7 2.912119253 3713
Field 2 7700 0.6 0.7 0.69 0.67 2.912119253 3713
Field 3 1500 0.59 0.7 0.7 0.69 2.912119253 3713
Field 4 3100 0.6 0.7 0.7 0.69 2.912119253 3713
Field 5 6600 0.59 0.7 0.68 0.67 2.912119253 3713
Field 6 2700 0.57 0.7 0.68 0.67 2.912119253 3713
Field 7 2000 0.52 0.64 0.64 0.64 2.532181241 3493
Field 8 4300 0.59 0.7 0.7 0.7 2.912119253 3713
Field 9 1700 0.52 0.65 0.66 0.64 2.591873781 3530
Field 10 3700 0.6 0.7 0.7 0.69 2.912119253 3713

Figure-6

Observations and calculations:

The early Max NDVI occurred due to warm winter, dry conditions, less clouds and heat tolerant yield variety. The early NDVI reached and forecast of high temperatures will adversely impact yield of the crop. Generally peak NDVI reached by 20-28 Feb but this year it has reached 10 days before and the average temperature is much higher than normal temperature and forecasted temperature is almost 10 Degree Centigrade higher than normal year will adversely affect yield in maturity stage. Higher NDVI reached before due date is abnormal and due to that peak NDVI is not used here but the next reading on 16-Feb-2023 was used to calculate yields. Also, the fall in NDVI is much sharper than 2021-22 and 2020-21 which shows that there will be major fall in yields.

Also, the average max temperature in the crop season was above corresponding period last year and 2020-21 and humidity and cloud cover were significantly lower than 2021-22 leading to more sunlight and higher temperatures on the crop thereby affecting the quality of crop and lower yields. Lower humidity leads more sunlight in winter and if temperatures remain above average than it impacts yields. Also, there is forecast of temperatures reaching 38 Centigrade by March 5 will adversely impact crop when it is in maturity phase.

In the Figure 1, there is comparison of peak NDVI of 2022-23 wheat crop vs 2021-22 while crop showing that last year wheat crop was in worse condition than current year and the average yield was 2.96 Tonnes/Hectare in the above plot while this year the average yield in the above 10 fields are 3.37 Tonnes/Hectare which is approximately 14% above last year and 9% lower than 2020-21. The data was corroborated with discussion with Krishi Vigyan Kendra- Saharsa which said the peak crop yield will fall for late shown non heat resistant varieties and heat resistant varieties have low yields. Advisories have been given to irrigate fields and use micronutrients to mitigate fall in yields. Also, heat resistant varieties have yield in the range of 3.5-4.0 Tonnes/Hectare. Long Period Average yield of rice is 4.3-4.5 Tonnes/Hectare for normal varieties which are sown on time. The yield in 2022-23 is 3.37 Tonnes/Hectare was 2.96 Tonnes/Hectare last year which is approximately 25-30% lower than Long Period Average but 14% higher than last year. The crop will be ready to harvest by End-March-2023 compared to mid-April last year and start of April in 2020-21.

Results:

Data from our observation show that the average yield is 3.37 Tonnes/Hectare compared to 2.96 Tonnes/Hectare in the same fields last year. The yield ranges from 3.31 Tonnes/Hectare to 3.64 Tonnes/Hectare compared to 2.87 Tonnes/Hectare to 2.94 Tonnes/Hectare last year and 3.4 Tonnes/Hectare to 3.71 Tonnes/Hectare in 2020-21.

The above-mentioned forecasted yields can vary depending upon weather and other unavoidable factors before harvest. If normal condition prevail then the above yields can be achieved subject to error of 10%.

The researches done in the area of yield forecasting of field crops by remote sensing has demonstrated good results. With the help of new sensors and indexes, researchers can calculate yields with less errors in future.

 

Global economic slowdown will reduce exports and slow Indian economy, causing income and inflation issues. This will particularly impact farmers and the poor due to peak food inflation.

 

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By utilizing Farmonaut’s platform, farmers an access up-to-date information on vegetation water stress, evapotranspiration, soil organic carbon and weather forecasts, allowing them to make data-driven decisions on fertilization, pest management, irrigation needs and advisory. This proactive approach to crop management can potentially help mitigate the effects of climate change, reduce the risk of lower crop yields, and decrease inflationary impacts on food prices.

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Written By:

Dipanker Gyan

 

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