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Want to know how Turmeric is growing in your area?

Advanced estimate of the crop yield is useful in various applications e.g. crop insurance, harvest planning, delivery estimates, etc. Advanced estimate of the crop yield can be obtained by performing the statistical analysis, however, it does not take into account the current ground realities and rely only on the historical data. The satellite remote sensing provides the ground information in near real time which can be assimilated in a crop yield prediction model to provide the improved crop yield forecast dynamically.

Though crop yield depends on many factors such as climate (e.g. maximum and minimum temperature, rainfall), nutrient, etc., relatively it depends more on soil moisture1. The Turmeric crop yield model was developed by using the satellite soil moisture and observed crop yield data. In the model, MODIS and SMOS satellite data was used for obtaining soil moisture. The observed yield data of Turmeric crop used to calibrate the model for Kharif season from 2003 to 2014 and was downloaded from data.gov.in portal. The observed yield data showed a significant temporal variability having range from 5692 to 9046 kg/ha.

Turmeric crop yield predication for Medak district

Turmeric crop yield predication for Medak district

The modelling was performed using copula, an advanced statistical tool to describe the dependence between variables in terms of univariate marginal distribution functions. The copula model was calibrated based on the satellite-estimated soil moisture and estimated crop yield (in kilogram/hectare). The simulated crop yield is shown in terms of expected yield along with the 50 % confidence limit (CL). The dynamic crop yield prediction model showed a significant temporal variability. As can be seen from above figure, at the end of cropping season, in Medak district, there were 50% chances of crop yield being between 5000 to 8700 kg/ha and the expected yield was 7432 kg/ha. The model can be used to monitor the growth of any crop in near real time and to predict the crop yield. If you want to know in detail how Turmeric is doing anywhere across the globe, you can write to us at contact@aapahinnovations.com.

  1. Baier, W. and Robertson, G. W. (1968). The performance of soil moisture estimates as compared with the direct use of climatological data for estimating crop yields. Agricultural Meteorology, 5(1):17–31 []

Did crop get enough water in Maharashtra this Kharif season?

The Kharif season is approaching its end and the availability of water to crops in Maharashtra using Satellite microwave remote sensing reasonably be quantified. The satellite estimated daily soil moisture is converted into monthly average relative soil moisture for June-September 2017 as shown in the figures.

During June, relative soil moisture varies from 0 to 0.8. Soil moisture is low in most part of the state except few patches in certain districts such as Ahmadnagar, Pune, Satara, Solapur, Osmanabad and Sangali. The low soil moisture in June had impacted the sowing activities adversaly1.
soil_moisture_mh_jun

In July, relative soil moisture was better than June in most of the states, however still below 0.5. Though the soil moisture improved in July, however, it was still not sufficient to eliminate the water stress in the crops and provide a conductive environment for the crops to grow.
soil_moisture_mh_jul

Subsequently, Soil moisture in August improved significantly that varied from 0.4 to 0.8, except some districts such as Amaravati, Aurangabad, Nashik and Pune division, having soil moisture below 0.3. This being the time for the grain filling activities of crops, the yield in these districts is expected to adversely impacted.

Soil moisture was found to be improved further in September, as most of the state has relative soil moisture around 0.8, providing a conducive environment during the ripening stage of the crops. However, still, some parts of Vidarbha and Pune regions had relative soil moisture less than 0.5. Vidarbha and Marathwada regions are the drought-prone regions, which received the deficient rainfall (23% as of 20th September) this season.2.
soil_moisture_mh_sep

To get more insight at fine scale, you can write to us at contact@aapahinnovations.com.

  1. http://www.deccanchronicle.com/nation/current-affairs/140616/imd-tells-farmers-to-postpone-sowing-as-monsoon-delayed-in-maharashtra.html []
  2. http://economictimes.indiatimes.com/news/politics-and-nation/late-monsoon-surge-to-shower-relief-on-states-at-drought-risk/articleshow/60775218.cms []

Mapping India’s farmland from space

In India, 72 % of the farmers hold less than 2 ha of land, out of which approximately two-third has less than 0.5 ha of land, based on the latest available agriculture census (see Figure 1). With more than a lakh farmers falling in this category, the regular maintenance of the farm records (farming type, type of crop, health of the crop, water stress, etc.) is a humongous task. While agriculture is influenced by factors varying from farm to farm and requires information tailored to the local scale1. Due to the lack of proper data, the unit of the various government schemes is not an individual farmer, but rather a larger administrative unit (e.g. a village, panchayat, taluka, tehsil, district, etc.).

Once such large scheme is crop insurance (Pradhan Mantri Fasal Bima Yojna(PMFBY)) which aims to provide protection to farmer against losses caused by crop failure and thereby ensuring stability in farm income2. Due to the tedious effort required in collecting the data at the farm scale, the crop insurance is implemented using the homogeneous area approach. However, the cause of crop failure varies from farm to farm and a significant variation is observed within a village.

A possible solution to collect the data at farm scale is the satellite remote sensing. The availability of higher resolution satellite in microwave bands which are capable of penetrating clouds makes it possible to collect the data at farm scale in all weather conditions. As an example, the active microwave (RADARSAT-2) estimated soil moisture for the farms in Chamarajnagar district of Karnataka, India is shown in the above figure. The satellite estimated soil moisture was validated using the 1262 data points spanning over 4 years in 50 farms3. During the validation, the mean Root Mean Squared Error was observed to be around 0.04–0.06 m3/m3 indicating the good retrieval of soil moisture from satellite data at the farm scale.

  1. http://www.aapahinnovations.com/psychology-low-penetration-crop-insurance/ []
  2. http://www.economicsdiscussion.net/india/crop-insurance/crop-insurance-need-advantages-and-nature-india/21606 []
  3. http://www.mdpi.com/2072-4292/7/6/8128/htm []

Weather Forecasting

The satellite soil moisture can provide the estimate of evaporation and transpiration. The information on the evaporation from the land surface and transpiration from plants can be used to enhance the short and medium term weather forecast.

The weather forecast would help us in preparing for the calamities like flood and in short-term irrigation planning.

Integrated Water Management Programme (IWMP)

The primary objective of IWMP is to address the issue of soil degradation & moisture conservation in the rain-fed areas of the state.

The satellite soil moisture can help in the assessment of the impact of the watershed management programme on agriculture. The availability of long term (before and after the project is executed) soil moisture data can help in evaluating the impact of a water resources project. The long-term monitoring minimizes the influence on extreme events on the assessment. Comparison of projects can be done to identify the management practices being more effective.

Estimates of evapotranspiration can be used to quantify crop water productivity—the amount of marketable crops (for example, kilograms of grain) produced per cubic meter of water consumed to grow that crop.