Rabi Groundnut Area Estimation using Synthetic Aperture Radar (SAR) in Thiruvannamalai District of Tamil Nadu

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a Crop Acreage and Production Estimation (CAPE). The CAPE’s main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved. Methods: Multi-temporal Sentinel 1A SAR IW- GRD data with 20 m spatial resolution and 12 days temporal resolution of Vertical - Horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75.

An Innovative Approach to Automated Photo-Activation of Crop Acreage Using UAVs to Stimulate Crop Growth

Photoactivation of plants by laser treatment is a promising direction in the development of modern agricultural production. Treatment of plants with radiation with specified characteristics stimulates the development of plants, the formation of generative traits and an increase in yield. An approach based on the use of a specialized laser installation mounted on an unmanned aerial vehicle (UAV) is proposed to automate the process of photoactivation of large cultivated areas. It is possible to perform laser activation of large areas with minimal expenditure of time and human resources due to autonomous processing of the field with the help of UAVs. An algorithm for calculating a covering trajectory for covering large rectangular areas with a laser spot with given characteristics is proposed in the paper. A methodology for calculating the required power of the laser installation depending on the altitude and flight time of the UAV is presented. The advantage of the developed approach is its versatility, since this approach takes into account the characteristics of a laser installation and can be used with devices of various types. Depending on the laser parameters, the algorithm builds such a trajectory for the UAV so that the irradiation of plant seedlings is uniform throughout the entire processing process. Field experiments were conducted when the UAV moved along the calculated trajectory at a speed of 0.3 m/s and the average processing time for a field 200 m long and 1 m wide was 9 minutes. The results of field experiments show that laser irradiation on most of the studied crops increased the yield and height of the stand (in cereals - in four out of six crops, in legumes - in four out of five studied crops). The proposed algorithm for constructing a path for uniform laser irradiation of a site takes into account the area of the laser spot to ensure the required radiation characteristics when using any laser installation.

Do cost-share programs increase cover crop use? Empirical evidence from Iowa

Cover crops can generate both on-farm and water-quality benefits. However, their use in Iowa remains subdued, partly due to implementation costs faced by farmers. We tested the hypothesis that monetary incentives through cost-share programs are effective at increasing the area of farmland planted to cover crops in Iowa, as opposed to the alternative in which the participants of cost-share programs would have planted the same cover-crop acreage in the absence of payment. We found that cost-share payments induced a 15 percentage-point expansion in cover-crop acreage beyond what would have been planted in the absence of payment, among farmers who participated in cost-share programs. The estimated additionality rate was 54%, suggesting at least half of cost-share expenditures funded cover-crop acreage that would not have been planted without payment. Furthermore, we estimated the public cost to reduce nitrogen loads to Iowa waterways via cover crop, beyond what would have occurred in the absence of cost-share programs, to be $1.72–$4.70 lb−1 N ($3.79–$10.36 kg−1 N). Farmers absorbed about 70% of those costs as private losses, and cost-share payments offset the remaining 30%. Although the additionality rate estimated in this study is less than what has been found in other states, the cost-share programs in Iowa have been relatively cost-effective, due to their lower payment rate.

Wheat Crop Acreage Estimation Based on Remote Sensing and GIS in Jabalpur (Madhya Pradesh, India)

Mapping and classification crop by using satellite images is a challenging task that can minimize the complexities of field visits. The recently launched Sentinel-2 satellite has thirteen spectral bands, short revisit time and determination at three different resolutions (10 m, 20 m and 60 m), besides that, the free availability of the images makes it a good choice for vegetation mapping. This study aims to classify crop, using single date Sentinel-2 imagery within the Jabalpur, state of Madhya Pradesh, India. The classification was performed by using Unsupervised Classification. In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 were stacked for the classification. The results show that the area of wheat crop corresponds to 83.07%; Gram/ Pulses, 14.64%; and other crop, 2.28%. The overall accuracy and overall Kappa Statistics of the classification using Sentinel-2 imagery are 85.71% and 0.819%, respectively. Therefore, this study has found that Sentinel-2 presented great potential in the mapping of the agriculture areas of Jabalpur by remote sensing.

An Economic Analysis on the Transition to Dryland Production in Deficit-Irrigated Cropping Systems of the Texas High Plains

The objective of this analysis was to integrate hydrologic, agronomic, and economic methods to evaluate various management strategies by changing crop acreage to better manage the declining resources of the Ogallala aquifer. A non-linear optimization model was used to estimate the optimal water use, crop mix, crop yield, and net returns over a 50 year period under dryland and deficit irrigation scenarios in the Texas High Plains. Results indicated that growers could maintain profitability by switching from fully irrigated center pivots to irrigating ½ and ¼ pivots.

Multi-Temporal Synthetic Aperture Radar Data for Paddy Crop Area Estimation in Eastern part of Godavari Delta, Andhra Pradesh, India.

In the present study, an attempt is made to estimate the area under paddy crop during Rabi, 2013-14 using Microwave satellite data in the eastern part of Godavari delta. Clouds veil nearly the entire sky in both (Kharif & Rabi) seasons of Andhra Pradesh and hinder the estimation of crop acreage through optical satellite sensors. Microwaves can penetrate clouds and be used to detect crops during the day and night, regardless of cloud cover. Radar Imaging SATellite-1 (RISAT-1), microwave sensor, dual-polarization Horizontal-Horizontal (HH), Horizontal-Vertical (HV), Medium Resolution scanSAR Mode (MRS) data (18 m pixel spacing and 37° incidence angle) of three different dates (in December, January, and February) with 25 days interval was used. The backscatter (dB) values of the early, mid, and late-season transplanted stages of paddy crop were used to estimate the paddy crop acreage coupled with ground truth information during different stages of the crop. It was observed that the dB values at the transplanting stage rapidly increased with plant growth in the early season sown areas and mid-season sowed paddy illustrate a dip in dB values in the second date due to change in transplantation and increased backscatter coefficient values in the third date because of crop growth after transplantation. The backscatter signature value of late sowing paddy crop showed first and second dates with high backscatter due to previous crop/vegetation and then a sudden dip in the third date as submerged field ready for transplantation. The dB values of the above stages were used in decision-based classifier to estimate paddy crop acreage. The paddy area was compared at Mandal (sub-district level) estimates observed the significant coefficient of determination (R² = 0.89) between traditional estimates and Synthetic Aperture Radar (SAR) data assessment. This study robustly suggests the utilization of SAR data in agricultural crop monitoring during cloud cover.