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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively. [ABSTRACT FROM AUTHOR]

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price. [ABSTRACT FROM AUTHOR]

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers' field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent. [ABSTRACT FROM AUTHOR]

Sodicity is one of the major salt stresses that impair crop production. Exopolysaccharide-producing sodic tolerant bacteria (EPS-STB) play a significant role in reducing the sodic stress in plants by hampering the uptake of sodium. In this context, this study aims to isolate the EPS-STB for alleviating sodic stress in rice under a sodic environment. Thus, artificial sodicity was created in culture media, and 253 bacteria were isolated from the rice rhizosphere of sodic soils in Trichy and Chinna Salem of Tamil Nadu in India. Fifty bacterial isolates were initially screened based on EPS production, sodic tolerant ability, and plant growth-promoting activities. Further, these bacterial isolates were identified using 16S rDNA sequencing. The results suggested that the isolated bacteria possessed biofilm-forming abilities along with plant growth-promoting activities and osmolyte accumulation under sodic stress conditions. Bacillus rugosus L1C7T, Bacillus paralicheniformis L1C5L, Pseudomonas sp. L5C14T and Franconibacter helveticus L2C1L2 were chosen as better EPS-STB plant growth-promoting bacteria, and their impact on rice under sodic conditions was evaluated. Among the sodic tolerant bacteria, Franconibacter helveticus L2C1L2-inoculated rice plants increased dry matter production compared to the control. Thus, this study showed that the utilization of EPS-STB will become a promising tool to alleviate sodic stress in rice. [ABSTRACT FROM AUTHOR]

The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems. [ABSTRACT FROM AUTHOR]

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique's effectiveness is confirmed by a fair comparison to existing procedures. [ABSTRACT FROM AUTHOR]

Pesticide treatment dramatically reduces crop loss and enhances agricultural productivity, promoting global food security and economic growth. However, owing to high accrual and persistent tendency, pesticides could create significant ecological consequences when used often. Lately, the perspective has transitioned to implementing biological material, environmentally sustainable, and economical strategies via bioremediation approaches to eradicate pesticides contaminations. Microalgae were regarded as a prominent option for the detoxification of such hazardous contaminants. Sustainable application and remediation strategies of pesticides pollutants in the agriculture system by microalgae from the past studies, and recent advancements were integrated into this review. Bibliometric strategies to enhance the research advancements in pesticide bioremediation by microalgae between 2010 and 2020 were implemented through critical comparative analysis of documents from Scopus and PubMed databases. As a result, this study identified a growing annual research trend from 1994 to 2020 (nScopus > nPubMed). Global production of pesticide remediation by microalgae demonstrated significant contributions from India (23.8%) and China (16.7%). The author's keyword clustering was visualized using bibliometric software (VOSviewer), which revealed the strongest network formed by "microalgae", "bioremediation", "biodegradation", "cyanobacteria", "wastewater", and "pesticide" as significant to the research topic. Hence, this bibliometric review will facilitate the future roadmap for many scholars and authors who were drawing attention to the burgeoning research on bioremediation of pesticides to counteract environmental impacts while maintaining food sustainability. [ABSTRACT FROM AUTHOR]

In the eastern part of India, rice as the most vital staple food crop supports as well the livelihood security of a vast population. Rice is mostly grown under conventional flooded culture without proper nutrient management. Crop performance, water productivity and economic profitability of rice cultivation need to be assessed under water-saving rice production methodologies with proper integrated plant nutrient management strategies using locally available low-cost nutrient sources. A field trial was conducted at the Adaptive Research Farm, Polba (58.57 m msl), Agriculture Department, West Bengal, India, during the rainy/wet seasons of 2014 and 2015 under aerobic culture, the system of rice intensification (SRI) and conventional flooded culture. The experiment was conducted to evaluate the influence of integrated plant nutrition and water-saving rice production methodologies on the crop performance and water productivity of rice and analyse the economic profitability of rice under different nutritional management and crop production methods such as aerobic culture, conventional flooded and SRI with an objective of sustainability in rice cultivation in the agroclimatic region. The results revealed that crop productivity significantly (p ≤ 0.05) varied from 4.68 t ha−1 (average yield recorded under aerobic culture) to 6.21 t ha−1 (average yield as achieved under SRI). Cultivation of rice under aerobic and conventional culture resulted in 24.6% and 20.9% yield reduction respectively as compared to SRI. Integrating 75% of the recommended dose of nitrogen (RDN) through chemicals with 25% RDN from vermicompost resulted in maximum crop productivity irrespective of crop culture. Aerobic rice culture registered maximum water economy in terms of both irrigation water productivity and total productivity. The study concludes that, for maximization of economic profitability, value cost ratio and partial factor productivity of nutrients the SRI method can be adopted along with integrated nutrient management (75% of RDN through chemicals with 25% RDN from vermicompost) in the lower Indo-Gangetic Plain Zone (IGPZ) of West Bengal, India. [ABSTRACT FROM AUTHOR]

The change in future climate will have a prominent impact on crop production and water requirement. Crop production is directly related to climatic variables. Temperature, solar radiation, wind, precipitation, CO2 concentration and other climatic variables dictate crop yield. This study, based on long-term historical data, investigates the patterns and changes in climatic variables (precipitation, temperature, and solar radiation) that would most significantly affect the future crop production in many parts of the world, and especially in India, where most farmers depend on rainfall for rice production. Statistical analyses—box and whisker plot, mean absolute error, Taylor diagram, double mass curve, Mann–Kendall trend test, and projected climate change—were used to assess the significance of the climatic factors for the purpose of agricultural modeling. Large variability in precipitation may cause the flash floods and affect the farming, and at the same time, increase in temperature from baseline period will lead to high water requirement by crops, and may cause drought if rainfall does not occur. Decrease in solar radiation will affect crop growth and development, and thus, would hamper the crop production. The results of this study would be useful in identifying the negative issues arising from climate change in future agricultural practices in Bihar, India. Furthermore, the results can also help in developing management strategies to combat the climate change impact on crop production. [ABSTRACT FROM AUTHOR]

Annual, seasonal and intra-seasonal variations in rainfall affect crop production from land preparation to the realization of potential crop yield in a region. Particularly, the onset of the rainy season is most crucial for determining the sowing period. Statistical analysis (Modified Mann-Kendall aka MMK-test for trend and likelihood ratio test for shifting pattern) of 60 years rainfall of south interior Karnataka (SIK) inferred the presence of temporal variability in rainfall. There was a monotonic increase in rainfall of February, March, April, June and August months (a positive sign of MM-K (tau) value), with a negligible rate of change (Sen's slope towards zero). Upon seasonal analysis, there was a significant increase in winter, pre-monsoon and monsoonal rainfall as compared to post-monsoonal rainfall (higher Sen's slope for pre-monsoon), indicating a need for agronomic interventions for estimating an effective date of sowing for reducing risks of crop production. Further, the agro-climatic onset of cropping season was estimated by considering soil–crop–water relations. Earlier onset of cropping season was estimated based on thresholds of soil–crop–water relations, which highlights sowing of crops in advance (May 1st fortnight) instead of late (June 1st fortnight) to avoid crop losses due to early-season drought. [ABSTRACT FROM AUTHOR]

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments. [ABSTRACT FROM AUTHOR]

In spite of the considerable potential contribution of neglected and underutilised crops to climate resilience, food security and nutrition; widespread adoption of these crops remains a challenge. Uptake is inhibited by poor economic performance due to low yields, compounded further by various social factors. Using farm survey data and aggregated time-series data from four states in southern India, this study examines factors influencing productivity in finger millet cultivation. A farm-level yield gap analysis is complemented by an analysis of total factor productivity (TFP) growth between 1999 and 2014 to better understand the role of research and innovation. Results suggest that there is considerable potential for improved growing practices to achieve better yields, but also education levels and technical support emerge as crucial factors for boosting finger millet productivity. The TFP analysis indicates a moderate level of growth, with a high variability and conflicting trends between states, suggesting a need to invest more in research and development, extension and infrastructure. Sustained productivity gains will require research efforts that respond to the needs expressed by farmers and that finger millet forms part of an overall strategy for sustainable intensification. [ABSTRACT FROM AUTHOR]