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Tomato is a most economic vegetable in India. Covers major area in its cultivation during crop production, it is threatened by major viral disease i.e tomato leaf curl disease, caused by begomovirus and transmitted by the vector. It causes yield loss up to 90-100 per cent, application of insecticide for the control of vector causes regular outbreak. Identification of resistant source from wild species and their introgression with susceptible source, development of lines/varieties, resistant/tolerant to Tomato leaf curl disease is the safest and long term alternative for solving this problem. This paper covers the major aspect related to virus, virus causing factors, vectors and sources of disease resistance and also breeding tools employed for its resistance and some of the barriers in breeding programme. [ABSTRACT FROM AUTHOR]

In India agricultural and packaging technology practices are constantly evolving to enhance crop production by improving soil quality and creating suitable environments, respectively. Mulching and improved packaging films have emerged as popular methods, but the use of non-biodegradable polyethylene (PE) for both poses disposal challenges. To address this, the focus has shifted to the biodegradable polymer, polylactic acid (PLA), in combination with fruit peel waste powder as filler. This literature review paper was written to describe a detailed survey about PLA with the incorporation of increasing amounts of fruit peel waste to enhance the properties of the PLA film. The primary aim was to explore the potential of PLA mulch film with the incorporation of fruit peel waste as an environmentally friendly alternative for mulching application in agriculture. To date we know of no review article published regarding the incorporation of fruit peel waste as a filler within the PLA matrix to enhance the properties of the films used in agriculture and the packaging industry. Moreover, this review paper also describes recent trends in the application and methods of preparation of PLA mulch films. [ABSTRACT FROM AUTHOR]

Agricultural production in developing countries is heavily rainfall dependent. Any unexpected variation in rainfall can affect the welfare of households. Using unit record data from India, this paper shows that households can insure against agricultural productivity (rainfall) shocks. Evidence suggests that they do so by varying the time allocation of individual members to different activities, particularly to regular wage work and human capital accumulation. There is a gender‐differentiated aspect to this response. Rainfall shocks adversely affect women's human capital accumulation. While there is no evidence that households use participation in NREGS to insure against rainfall shocks, the availability of NREGS helps reduce the impact of rainfall shocks on human capital accumulation. [ABSTRACT FROM AUTHOR]

Poverty remains a major challenge for the countries of the global south. Decent employment opportunity, a crucial determinant of poverty reduction, is increasingly becoming elusive for those countries regardless of a decent rate of economic growth. There is a growing separation of economic growth and employment growth under the aegis of neo-liberal economic policies. Economic growth, instead of becoming inclusive, turns out to be “jobless” or “job loss.” Failing to reverse this trend, “Active Labour Market Policies” (ALMPs) in the form of “Public Works Programmes” (PWPs) have emerged as a major strategy to reduce unemployment and poverty. Secondly, rural–urban migration is very much relied upon for the same. Raising agricultural productivity, a crucial question of “agrarian transition,” well evident and verified across the globe, is, however, becoming oblivion in the fight against poverty. In this context, based on a primary survey of 300 households in the poorest regions of India, this paper endeavors to investigate the relative strength of MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act), rural–urban migration, and land productivity on poverty. The study suggests that an agrarian-centric intervention is more effective in dealing with poverty as compared to MGNREGA (ALMPs) and migration. [ABSTRACT FROM AUTHOR]

This article is furnished with a comprehensive review of nature and extent of rural livelihood diversification with special focus on North-Eastern region of India. This study reveals that the tribal communities of this region adopt livelihood diversification as a strategy of coping with risk due to persistent low agricultural productivity and population pressure. Despite this fact, the extent of diversification is low. Agriculture and land still occupies main source of income. It is evident from this article that this region has great potentialities of diversification towards multiple income sources. But difficult terrain and geographical isolation of this region make these resources yet to be properly utilised. The aim of this review article is to point out some issues related to livelihood diversification of North-Eastern region for further research and also try to attract the eyes of policy makers towards the potentialities of livelihood options available in this region. [ABSTRACT FROM AUTHOR]

In recent years concerns have been raised regarding the environmental consequences of over-use of nitrogen fertilizers on a global level. However, the balance between sustainability and agricultural productivity, a central concern for policy makers in developing countries, has not been sufficiently addressed. In this paper, I evaluate farmers' fertilization practices and their effect on yield using unique plot level data from India. I estimate quadratic crop response functions for different crops and cropping systems. To address endogenous input choices, I use input prices and cost shifters from the fertilizer industry as instrumental variables for the fertilization practice. I find that a large share of Indian cultivators overuse nitrogen relative to the other two nutrients, and could benefit from simply reducing the amount of nitrogen used while keeping the other nutrients fixed. This suggests a potential win-win situation where both productivity and sustainability can be improved by changing fertilizer application. The widespread "nitrogen-only" fertilization pattern is rejected as optimal in most cases. [EconLit Citations: Q12, Q15, Q16, E23, C26, C14]. [ABSTRACT FROM AUTHOR]

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. • Data Envelopment analysis was estimated to derive the paddy efficiency among the tribal villages of eastern India. • The DEA estimates show that average Technical efficiency in the study region is much below the national average. • Irrigated farmers are more efficient than the rain-fed farmers. Surface irrigation is the dominate factor. • Rain-fed paddy cultivators' farm efficiency would improve by 6.5%, if they had irrigated. • Semi-mountainous regions with little access to surface irrigation must explore micro-irrigation practices such as drips and sprinklers. [ABSTRACT FROM AUTHOR]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

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]

Purpose: This work examines the impact of institutional agricultural credit on crop productivity of some major crops such as paddy, cotton, wheat and pulses for small and marginal farmers across various social groups. Design/methodology/approach: The cross-sectional field data on socio economic variables was collected from three Indian states from about 400 small and marginal farmers across various social groups using multi-stage stratified random and purposive sampling through a structured questionnaire by interviewing. The method of propensity score matching (PSM) was employed to calculate average treatment effect (ATE) and average treatment effect on the treated (ATET) by categorising sample farmers as treatment group and control group where crop productivity was considered as outcome variable and access to institutional credit was considered as treatment variable. Findings: The PSM estimates reveal that ATE and ATET for all the selected crops are found to be significantly higher for the treated group vis-à-vis non-treated group suggesting that institutional agricultural credit has a statistically and significant positive impact on the crop productivity. Research limitations/implications: Similar study can be extended for more crops and across regions in India for a universal coverage. Originality/value: The agricultural credit policy of India has been to increase the access and availability of institutional farm credit. This has led to in general increase in the flow of formal farm credit to agricultural sector. However, the impact of institutional credit and crop productivity especially for small and marginal farmers across social groups is not well recognized in India using field data. Accordingly, this field data study contributes to the existing research by providing fresh evidence from field across social groups for both kharif and rabi crops using recent survey data from small and marginal farmers which has important policy implications. [ABSTRACT FROM AUTHOR]

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to disease. Deep Learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for enhancement. The objective is to give an open-source online application is developed using Flask to aid farmers in informed decision-making. Utilize Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost productivity. The various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and efficiency. The outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's economy. This approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. [ABSTRACT FROM AUTHOR]

Tomatoes, which are extensively grown in Indian agricultural fields, are among the commonly cultivated vegetable crops. The tropical climate in India provides favorable conditions for tomato cultivation. However, various factors, including climatic conditions and other elements, can adversely affect the normal growth of tomato plants. Plant diseases present a significant risk to crop production and can lead to economic losses, adding to the challenges posed by climate conditions and natural disasters. Unfortunately, traditional methods of detecting diseases in tomato crops have proven to be ineffective and time-consuming. This study primarily aims to accurately identify tomato plant leaf diseases that are found using image analysis. Various methods have been applied for extracting the features that have been used to improve the accuracy of classification. Alex Net, InceptionV3, and a CNN (Conv Net) algorithm have been utilized to classify many types of tomato plant diseases. When comparing the results, it is notable that the CNN (Conv Net) classifier outperforms the other two models. The findings demonstrate the practical applicability of the model in real-life scenarios. [ABSTRACT FROM AUTHOR]