Search

Agro-based pulp and paper mills in India are one of the most polluting industries; in addition, they are high consumers of raw water. Growing scarcity of high quality freshwater as well as stringent regulatory standards is compelling these units to explore appropriate water management options. Based on data obtained through a questionnaire survey and plant visits, this work provides an overview of the water use and effluent treatment status in Indian agro-residue and recycled pulp and paper mills. The challenges faced by this sector are reviewed and practices adopted by progressive units to minimize freshwater use are illustrated through case studies.

Agricultural transformation in Punjab, spurred by the Green Revolution of the mid-1960s, has led to significant shifts in land use patterns, crop diversification, and cropping dynamics. This transformation has profound implications for sustainability and economic resilience. This study delves into the complex relationship among agricultural growth, instability, and environmental sustainability in Punjab. It examines the growth and instability in the state's key crops' area, production, and yield/productivity over 56 years, from 1966-1967 to 2021-2022. Using secondary data, this study employs an assortment of methodologies, including compound annual growth rate, simple linear regression, diversity indices such as the Herfindahl index and Simpson diversity index, instability analysis, and decomposition analysis. The results indicate an increase in cropping intensity, indicating agricultural intensification, with a shift towards wheat and rice monoculture from once diverse cropping patterns. The instability analysis reveals that wheat and rice emerge as the most stable crops in their area, production, and yield, while others exhibit instability, raising concerns about diminishing crop diversity and its implications for Punjab's natural resources. The study emphasizes the urgent necessity for sustainable agricultural practices and policies to counteract monoculture's adverse effects and ensure long-term agricultural resilience., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The study attempted to evaluate the agricultural soil quality using the Soil Quality Index (SQI) model in two Community Development Blocks, Ausgram-II and Memari-II of Purba Bardhaman District. Total 104 soil samples were collected (0-20 cm depth) from each Block to analyse 13 parameters (bulk density, soil porosity, soil aggregate stability, water holding capacity, infiltration rate, available nitrogen, available phosphorous, available potassium, soil pH, soil organic carbon, electrical conductivity, soil respiration and microbial biomass carbon) in this study. The Integrated Quality Index (IQI) was applied using the weighted additive approach and non-linear scoring technique to retain the Minimum Data Set (MDS). Principal Component Analysis (PCA) identified that SAS, BD, available K, pH, available N, and available P were the key contributing parameters to SQI in Ausgram-II. In contrast, WHC, SR, available N, pH, and SAS contributed the most to SQI in Memari-II. Results revealed that Ausgram-II (0.97) is notably higher SQI than Memari-II (0.69). In Ausgram-II, 99.72% of agricultural lands showed very high SQI (Grade I), whereas, in Memari-II, 49.95% of lands exhibited a moderate SQI (Grade III) and 49.90% showed a high SQI (Grade II). Sustainable Yield Index (SYI), Sensitivity Index (SI) and Efficiency Ratio (ER) were used to validate the SQIs. A positive correlation was observed between SQI and paddy ( R 2  = 0.82 & 0.72) and potato yield (R 2  = 0.71 & 0.78) in Ausgram-II and Memari-II Block, respectively. This study could evaluate the agricultural soil quality and provide insights for decision-making in fertiliser management practices to promote agricultural sustainability., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Actinomycetes are a suitable microbial group for the synthesis of lignocellulose-degrading enzymes. Enzymes that may degrade organic material, including cellulose, hemicellulose, and lignin, are released by actinomycetes. The aim of this research was to isolate actinomycetes from Rajkot, Gujarat, India’s soil and evaluate the activity of their cellulase and Xylanase enzymes. Starch Casein Agar (SCA) was used to identify a total of 30 isolates of actinomycetes. A qualitative plate assay (CMC-Na, Congo red) revealed that the highest zone of catalysis for MMD1 was 36 mm. Five strains were discovered to be effective for quantitative quantification of endoglucanase utilising filter paper and CMC as substrates: MMD1, MMD2, MMD3, MMD4, and MMD8. Following MMD 1 (endoglucanase 5.4 IU; FPase 4.4 IU), MMD 2 (endoglucanase 4.5 IU; FPase 3.4 IU) has demonstrated considerable endoglucanase and FPase activity. Beechwood xylan was used to treat sugarcane bagasse in order to test Xylanase, and 45% of the xylan (hemicellulose) fraction was obtained. MMD1 and MMD2 measured the xylanase enzyme activity (4.8IU and 4.2IU) in quantitative and qualitative assays (34 mm and 22 mm for BWX and 32 mm and 14 mm for agro-waste xylan). The strain MMD1 was identified as Streptomyces chartreusis through morphological, biochemical, and finally molecular characterization by 16S rRNA sequencing. It was then submitted to NCBI GenBank with the accession number MT254830. [ABSTRACT FROM AUTHOR]

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R 2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Climate-induced extreme events with fluctuations in climatic indicators like temperature and precipitation highly influence crop productivity. This study deals with quantitative analysis of climatic variability and crop production (1990-2018) using panel data regression analysis. The focus is on variability of three crops, i.e., paddy, maize, and wheat in the Rangit river basin of Sikkim Himalaya, India. Meterological data were acquired from the Indian Meteorological Department, agricultural data from the state agricultural department and a field survey were also conducted with the farmers, using a structured questionnaire, focused group discussion, and key informant observations. The acquired data was analyzed with the help of correlation and multiple linear regression analysis to analyze the relationship between climatic variability and crop production. The result of the study shows that all three crops are dependent on rainfall; however, paddy was the most sensitive to climatic variability. It was found that the overall grain production had an inverse relation to temperature, but it had a positive correlation with rainfall. It was observed that there was a continuous decline in the overall production of paddy and wheat. During deficiency years, it was found that when - 11.33% rainfall was deficient, paddy yield declined by - 1.52%. Further, a deviation of - 13.48% led to a decline of - 54.78% in wheat. The study advocates that timely policy interventions and strategies shall reduce climatic shocks and improve productivity. This would strengthen the livelihood security of the local communities, overcome the challenges of food security, and ensure long-term sustainability of Rangit River basin., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The unpredictability of the climate has drawn a lot of interest worldwide, especially that of the annual mean temperatures and rainfall. In this study, non-parametric tests such as the LOWESS curve method, Mann-Kendall (MK), SNHT test, Pettitt's test (PT), and Buishand range test (BRT) were used to evaluate long-term (2000-2020) rainfall data series to examine rainfall variability. The Dakshina Kannada district has the highest average rainfall is 3495.6 mm with a magnitude change% of about 26.2, while the Koppala district has the lowest average rainfall roughly about 530.4 mm, with a magnitude change % of about 11.49 mm in a year. The statistics from the fitted prediction line were utilized to determine the maximum coefficient determination (R 2  = 0.8808) in the Uttara Kannada region. Because of the commencement of the present rising era, 2015 is the shift year in rainfall with the highest potential of being a change point in the state's Western Ghats region. It was also revealed that the majority of the districts exhibit positive trends before the change point and vice versa. The current research can be used to plan for and minimize the agricultural and water resource challenges in the state of Karnataka. To link observable patterns to climate variability, the next inquiry must identify the source of these changes. Overall, the study's findings will help organize and improve drought, flood, and water resource management techniques in the state., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Demand planning plays a very strategic role in improving the performance of every business, as the planning for a whole lot of other activities depends on the accuracy and validity of this exercise. The field of agriculture is not an exception; demand forecasting plays an important role in this area also, where a farmer can plan for the crop production according to the demand in future. Hence, a system which could forecasts the demand for day-to-day food harvests and assists the farmers in planning the crop production accordingly may lead to beneficial farming business. This paper would experiment by forecasting the demand using multiple linear regression (EMLR-DF) for different food commodities and implements the model to assists the farmers in demand based constructive farming. Implementation results have proved the effectiveness of the proposed system in educating the farmers in producing the yields mapping to the demand. Implementation and comparison results have proved the proposed EMLR-DF is more effective and accurate.

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R 2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as 'Scintillometry' which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape. [ABSTRACT FROM AUTHOR]

We investigated methane (CH 4 ) and nitrous oxide (N 2 O), two important greenhouse gases (GHGs) emissions using the closed chamber method from a flooded rice field in Brahmaputra valley of Assam, northeast part of India. We tried to understand the factors responsible for the emission and identify appropriate agro-technologies for their mitigation. Various factors like water level, drainage management, soil organic carbon management, crop management, fertilizer amendment, cultivar type etc. affect the GHG production and emission from the flooded rice soil. In this study, six treatments were employed, namely, farmer's practice (FP), recommended fertilizer dosage (RDF), direct seeded rice (DSR), intermittent wetting drying (IWD), use of efficient methanotrophs (MTH), and use of ammonium sulfate as a nitrogen source for real-time nitrogen management using leaf color chart, (AS). GHG flux was measured through the static closed chamber technique. Soil temperature, pH, and redox potential (Eh) and other soil physico-chemical and biological properties that have a potential role in GHG emission were also assessed. The lowest CH 4 flux was observed in IWD treatment. The highest CH 4 but lowest N 2 O flux was observed in RDF thus portraying a tradeoff relationship among these two GHGs. The highest N 2 O flux was observed in AS. Changes in Eh strongly altered CH 4 and N 2 O emissions. The CH 4 flux for the growing season varied from 62.5 to 86.3 kg ha -1 with an average of 72.4 kg ha -1 . The average N 2 O flux was 0.89 kg ha -1 with values fluctuating between 0.72 - and 1.08 kg ha -1 . The findings of this study could assist in understanding the factors affecting the source, production, and sink of these two important GHGs. IWD, along with judicious N-based fertilizer use, could provide significant respite from GHG emissions in rice-based agriculture. These climate-smart strategies not only reduce emissions but also have the potential to improve yield., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crop residue management has become more challenging with intensive agricultural operations. Zero tillage and crop residue returns, along with the enhancement of in-situ residue decomposition through microbial intervention, are essential measures for preserving and enhancing soil quality. To address this problem in view of stubble burning, field experiments were conducted in rice-rice (variety Swarna) cropping systems under lowland conditions, wherein the following different residue management practices were adopted viz., conventional cultivation (CC), residue incorporation (RI @ 6 t paddy straw ha -1 ), residue retention (RR @6 t paddy straw ha -1 ), and zero tillage (ZT). In this experiment, two microbial products i.e. solid microbial consortium (SMC) at 2.0 kg ha -1 ) and capsule (10 numbers ha -1 ), were evaluated in both Rabi (dry) and Kharif (wet) seasons under different residue management practices. The results on soil microbial properties showed that application of either SMC or capsule based formulation could significantly improve the soil organic carbon (SOC) content in ZT (9.51 g/kg), followed by RI (9.36 g/kg), and RR (9.34 g/kg) as compared to CC (7.61 g/kg). There were significant differences in the soil functional properties (AcP, AkP, FDA, and DHA) with microbial interventions across all residue management practices. SOC was significantly positive correlated with cellulase (R 2  = 0.64, p < 0.001), β-glucosidase (R 2  = 0.61, p < 0.001), and laccase (R 2  = 0.66, p < 0.001) activity; however, the regression coefficients varied significantly with microbial intervention. Moreover, the availability of N, P, and K in soil was significantly (p < 0.05) improved under microbial treatments with either RR or RI practices. Among the different methods of residues management practices, RI with microbial intervention registered a consistent yield improvement (8.4-17.8%) compared to conventional practices with microbial intervention. The present findings prove that the application of decomposing microbial consortia for in-situ rice residue management under field conditions significantly enhances soil quality and crop yield compared to conventional practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Extensive global dependency on rice and wheat crops has necessitated the adoption of intensive cultivation practices, thereby compelling to closely monitor the potential yield-limiting factors, among which, boron (B) deficiency stands out to be a prime concern. The present study explores the effects of B fertilization strategies within the Rice-Wheat Cropping System (RWCS) in the Tarai region of North-West India. A comprehensive six-year field experiment was conducted (2013-2019) at G.B. Pant University of Agriculture and Technology, Uttarakhand, India. The experiment tested graded B doses (0.5, 1.0, 1.5, and 2.0 kg ha -1 ) at varied frequencies (single, alternate, and annual) in a factorial design. The study revealed significant impacts of alternate B application at 1.5 kg ha -1 on crop yields and the Sustainable Yield Index (SYI). The System Rice Equivalent Yield (SREY) exhibited an increase of 6.7% with B supplementation over B-deprived plots, highlighting the pivotal role of B fertilizer in enhancing productivity within the RWCS. The economic optimum B dose was found to be 1.422 kg ha -1 using a linear plus plateau model, resulting in a calculated annual SREY of 9.73 t ha -1 when applied alternately to the cropping system. Continuous application and higher B rates demonstrated substantial increases in various B fractions, while the mobility factor remained within 10%, depicting safe ecological limits. The distribution of fractions in B-treated plots on average followed the order: residual B > organically-bound B > oxide bound B > specifically adsorbed B > readily soluble B. Similarities in the distribution patterns of B fractions between B-treated plots and the control indicated potential influence of biotic or abiotic processes on B fraction dynamics, even in the absence of external B application. To sum up, B application in alternate years at 1.5 kg ha -1 was most sustainable in enhancing the SREY, SYI, available soil B, and B fractions and lowering the environmental hazards., Competing Interests: Declaration of competing interest The author(s) declared no potential conflict of interest that could have appeared to influence the work reported in the present paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Given the dominant share of India in global production of fruits and vegetables, this paper intends to analyze the export competitiveness of India and other major food exporters in the world trade. The purpose of this study is to examine export structure, substitutability and complementarity of selected fresh and processed fruits and vegetables of top ten food exporters for the period 2010-20. Design/methodology/approach: Balassa's (1965) revealed comparative advantage (RCA) index was used to measure RCA indices of selected fruits and vegetables under study. Also, revealed symmetric comparative advantage (RSCA) and normalized RCA (NRCA) indices have been calculated. Further, Spearman rank correlation coefficients were computed to analyze changes over the study period for India and other competing countries. The export data have been sourced from UN Comtrade, an electronic database of United Nations, as well as World Trade Statistical Review, a database of World Trade Organization. The analysis was undertaken at Harmonized System (HS) four-digit classification for the period 2010-20. Findings: The results disclosed an improvement in India's comparative advantage over the period of 2010-20 in HS 07 product category, whereas the advantage ceded to other competitive nations in HS 08 product category. Further, Spearman rank correlation coefficients revealed that India faces competition from countries like China, Indonesia, Brazil, Thailand, Argentina and European Union for HS 07 product category, while countries like Mexico, Indonesia, Brazil and Thailandare the major competitors of India in HS 08 product category. Originality/value: The paper expands the existing agricultural trade literature in three ways. First, it is one of the very few studies that have analyzed RCA for Indian fresh and processed fruits and vegetables using three different types of indices, namely, Balassa's RCA, RSCA and NRCA. Second, the authors provide a number of comparisons related to RCA for Indian fruits and vegetables with other top food exporters in the world for a period of 10 years (2010-20). Third, the authors contribute to agricultural trade literature by assessing the substitutability or complementarity of India in the export of fruits and vegetables with other competing nations by using Spearman rank correlation coefficients. [ABSTRACT FROM AUTHOR]

Global climate change has numerous implications for members of mountain communities who feel the impacts in both physical and social dimensions. In the Western Himalayas of India, a majority of residents maintain a livelihood strategy that includes a combination of subsistence or small-scale agriculture, seasonal pastoral migration, male out-migration, and localized natural resource extraction. Particularly under conditions of heavy male outmigration, but throughout the region, mountain women play a key role in providing labor and knowledge related to the management of local natural resources, yet often lack authority in related political and economic decision-making processes. This gap has important implications for addressing the impacts of climate change: while warming temperatures, irregular patterns of precipitation and snowmelt, and changing biological systems present challenges to the viability of these traditional livelihood portfolios throughout the region, mountain women increasingly face new challenges in their roles as household managers that have not adequately been emphasized in larger scale planning for climate change adaptation and mitigation. These challenges are complex in nature, and are shaped not only by gender issues but also interacting factors such as class, caste, ethnicity, and age (among others). In this paper, we review the main arguments behind the discursive gender/climate change nexus, discuss the implications for gendered vulnerabilities and transformation of adaptive capacities in the region, and suggest ways that researchers and policymakers seeking to promote "climate justice" can benefit from the incorporation of gender-based perspectives and frameworks. [ABSTRACT FROM AUTHOR]

The two main avenues via which agricultural produce is gathered, distributed, and resold in rural India are the regulated markets set up by the State Governments and the sporadic marketplaces that have developed naturally and are unregulated. Under the auspices of extensive regulations that have been formulated and amended periodically by the corresponding state Agricultural Produce Marketing Regulation Act, agricultural produce market committees have been established to promote equitable trade. Since their inception, regulated markets have assisted in reducing market disadvantages; yet, the competitive market structure was not well-foreseen by their legal framework. In an environment of liberalized trade, this paves the way for the market committee to become more manageable and sustainable. Given the dependence of other sectors on agriculture, India has viewed it as a valuable tool for economic progress. The regulated markets are regarded as accountable establishments that carry out all the tasks associated with the selling of products while considering the interests of all final consumers as a whole. The current paper highlights Indian agriculture, with a particular emphasis on the regulated agricultural marketing system and its development, status, and prospects. The report also emphasizes the difficulties and impact of a regulated market on farmers' socioeconomic standing. At the study's conclusion, a number of restrictions that either directly or indirectly impacted the farmers' socioeconomic standing were mentioned. To comprehend the link between the dependent and independent variables, a variety of statistical methods were employed. To arrive at the ultimate outcome, an ex post facto research design was mostly employed. [ABSTRACT FROM AUTHOR]

The sole purpose of this review paper is to reveal the land use and land cover (LULC) changes in the study region (Northern India) from 2017 to 2021. The researchers discovered that no systematic review of the LULC dynamics of the study region had been conducted so far. This study used a novel systematic review procedure (i.e., the ICA framework) to conduct the review process. The data for this study were retrieved from the Web of Science of Clarivate Analytics, using keywords related to LULC. A thorough review of 29 full-text articles was done to examine the primary land use classes (LUC), techniques used, factors responsible, and dynamics of the major land classes. Most articles included agriculture, built-up, water bodies, and plantations as the main LULC categories. Most of them used on-screen digitisation and supervised algorithms to classify LULC classes and incorporated accuracy assessment to validate their results. It was also found that almost all the papers cited climate change, increasing population, urbanisation, and economic considerations as the main drivers responsible for the changes that occurred in different LULCs. The findings of the various studies revealed that horticulture and built-up areas have increased while agricultural, forest cover and open spaces have decreased to a greater extent. Environmental negatives of LULC dynamics of the region suggested that LULC change is not favourable everywhere. Thus, to control the haphazard LULC change, the government must come up with a substantial land conversion policy in the region with ground-level implantation that will restrict the people from blind land conversion. [ABSTRACT FROM AUTHOR]

Agriculture in India is unique, in terms of topography climate and size of landholdings being very small. Agriculture has been in focus for both positive and negative reasons. Despite liberalization to a more marketoriented economy in 1991, the agriculture sector continues to have policy distortions. The issues related to agriculture are complex in nature which require a balance between the market and the state. This needs addressing major long-term challenges from food security to natural resource sustainability, especially soil and water. The objective of the paper is to highlight some of the problems of Indian agriculture and policy response of the government. It also traces the trajectory of the farmers' agitation against the farm laws. After the repeal of farm laws, the question of reform in agriculture is paramount. The paper proposes a road map for agri-reforms. It is argued that more unified framework of analysis is needed for an effective agricultural policy. This paper may be helpful in the field of public policy particularly relating to agriculture. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the agriculture trade dynamic between India and China from 2002-2021. It employed the Revealed Comparative Advantage (RCA) and Revealed Symmetric Comparative Advantage (RSCA) indices and various statistical analyses to assess the trade performance and provide an enhanced comprehension of the specialization pattern. The study has selected 45 agricultural commodities, categorized into seven sections according to the HS Nomenclature 2017. The findings revealed that India boasts a more diversified export portfolio compared to China, with a more significant number of agriculture chapters demonstrating a comparative advantage. Conversely, China's export basket is more concentrated, featuring fewer chapters with a comparative advantage but higher values of RCA and RSCA. The paper also identified the prospective areas for agriculture-trade cooperation and collaboration between the two countries and put forward recommendations. This research aims to offer valuable insights for policymakers, researchers, and traders to enhance bilateral ties and mutual benefits in the agriculture sector., Competing Interests: The authors have delcared that no competing interest exist., (Copyright: © 2023 Shukla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Second-generation bioenergy, a carbon neutral or negative renewable resource, is crucial to achieving India's net-zero emission targets. Crop residues are being targeted as a bioenergy resource as they are otherwise burned on-field, leading to significant pollutant emissions. But estimating their bioenergy potential is problematic because of broad assumptions about their surplus fractions. Here, we use comprehensive surveys and multivariate regression models to estimate the bioenergy potential of surplus crop residues in India. These are with high sub-national and crop disaggregation that can facilitate the development of efficient supply chain mechanisms for its widespread usage. The estimated potential for 2019 of 1313 PJ can increase the present bioenergy installed capacity by 82% but is likely insufficient alone to meet India's bioenergy targets. The shortage of crop residue for bioenergy, combined with the sustainability concerns raised by previous studies, imply a need to reassess the strategy for the use of this resource., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Asia contains 58 % of the global population and approximately 39 % of the world's cropland, making evaluation of the spatiotemporal variability patterns in cropland fire critical for understanding the interplay between crop residue burning and human activity in Asia. Although agricultural expansion and intensification have contributed to an overall decline in vegetation fires worldwide since the late 1990s, burned area by cropland fires in Asia has expanded by more than 19 % over the past two decades. India accounts for about 32 % of cropland fires in Asia, and the burned area has increased by more than two-thirds since 2003, particularly increased around 80 % during the two major cereal harvest seasons of March-May and October-November. In comparison, cropland fires have increased by <6 % in China since 2003, and there has been a marked downward trend in burned area in June due to the intensive implementation of the nationwide bans on open-land crop straw burning. The expansion of agricultural harvests is primarily responsible for the rapid increase in cropland fires in Asia, notably in India, where agricultural intensification is occurring with population growth and economic development in recent years, and crop straw burning should be strictly controlled in the future., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

The future of agriculture may be fully realized using knowledge of artificial intelligence (AI) accumulated by human expertise. With increasing climate change and population pressure challenges, new technologies, such as AI and RS in vital sectors like agriculture is urgently required to assess agricultural suitability, optimize productivity, and then support sustainable development. This paper presents bibliometric and review analyses on AI and geospatial analysis applied to the agricultural field. Based on the prestigious database, Scopus, thousands of documents were retrieved in the period 1992–2021 and processed using Scopus online tool and VOSviewer software. The results show a growing trend in the number of publications during this period. Earth and Planetary Sciences is the top domain, remote sensing (RS) is the leading source, while the United States and India are the most influential countries. RS, machine learning (ML), GIS, land-use, decision-making, decision trees, and satellite imagery are the most occurring topics in this search. Otherwise, research in this field is turning toward ML and deep learning techniques, whereas agricultural robots and antennas are the top trending tools. The current study provides specific information about the connections between artificial intelligence, geospatial analysis, and agriculture in the period 1992–2021, the fruitful period where the AI coupled to geospatial technologies is in full development. [ABSTRACT FROM AUTHOR]

Copyright of Oilseeds & Fats, Crops & Lipids (OCL) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Documenta Praehistorica is the property of Documenta Praehistorica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined, uniformly scaled, regular image patterns of leaf disease, is maintained. The research suggests that farmers utilising the model can predict the breakout of leaf disease predominantly acquiring 100% yield. [ABSTRACT FROM AUTHOR]

About 70% of India's economy is involved in the agriculture sector to live their lives and contributed to the GDP of the country. The Crop yield information along with the environmental change estimate will be useful for the agriculturalist to decide on price policies prior to harvesting the food source. It establishes a requirement for the prediction model, which precisely determines the harvest conditions, crop varieties, and agricultural yield. In literature, numerous crop prediction methods were devised to estimate crop production in the agricultural field & each technique has its potential in terms of yield forecasting. This review article provides a detailed analysis of the utilized approaches in the literature for the prediction of crop production as well as a discussion on the identification of concerns related to the yield gaps of crops. The discussed approaches were classified based on the application of different strategies, such as Machine learning methods, Deep learning methods, Data mining techniques, vegetative indices, fuzzy logic, and hybrid methods. The study was analyzed based on performance metrics, year of publication, datasets employed, software used for experimentation, and performance attained using various methods and highlights the research gaps of the respective method along with the future direction. [ABSTRACT FROM AUTHOR]

This paper examines the role of crop diversification in agricultural transition towards high-value crops as well as farm income growth in Odisha, Eastern India. The empirical analysis reveals some crucial facts: first, a stagnant and relatively low level of crop diversification in Odisha agriculture. Second, there is an insignificant agricultural transition due to the negative area substitution effect for most of the crops, along with a weak expansion effect over the last two decades from 1995-2018. Third, a declining contribution of productivity growth coupled with an insignificant contribution of crop diversification to the farm income growth over the years. Hence, we conclude no or an insignificant agricultural transition from traditional to high-value modern agriculture in Eastern India, causing unsustainable farm income growth. This calls for an urgent need to promote a mixed cropping pattern and colossal investment to encourage the farmers to transition towards high-value crops, stimulating farm income growth. Furthermore, massive efforts are required to make farmers aware of the advantages of diversification. [ABSTRACT FROM AUTHOR]

Green and decent work in the Global South is inextricably linked to sustaining rural livelihoods especially in agriculture that has undergone significant deskilling under the top-down, technocentric assemblages of the Green Revolution. Additionally, agrarian communities are also seeing youth quitting farming occupations in search of better livelihood options. Scholarly attention to green transitions though has been largely limited to the ecological dimensions. Enacting futures with a focus on ecologically responsible livelihoods need to go beyond existing narratives of technocentric and economic change and foreground the diverse micro institutional innovations that offer newer framings of reskilling. The growing evidence of agroecological initiatives across India indicates less discussed stories of transformation and innovations. Recognising the processes and linkages that allow for, and hinder, transformations at multiple scales and organisational levels is crucial for designing transformative initiatives and policies. Using two illustrative case studies, this paper explores opportunities for green work and the newer skills that might be required to enable sustainable agri-food systems. The case of Natural Farming Fellows (NFFs), a unique programme to encourage young agri-graduates to pursue Natural Farming is presented to understand enabling processes at the grassroots level. The second study explores institutional initiatives to engage rural youth through discussing the pedagogy and curricular approach of a Gandhian university along with opportunities to intern with field organisations. Together, these cases illustrate possible pathways and complexities underlying the process of nurturing sustainable livelihoods, the conception of which needs a broader idea of skilling based on personal aspirations and institutional support. [ABSTRACT FROM AUTHOR]

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing "crop management", "soil management", and "water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. [ABSTRACT FROM AUTHOR]

The fund allocation in agricultural sector in India is heavily tilted toward input subsidies provision; however, researchers seem to favor investment expenditure instead. The present paper seeks to compare the usefulness of input subsidies as compared to investment with regard to agricultural productivity so that policy makers hit the right tool and avoid less productive state expenditure. We investigated a total of four regression models using autoregressive and distributed lag cointegration in a time series framework covering period from 1983 to 2019. The first model considers all input subsidies in aggregate form, and the rest three models take input subsidies in disaggregate forms, namely fertilizer subsidy, irrigation subsidy and power subsidy, respectively. It is observed from the results that input subsidies still contribute more than what public investment does to agricultural productivity. It is also found that power subsidy is the most effective component of input subsidies followed by fertilizer subsidy. Hence, government expenditure on input subsidies is justified on the ground that it ensures all farmers to have access to affordable agricultural inputs. Targeted subsidies combined with adequate investment in agricultural infrastructure could deliver long-term agricultural development in India. [ABSTRACT FROM AUTHOR]

-Enhancing the productivity of rainfed crops, especially rice, while coping with climate adversities and saving critical natural resources is essential for ensuring the food and nutrition security of a growing population. With this context, the present study was undertaken to validate promising farm innovation and adaptation practices used by small-medium landholding farmers for rice cultivation in eastern Uttar Pradesh (UP), north India, as well as to examine the sustainability of innovative practices for large-scale adoption. For this, a 3-year study comprising extensive field surveys and experiments was undertaken to compare single transplantation (ST) and double transplantation (DT) in rice along with organic addition (farm-yard manure, FYM) on crop growth, yield, climate resilience, soil quality, and overall sustainability i.e., social (women involvements and labour productivity), environmental (water productivity and nutrient use efficiency), and economic (benefit:cost ratio) dimensions of sustainability. Field experiments were conducted in triplicate using two local rice varieties (MotiNP-360 and Sampurna Kaveri) in two agroclimatic zones, namely the middle Gangetic plains and the Vindhyan zone, in the Mirzapur district of eastern Uttar Pradesh. The DT practices of rice with and without farm yard manure (FYM) (replacing at a dose of 25% NPK) were evaluated over conventional methods of rice cultivation (i.e., ST, as control) and analysis was done periodically. The DT practice improved growth (p < 0.05), percent fertile tiller and grain (p < 0.05), and rice yield (15-20% higher than ST), while also improving soil quality, yield indices, water and labour productivity, and the benefit-cost ratio. The DT practice also resulted in early maturity (10-15 days earlier than ST), created more labour days for women, decreased lodging and pest/disease incidence, as well as a subsequent reduction in the use of synthetic chemical pesticides and associated environmental costs. Importantly, the residual effects of FYM application significantly improved (p < 0.05) the grain yield in subsequent years of cropping. Optimizing DT cultivation practices, preferably with FYM input for various agro-climatic regions, is essential for large-scale sustainable rice production under changing climatic conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Given the structure and functioning of agricultural markets and the policies and institutional space, smallholder farmers in India often face the risks of strategic failures, which force them to either opt for distress selling, operate as fringe retailers or participate in the existing supply or value chains on given conditions. In this perspective, this paper examines if smallholder farmers' collectives can potentially strengthen their market position vis‐à‐vis other individual players and enable them to explore strategic options in a greater way. On the basis of the structure–conduct–performance theory and field experiences from two selected villages of West Bengal (India), the paper finds that the collectivization of smallholder farmers helps in exploring a larger set of strategic options for better outcomes. Further, the choice of the strategy set varies across the collectives depending on their composition, functioning, and resource endowments. Nevertheless, there is a possibility of strategic failures because of intra‐ as well as intercollective conflicts and competition from other players. The success in this regard would, therefore, depend on how the collectives democratically resolve such conflicts and develop capacity and capabilities to enhance their competitiveness for greater strategic success. Besides, addressing market imperfections through policy and institutional interventions would also be crucial. [ABSTRACT FROM AUTHOR]

Purpose: Agriculture insurance is the panacea for the farming community. Many policy interventions were implemented for stimulating agriculture insurance access to farmers in India. However, access to agriculture insurance constantly remained one of the major challenges to Indian policy planners. The goal of the present paper is to explore current policy interventions in the area of agriculture insurance in India. Design/methodology/approach: The present paper reviews and analyzes the evidence literature through a content analysis method on development and performance analysis perspective of existing agriculture insurance schemes in India. Findings: Agriculture insurance is a significant risk management policy, but this is not easily reachable to the majority of farmers in India. The government of India introduces a novel agriculture scheme every decade, but every crop insurance scheme was inconsistent and ineffective owing to operational defects. Agriculture insurance in India is still developing in terms of coverage, scope, and exposure, but farmers' dissatisfaction about agriculture insurance turned out to be a negative word of mouth. Insurance illiteracy and farmers' preference for agriculture relief payments are the main reasons for limited access to agriculture insurance. The current crop insurance schemes are improperly operated because of implementation issues at the state level. Research limitations/implications: This paper will be useful for researchers and academicians to analyze the past and present status of crop insurance in India. Originality/value: The paper is the unique work of the authors as it has attempted to present India's journey with agriculture insurance. An effort is made in the present study to provide a comprehensive and holistic developmental and performance analysis perspective of agriculture insurance in India. [ABSTRACT FROM AUTHOR]

Expansion of aquaculture in the Sundarbans Biosphere Reserve (SBR) is irreversibly replacing agricultural land and the drivers of this change are disputed. Based on in-depth interviews with 67 aquaculture farmers, this paper characterizes major aquaculture types in the SBR, their impacts, and identifies drivers of conversion from agricultural land. Aquaculture types included traditional, improved-traditional, modified-extensive, and semi-intensive systems. Extensive capture of wild shrimp larvae is environmentally harmful but constitutes an important livelihood. Semi-intensive aquaculture of exotic shrimp (Litopenaeus vannamei) has much higher unit-area profitability than other types but involves greater financial risk. Profitability is the main driver for the transition from agriculture, but environmental factors such as lowered crop yields and cyclone impacts also contributed. Many conversions from agriculture to aquaculture are illegal according to the stakeholders. Existing legislation, if enforced, could halt the loss of agriculture, while the promotion of improved-traditional aquaculture could reduce the demand for wild seed., (© 2022. The Author(s) under exclusive licence to Royal Swedish Academy of Sciences.)

Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%—without fusion, 87%—with fusion) in classifying leaf diseases. [ABSTRACT FROM AUTHOR]

Purpose: Government of India has launched FPOs to organise small and marginal farmers into farmer collectives called FPOs. These FPOs, through economies of scale, aim to provide better collective strength to farmers for better access to production technology, value-addition services, high-quality inputs and marketing services for improving their incomes. Recently, the government has launched a scheme for creation and promotion of 10,000 more FPOs in the country. Despite potential benefits of FPOs and encouragement from the Government, there are many farmers in India, particularly Sikkim, who do not join the FPOs. This study aims to identify the major determinants that motivate farmers to join FPOs. Design/methodology/approach: Primary data for this study was collected during October, 2019–February, 2020 from 560 farm households in all four districts of Sikkim, India. The study used two-sample t-test and FPO participation probit model for determining the factors influencing a household's decision to participate in an FPO. Findings: The results of this study illustrate that education, farming experience, farming as the primary occupation, size of landholding, access to Internet, distance to the nearest market, medium level of social participation, extension contact, transportation facility and plan to expand the scale of operation in future are the major explanatory variables that have statistically significant impact on the farm households' participation in FPOs. Research limitations/implications: This study was based on a cross-sectional survey. As a result, the findings may be subjected to some limitations though the study made all possible efforts to minimise the limitations. Originality/value: This paper is based on or a novel data set, collected specifically to examine the determinants of membership in Sikkim, India that has not been studied before. Moreover, this study has identified the importance of information and awareness initiatives among the farmers as responsible for farmers' participation in FPOs. The findings of this study will have important implications and lessons to draw from for the Central Government's effort towards Formation and Promotion of 10,000 new FPOs, especially in the hilly states of India. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2022-0216. [ABSTRACT FROM AUTHOR]

India's ecosystem is still in a non-developed state and a low level of intelligence is showcased. Yet being a farming nation, the people are proud of what they produce. India is one of the biggest producers of agricultural products including major crops for red gram and groundnut, but not much attention is given to the way it is produced. The choices of the farmer and the combination of crops they cultivate represent a biodiversity strategy which offers a conservation technique for the bio-diversity of crop cultivation, which is extremely important for the long-term goals of sustainability of the modern agriculture which uses input of high energy. This paper discusses and looks upon the issues related to the alternate pathways for development in agriculture, mainly in the seeds of groundnut, along with the alternate cultivation of crops, prediction of crops placed in the context of a better sustainable landscape management plan in dry land agriculture. In dry land, low productivity of the crops maybe because of various damaging factors and parameters. This paper presents review of different AI based approaches that monitors the soil and nutrient patterns in dry-land agriculture also utilizing crops which fits for the suitable land which helps production and also the sustainability of the economic condition of the formers. The system helps in increasing the gains and profits by utilizing accurate details on dry-land agriculture in dry-land area. [ABSTRACT FROM AUTHOR]

At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [ABSTRACT FROM AUTHOR]

One of the most fertile areas of modern cultural research is the study of Rituals performed by indigenous communities. Rituals maintain and preserve the cultural identity of a society, especially, in preliterate oral tribal societies of the world. In such communities, Agriculture and Religion are intrinsically linked to the socio-religious sphere of their life. Their survival is solely based on human-nature dynamics. Such communities practice nature-worshipping and believe in the interconnectivity of the physical and supernatural worlds. Hence, they perform many rituals to appease the malevolent and to express gratitude to benevolent supernatural beings, deities, and spirits. Tangams are one such animist community that is the least populated and lesser-known ethnolinguistic group of Arunachal Pradesh. Their everyday life is based on the performance of several rites and rituals. These ritual performances are part of their cultural heritage. However, such practices are slowly in decline due to various internal and external factors. Therefore, this paper is a descriptive attempt to document, elucidate, preserve, and disseminate the dying Maye ritual practice and its associated belief systems. [ABSTRACT FROM AUTHOR]

The solar water pumping systems (SWPS) is useful in remote or village areas where there is no electricity. In India, SWPS are revolutionizing agriculture by giving farmers access to power for everything, from harvesting crops to pumping water. As the government is working to make solar pumping systems affordable for the agricultural sector, induction motors (IM) are found to be the most appropriate motor type for this application because of its low cost, durability and ruggedness. In this work, the SWPS is powered by an inverter driven three-phase IM with V/f control technique. Various MPPT techniques have been proposed to track maximum power from PV system under partial shading condition. This paper mainly focuses on a hybrid Particle Swarm Optimization-Incremental Conductance (PSO-INC) algorithm to capture the peak power efficiently when IM based SWPS is subjected to partial shading. The results are compared with PSO algorithm, a hybrid PSO-PO method and a modified 0.8 Voc approach. The MATLAB/Simulink environment is used to simulate and test these algorithms on the SWPS system with partial shading. The motor's performance in terms of torque ripple and speed settling time is analyzed. [ABSTRACT FROM AUTHOR]

The Northeast region of India is endowed with diverse soil and agro-climatic conditions that are conducive to the growth of a large variety of temperate and tropical horticultural crops. Fruits, vegetables, and spices of the region are highly nutritious and have a market within and outside the country. The paper is an attempt to assess the potential of horticulture in the region. To gauge the state-wise and regional growth trend and variability in area and production of these crops during the period 2009-2019, Compound Annual Growth rates and Instability Index have been computed from secondary data. The study reveals a rising regional growth trend with low instability for the production of fruits and vegetables and moderate instability for spices. This indicates the possibility of sustainable development of horticulture in all the Northeast states through strategic planning. Fruits and spices of the region also have a market in Middle-East and neighbouring countries. However, lack of commercialisation, poor market intelligence, and linkages are impeding the growth of exports. To unleash the true potential of horticulture, it is imperative to develop infrastructure, modernise farming and establish seamless value chains with greater market integration. [ABSTRACT FROM AUTHOR]