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The paper is devoted to crop production adaptation to climate change. It considers the problem of finding the future optimal structure of the sowing area, considering the possible climate change scenario. It implies finding a distribution of the sowing area for different groups of crops that would maximize the average annual yield while limiting the risk of shortfall in crop production due to adverse weather conditions. The authors have developed a mathematical model to solve this problem and used it for computations. [ABSTRACT FROM AUTHOR]

Addressing the excessive input and inefficient use of agrochemicals are crucial for global food security, environmental protection, and human health. This paper offers a new idea from the perspective of outsourcing agricultural production. The impact of outsourcing on the frequency and intensity of agrochemical inputs were theoretically analyzed and empirically tested using a field survey of 1211 farmers in Heilongjiang, Henan, and Hunan, the major food-producing areas in China. A Logit regression framework was used to analyze the effect, a conditional mixture process (CMP) method was used to address potential endogeneity concerns, and a mediation effect model was used to dissect the mechanism. The results show that the effect of outsourcing on both input frequency and input intensity of agrochemicals was positive at the 1% significance level. The positive effect conclusion still holds even after addressing the potential endogeneity concerns, and in the sub-sample estimates for maize, wheat, and rice. We conclude that outsourcing can improve the utilization efficiency of agrochemicals by increasing the frequency of agrochemical inputs, but fail to solve the excessive agrochemical inputs and even leads to a further increase in the intensity of agrochemical inputs. Moreover, the mechanism for an increase in agrochemical input intensity due to outsourcing was explored, and it is more likely to be caused by inhibiting farmers' investment in soil improvement measures. [ABSTRACT FROM AUTHOR]

The conflict between agricultural production and environmental protection, especially vegetable production, is one of the world's most prominent concerns. The acceptance degree and possible response of new agricultural operation subjects, represented by Farmers Professional Cooperatives (FPCs), to green production policies are a core issue that must be considered when designing environmental policies in the future. Based on field survey data of 192 FPCs of vegetables in Shanghai, China, this paper uses a choice experiment method to test the willingness of cooperatives to adopt green production behaviors under different policy settings and to identify the differences under different operation scales. The results show that both mandatory policies and incentive policies have an impact on the willingness of cooperatives to adopt green production behaviors, but significant differences exist among FPCs of different operation scales. Specifically, the willingness of large-scale FPCs to adopt green production behavior is affected by key technology training and income subsidies, whereas the willingness of small-scale FPCs is only affected by income subsidies; the willingness of medium-scale FPCs is simultaneously affected by key technology training, income subsidies, and penalties. Therefore, when formulating relevant policies in the future, policy-makers should consider local conditions, including the endowment heterogeneity of the production and operation subjects, so as to enhance the level of green production. [ABSTRACT FROM AUTHOR]

Cotton is a crucial cash crop in the tribal belt of Odisha. It has a significant effect on the livelihood of the tribal people. The volatile socio-economic background of the tribal farmers is a major hindrance to the proper development of the agriculture sector. In this regard, it is important to study the possible factors affecting cotton productivity in the tribal region to boost the policy effectiveness for the development of this cash crop in the area. More precisely, how is their socio-economic condition reflected in their crop productivity? So, the development of the tribal farmers can develop this crop in the region. This study analyses the detrimental effect of socio-economic factors on cotton productivity in the tribal area of Odisha. The data are collected using a structured questionnaire and a multiple-sampling technique. To understand the effect of socioeconomic factors on productivity, the ordinary least square (OLS) method has been employed in this paper. The result shows that most of the farmers are illiterate for which productivity is degrading. The age and gender of the head of the household are significant contributors to the cotton productivity of the study area. Male-headed households are more productive than female-headed households. Per capita monthly income and proportion of irrigated land are also important factors to affect cotton productivity positively. However, the social group of the family has no significant effect on the cotton productivity in the study area. Policymakers and agriculturists should emphasize farmer literacy, farmer income and land irrigation facilities to boost the productivity of this crop in the studied region. [ABSTRACT FROM AUTHOR]

An efficient plant disease identification is prime important for Agricultural productivity. This paper proposes a method of automatic leaf extraction and classification with improved accuracy compared with existing techniques. Images acquired from natural fields are complex, with background information that needs to be accurately segmented to identify areas of interest. Grab cut is a semi-automatic approach to extract foreground objects, which might occasionally degrade an object's properties. This paper suggests a better Grab cut for automatically extracting leaves from real-field images. The enhanced dataset of 23,617 images with eleven tomato leaf classes are created using the plant village dataset and actual field images. This paper addresses a wide range of issues related to convolutional neural network optimization, including how the number of layers affects the results of leaf disease detection and the creation of tiny models for portable devices. Five models, VGG 16, MobileNet, and custom architecture with input sizes of 98 × 98, 160 × 160, and 256 × 256, have been optimized and evaluated through several trials. Training and testing involve varying the input size, the number of layers, the optimizer, the dropout, the batch normalization, and the transfer learning. This paper presents and analyzes the findings of thirty experiments on various architecture. The proposed modified sequential eleven-layer architecture achieves accuracy compared with MobileNet and a reduced model size of 3.9 MB for input size 160 and 4.5 MB for input size 98. [ABSTRACT FROM AUTHOR]

Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China's farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs." [ABSTRACT FROM AUTHOR]

Anthropogenic activities have led to a surge in the use of synthetic chemical compounds in agriculture, elevating environmental toxicity levels. As a response to this concern, there is a growing demand for environmentally friendly solutions. In recent times, the focus has shifted towards the development of cost-effective and ecologically sound organic products known as biostimulants. These innovative products play a pivotal role in enhancing agricultural productivity by fostering comprehensive plant growth and development. Biostimulants encompass a diverse range of natural and synthetic substances, categorized into microbial, non-microbial, and waste-derived sources. When judiciously applied to crops, these substances exhibit the remarkable ability to enhance plant metabolism, bolster productivity, and enhance resilience to adverse environmental conditions. Through modulation of molecular mechanisms and epigenetic alterations, biostimulants achieve this by influencing critical signalling molecules, transcription factors, and hormonal levels, which collectively contribute to stress tolerance. This review paper delves into the burgeoning industrial interest surrounding biostimulants. It sheds light on their intricate modes and mechanisms of action, encompassing both physiochemical and molecular aspects. Furthermore, the paper underscores the captivating potential of biostimulants to induce trans-generational plasticity and metabolite accumulation within plants, a phenomenon warranting deeper exploration through metabolomics. This review paper focusses on valuable insights into the transformative influence of biostimulants on agricultural practices, showcasing their capacity to usher in a new era of sustainable and resilient crop cultivation. [ABSTRACT FROM AUTHOR]

Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem. [ABSTRACT FROM AUTHOR]

Nutrient stress can impose significant metabolic strain on plants, resulting in declining agricultural productivity. Nitrogen, phosphorus, and potassium are essential growth-limiting nutrients and are the primary building elements for amino acids, nucleic acids, proteins, and chlorophyll. The absence of these nutrients has observable effects on various plant characteristics, such as leaf size, color, and plant height. However, recent technological advances in imaging have given birth to computer vision-based plant phenomics, which holds great promise for plant research and management. The non-destructive, quick, automated assessments made possible by these imaging techniques are transforming the field of plant nutrient stress research and monitoring. This paper presents a hybrid quantum–classical model (HQCM) for identifying multi-nutrient stress and nutrient stress level quantification (NSLQ) for plant stress level identification by analyzing plant leaf images, utilizing the combined capabilities of classical and quantum computing systems. The HQCM model uses groundnut multi-nutrient stress (private), rice plant nutrient stress (public), and plant village (public) datasets for experimentation. The HQCM model exhibited impressive levels of accuracy, achieving rates of 97.79%, 97.37%, and 98.75% on the groundnut, rice, and plant village datasets, respectively. This performance surpasses conventional models, including Xception, DECM, DWC, and ResNet50V2. The proposed model showed significant advancements in accuracy, reaching the performance of state-of-art algorithms by 3.24%, 4.07%, and 6.73%, thus emphasizing its better performance. [ABSTRACT FROM AUTHOR]

Crop diseases pose significant threats to agriculture, impacting crop production. Biotic factors contribute to various diseases, including fungal, bacterial, and viral infections. Recent advancements in deep learning present a novel approach to the detection and recognition of these crop diseases. While considerable research has focused on identifying and recognizing crop diseases, fungal disease-affected crops have received relatively less attention and also detecting disease on different region datasets. This paper is about spotting fungal diseases in crops across different regions with diverse climates. It emphasizes the need for tailored detection methods, addressing the risk of mycotoxin production by fungi, which can harm both humans and animals. Detecting fungal diseases in apple, guava, and custard apple crops such as spot, scab, rust, rot, leaf spot, and insect ate. In the proposed work, the modified ResNeXt variant of the convolution neural network (CNN) technique was employed to predict 3 major crop classes of fungal disease. Initially, using Inception-v7 and ResNet for fungal disease in crops did not yield satisfactory results. A modified ResNeXt CNN model was proposed, showing improved fungal disease prediction. The novel model underwent a comparison with established methodologies. The suggested model draws upon a benchmark dataset consisting of 14,408 images capturing fungal diseases, categorized into three distinct classes: apple, custard apple, and guava. Experimental outcomes show that the proposed mutated ResNeXt model outperformed the state-of-the-art approaches. The model achieved 98.92% accuracy and high performance across recall, precision, and F1-score (above 99%) for the benchmark dataset, which gained encouragement and was comparable with the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Burgeoning global trade and colonial policies promoted transformations in land use and agriculture throughout tropical regions in the 19th and 20th centuries, but the local and regional ecological consequences of landscape changes are still being identified and analysed. The Philippine Archipelago, which experienced successive colonial regimes across more than 7100 islands, exemplifies the multiplicity of ecological outcomes produced by these transformations. To better characterise diverse landscape change, we use colonial censuses and datasets to assess land use, production and agricultural yields in the Philippines during the late Spanish and early U.S. colonial periods (ca. 1870–1925). Our novel digital, quantitative analysis indicates that, at the national and provincial scales, agricultural production and land use increased for all major crops in both periods, while agricultural yields were mostly constant. Our results suggest that colonial investments to "improve" Philippine agriculture, specifically their efforts to increase production per hectare, were not effective. Our provincial-scale analysis also confirms the importance of distinct labour patterns, geographies and socio-political arrangements in defining this period's ecological consequences, and we provide quantified and historically contextualised data in a format amenable to ecologists to promote future, localised historic ecological research. [ABSTRACT FROM AUTHOR]

Greenhouses were known for their benefits in creating a stable and warm environment for plants to grow healthy. Meanwhile, air ventilation as well as optimal roof height of greenhouses is not well established in the literature as they are chosen based on only few experiences. As a remedy to this problem, the present work builds new correlations relating the roof height and the air velocity of ventilation on the temperature and vapor pressure deficit (VPD) thereby allowing a better control of these parameters for an optimal crop production. To achieve this goal, the study utilizes an experimental setup and a numerical model. The numerical model considers various elements, such as polyethylene, air, crops, and soil in combination, to evaluate their impact on the indoor climate. This paper presents a detailed analysis of how the greenhouse height ratio, designed as 'k,' can directly affect the microclimate within a polyethylene greenhouse, using a combination of numerical and experimental data. The study establishes strong correlations to capture the variations resulting from changes in the greenhouse height. In fact, a series of numerical simulations were conducted to assess the inlet velocities and height ratio on indoor temperature, velocity, and VPD. The comparative analysis reveals that the optimal conditions for the plant are only achieved in the lower part of the greenhouse for all considered greenhouse heights, where VPD = 2.2 for k = 1 and VPD = 1.8 for k = 4. Therefore, this study illuminates the impact of greenhouse height on greenhouse climates and underscores the significance of precise modeling and control to enhance agricultural practices in Tunisia. [ABSTRACT FROM AUTHOR]

The low-lying Vietnamese Mekong Delta (VMD) is a key agricultural production landscape increasingly threatened by anthropogenic stresses and climate change. Among the different threats, droughts caused by extreme events, climate change and upstream developments, affect the delta the most. This paper explores the relationship between the intensity, duration, and frequency of meteorological droughts and hydrological droughts using a range of indices. We used monthly rainfall and stream flow data for the period 1992–2021 to calculate the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), and the streamflow drought Index (SDI) for different time intervals. We found no observed time lag, and a strong correlation coefficient between upstream hydrological and downstream meteorological drought events assessed over long-term scales (i.e., 12-months). This is true for all downstream sites, except Ben Tre City. Hydrological drought events onset lagged 5–6-, 6-, and 3–4-month behind meteorological droughts at mid- and shorter assessment time scales (9-, 6-, 3-month). The average correlation coefficients between hydrological indices and meteorological indices at 9–3-month time scales ranged from moderate to weak. These findings shed light and advance the understanding of the progression of meteorological to hydrological droughts in the VMD. Our results aid the regional understanding of drought onset and the causative mechanisms at work, which is important for both medium- and long-term drought forecasting and adaptation planning. [ABSTRACT FROM AUTHOR]

Optimising the use of natural resources for food production in the context of changing climate is an increasingly important issue. Optimisation techniques have been shown to be remarkably effective for planning problems, and tools regional planners and farmers can use to determine the viability of agricultural land use planning into the future. This paper systematically reviews the recent literature in this area and draws out the key emerging themes: few studies to date have explicitly incorporated climate projections into optimisation models; increased tension for water resources between stakeholders; and various agricultural production systems of complex versions of crop planning. From this review it can be seen that increasing concentration on the use of climate projection models within agriculturally-oriented optimisation processes is a necessity. [ABSTRACT FROM AUTHOR]

Climate change poses an urgent threat, necessitating the implementation of measures to actively reduce carbon emissions. The development of effective carbon emission reduction policies requires accurate estimation of the costs involved. In situations where actual prices of commodities are not available in the market, shadow pricing provides a useful method to calculate relative prices between commodities with and without price information. However, most studies focus on the industry, with few contributions on agricultural sector. This paper estimates the shadow price of carbon emissions in the agricultural sector from a provincial perspective, incorporating the impact of livestock into the calculation of carbon emissions and shadow pricing. Our findings indicate that ignoring livestock may overestimate CSP values. On the whole, the level of carbon shadow price is rising, indicating good green development in China's agricultural sector. The two types of convergence results show that there is sigma convergence and beta convergence in the western and central regions, demonstrating a significant improvement in environmental performance. [ABSTRACT FROM AUTHOR]

Intercropping is a mature and well-known agronomic practice that began to attract interest from the scientific community in the mid-1900s and has known an exponential growth in research activity since the beginning of this century. Over the years, different intercropping indices have been developed to evaluate the performance of this crop production system in comparison to standard monoculture practices. Nowadays, more than 20 of these intercropping indices have been described in scientific literature. This review aims to review these indices and check their performance using a meta-dataset consisting of data points from various intercropping experiments that have been described in peer-reviewed publications. Our results show that different indices evaluate different aspects of intercropping trials and that commonly used indices generally do not capture the full performance of the system. More specifically, intercropping results are influenced by both the total sowing density and the crop ratio and indices differ in the way that these dependencies are accounted for. This study suggests creating a standard protocol for the intercropping trials and their evaluation as crucial elements to optimize intercropping research. [ABSTRACT FROM AUTHOR]

Rapid urbanization negatively affects the eco-land. The topic of accurate zoning and control of ecological space has become a challenge for many countries. This paper uses the idea of ESP to delineate the control system of China's coastal area. First, three types of ecological sources were identified from the aspects of landscape functions' importance, landscape sensitivity, and landscape connectivity. Then, ecological corridors and nodes were identified using the model of minimum cumulative resistance (MCR) based on the resistance surface. Finally, an ecological control zoning system was constructed by using a "bottom-up" transmission mechanism. The results show that: (1) Most of the landscape function-oriented sources and connectivity-oriented sources are located in the south, where habitat quality is superior. Urban construction and agricultural production are the main threat factors to sensitivity-oriented sources. (2) 81.24% of ecological corridors are reticulated, indicating good structural connectivity, and 11 fragile corridors should be specially protected. (3) Among the three second-level of strictly protected areas, the percentage of restoration area and landscape structure maintenance area are 40.19% and 28.47%, indicating the high threat to eco-land and low landscape connectivity; measures are needed to enhance eco-connectivity and increase the resilience of eco-lands. The research constructs a point-line-plane integrated ecological control zoning system, which can provide a scientific basis for the spatial prioritization of coastal ecological protection in China's coastal areas. [ABSTRACT FROM AUTHOR]

Additives are an effective means of improving the performance of anaerobic digestion of agricultural wastes and can be well suited to deal with the inhibitory effects produced at various stages of digestion. There are significant differences in the role of different types or doses of additives in other mechanisms, so it is essential to take advantage of the benefits of various additives to enhance overall process performance. This paper presents a review of additives used in different stages of anaerobic digestion of agricultural waste in terms of gas production performance, including associated bacteria and enzymes for accelerating the rate of hydrolysis, zeolites for controlling ammonia-nitrogen concentration/conductive materials for improving acid accumulation, and trace elements that can supplement the nutrients required by microorganisms and chelating agents for enhancing nutritional activity. Finally, this mini-review emphasized that the dosage of additives should be reasonably controlled to maximize the gas production efficiency of the anaerobic fermentation system during the actual production application. [ABSTRACT FROM AUTHOR]

Introduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.Materials and methods: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.Results: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.Conclusion: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring. [ABSTRACT FROM AUTHOR]

This paper highlights why it is crucial to determine crop production using artificial intelligence for the growth of agriculture. In this paper, an elaborated ResNet-59 model has been developed to estimate potato harvests accurately. The dataset contained a global potato and tomato production data set that began in 1961 and ended in 2021; different deep learning architectures considered were ResNet-59, GoogLeNet, VGG-19, ResNet-50, VGG-16, and MobileNet. Collectively, the outcome of this ResNet-59 model’s improvement led to a general superiority with more minor mean squared errors, which were recorded as 0.0083, and a mean absolute error of 0.0762, a median of absolute errors amounted to 0.0750 along with an R2 value equalling 99.05%. According to these results, precision agriculture is another area where ResNet-59 could be effective, thus promoting the rational distribution of resources, minimizing waste and increasing food security. It is epoch-making to deliberate on the capability of artificial intelligence to emancipate sustainable farming and future research. [ABSTRACT FROM AUTHOR]

The interplay of climate change, upstream hydropower development, and local water engineering interventions for agricultural production contributes substantially to the transformation of waterscapes and water scarcity in the Vietnamese Mekong Delta. This paper aims to examine how these dynamics are linked to the paradigm shift in water management in An Giang and Ben Tre, the two ecologically distinct provinces that face serious water scarcity in the delta. We used the adaptive management concept to examine how state-led policy directions from food security towards water security enable change in water management that gives priority to water retention. While policy learning is evident, questions remain about how this ad-hoc solution could help address the presently acute water scarcity and water security over the long term. The paper advocates achieving water security should focus not only on diplomatic interventions into upstream climate-development complexities but also local water-livelihood politics. [ABSTRACT FROM AUTHOR]

Crop diseases adversely affect agricultural productivity and quality. The primary cause of these diseases is the presence of biotic stresses such as fungi, viruses, and bacteria. Detecting these causes at early stages requires constant monitoring by domain experts. Technological advancements in machine learning and deep learning methods have enabled the automated identification of leaf disease-specific symptoms through image analysis. This paper proposes image-based detection of leaf diseases using various deep learning-based models. The experiment was conducted on the PlantVillage dataset, which consists of 54,305 colour leaf images (healthy and diseased) belonging to 11 crop species categorized into 38 classes. The Inception-ResNet-V2-based model achieved a 10-fold cross-validation accuracy of 0.9991±0.002\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$0.9991 \pm 0.002$$\end{document} outperforming the other deep neural architectures and surpassing the performance of existing models in recent state-of-the-art works. Each underlined model is validated on an independent cohort. The Inception-ResNet-V2-based model achieved the best 10-fold cross-validation accuracy of 0.9535±0.041\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$0.9535 \pm 0.041$$\end{document} and was found statistically significant among other deep learning-based models. However, these deep learning models are considered a black box as their leaf disease predictions are opaque to end users. To address this issue, a local interpretable framework is proposed to mark the superpixels that contribute to identifying leaf disease. These superpixels closely confirmed the annotations of the human expert. [ABSTRACT FROM AUTHOR]

Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R2 = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R2 = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R2 = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity. [ABSTRACT FROM AUTHOR]

Brazilian agriculture is characterized by the prevalence of small farms and regions with a high degree of rurality and dominance of the agricultural sector in the economy. These two characteristics affect the diversity of agricultural production in the country. Specifically, the article aims to analyze the effects of size farm and farmland use on agricultural diversification and the effects of demand and technology adopted by farmers. The database encompasses 4298 Brazilian municipalities from 1996 to 2017 (the last three agricultural censuses). Empirically, we consider spillover effects by estimating spatial models at the municipal level using panel data, highlighting the importance of location and neighborhood. The study's findings indicate a tendency toward local concentration of agricultural production in the country, despite the balance between municipalities with diversified and concentrated production. The results showed a significant effect of small farms and the municipalities' rurality degree on the agricultural output diversification. The study provides insights into the discussion on measures to strengthen support for small properties and regions that diversify crops to ensure economic efficiency and food security. [ABSTRACT FROM AUTHOR]

Legumes acquire nitrogen-fixing ability by forming root nodules. Transferring this capability to more crops could reduce our reliance on nitrogen fertilizers, thereby decreasing environmental pollution and agricultural production costs. Nodule organogenesis is complex, and a comprehensive transcriptomic atlas is crucial for understanding the underlying molecular events. Here, we utilized spatial transcriptomics to investigate the development of nodules in the model legume, Lotus japonicus. Our investigation has identified the developmental trajectories of two critical regions within the nodule: the infection zone and peripheral tissues. We reveal the underlying biological processes and provide gene sets to achieve symbiosis and material exchange, two essential aspects of nodulation. Among the candidate regulatory genes, we illustrate that LjNLP3, a transcription factor belonging to the NIN-LIKE PROTEIN family, orchestrates the transition of nodules from the differentiation to maturation. In summary, our research advances our understanding of nodule organogenesis and provides valuable data for developing symbiotic nitrogen-fixing crops. Legumes develop root nodules to access nitrogen. A spatial transcriptomic analysis of Lotus japonicus uncovered key molecular mechanisms and genes involved in nodule development, promoting the creation of novel nitrogen-fixing crops. [ABSTRACT FROM AUTHOR]

Background: Agriculture is Ethiopia's principal economic activity, employing the vast majority of the population, however, smallholder farmers in the country face long-standing challenges, including low levels of production, low income, and less credit use. This study aimed to analyze the impact of access to credit on crop productivity and income among small-scale farmers in the study area. Methods: We conducted a cross-sectional study from January 1st to 30th, 2022. Primary data were collected from purposely selected seven (7) kebele of the district based on potential users of agricultural credit. Then the impact was investigated by applying the propensity score matching technique. A Probit regression model was used to determine factors associated with credit use. The significance of the variables was decided at a 5% level of significance. Results: In our study, a total sample of 347 smallholder farmers were randomly selected, out of which 42.9% were credit-users while 59.1% were non-users. Regarding sources of credit use, Vision Fund was the most commonly used source of credit with 67.8% of users, followed by the Omo Microfinance institution with 22.1%. Sex of the farmer, age of the farmer, existence collateral, farm size, attitude towards risk, extension service, membership in credit institutions, road access, and number of total livestock units in tropical livestock units (TLU) had significant effects on credit use at a 5% level of significance. Moreover, the estimates showed that utilizing credit resulted in a rise in crop productivity in current market value and total yearly income by 19,302.44 and 18,009.63 respectively. Conclusion: Our study suggested that credit use had a considerable impact on crop productivity and total annual income. As a result, the study recommended that the government's rural finance policy agenda should prioritize opening different branches near farmers, improving credit usability, and providing more training programs to smallholder farmers to improve their skills and help reduce the problems that impede their use of agricultural credit. [ABSTRACT FROM AUTHOR]

Nutrient deficiencies in the plant can significantly reduce agricultural productivity. A sufficient ratio of nutrient elements like nitrogen (N), phosphorus (P), and potassium (K) is essential for plant growth. Deficiencies in these elements lead to a significant decline in crop yield. The primary objective of this work is to identify the nutrient deficiency in the leaf images and determine how severe the nutrient deficiency is in the plant. We have enhanced the VGG16 transfer learning model and integrated it with another proposed nutrient severity identification module. The proposed VGG16 model is trained with a groundnut dataset and classified the images as N, P, and K deficient. Predicting yield loss in the crop based on nutrition deficiency is the secondary objective of this work. To identify deficiencies, we have used two different datasets: rice plant and groundnut images. The proposed methodology classifies the plant leaf images into N, P, and K classes of deficiencies. Based on this information, crop yield loss due to each nutrient deficiency is estimated. The proposed enhanced (VGG16) classification accuracy is 98% on the groundnut dataset (i.e., our dataset collected from the field). The performances were compared with the existing state-of-the-art models, which found that the proposed model performs better than the current models trained on the same datasets. [ABSTRACT FROM AUTHOR]

Continued climate change impose multiple stressors on crops, including pathogens, salt, and drought, severely impacting agricultural productivity. Innovative solutions are necessary to develop resilient crops. Here, using quantitative potato proteomics, we identify Parakletos, a thylakoid protein that contributes to disease susceptibility. We show that knockout or silencing of Parakletos enhances resistance to oomycete, fungi, bacteria, salt, and drought, whereas its overexpression reduces resistance. In response to biotic stimuli, Parakletos-overexpressing plants exhibit reduced amplitude of reactive oxygen species and Ca2+ signalling, and silencing Parakletos does the opposite. Parakletos homologues have been identified in all major crops. Consecutive years of field trials demonstrate that Parakletos deletion enhances resistance to Phytophthora infestans and increases yield. These findings demark a susceptibility gene, which can be exploited to enhance crop resilience towards abiotic and biotic stresses in a low-input agriculture. Crop production is threatening by multiple stressors. Here, the authors report the identification of a thylakoid protein encoding gene and show that knocking out this gene results in enhanced resistance to oomycete, fungi and bacteria, as well as increased tolerance to salt and drought mimics in potato. [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]

Agricultural green production technology (AGPT) is essential for the sustainable development of agriculture, yet adoption rates among farmers are often low due to limited access to education and information. Based on the extended technology acceptance model, this study takes Liaoning, a major agricultural province in China, as a representative region to explore how digital multimedia influences the adoption of AGPT by farmers. The findings show that the perceived ease of use of these technologies significantly enhances farmers' intentions to adopt, while perceived risks detract from these intentions. Although digital multimedia effectively promotes AGPT by improving its perceived ease of use and usefulness, its role in mitigating perceived risks is minimal. Based on these results, we recommend that the government improve the quality of information on digital platforms by involving experts in the field and offer specific digital marketing training to potential users to increase the adoption of sustainable agricultural technologies. [ABSTRACT FROM AUTHOR]

Ensuring household food security and fighting hunger are global concerns. This research highlights factors affecting food security and solutions by utilizing a nexus of statistical and fuzzy mathematical models. A cross-sectional study was conducted in district Torghar, Northern Khyber Pakhtunkhwa, Pakistan, among 379 households through a structured interview schedule. Further, the data were coded into SPSS for analysis, i.e., descriptive, and inferential statistics. In a binary logistic regression model, the study explored that agricultural productivity lag, population growth, and climate change all had a negative impact on household food security (P = 0.000). In addition, these results were further cross-checked through spherical fuzzy mathematics. The fuzzy results revealed that population growth is the most powerful factor deteriorating the existing equilibrium of household food security in Torghar. The study concluded that climate change, population explosion, and agricultural production were the major factors behind the hunger in the study area. Thus, designing a synergy based on taking all these aforementioned realities into consideration is the need of the hour. If extended, a comprehensive model of addressing these anomalies with a solely objective-based approach may work as a panacea for all ills. Lastly, this study contributes to the food security literature by introducing a new approach that is more robust and flexible than existing methods. [ABSTRACT FROM AUTHOR]

Farm-level decision-making by resource-constrained smallholder farmers, such as investment in improved farm management practices and technologies may considerably be influenced by the age of farm operators. However, evidence of the effect of farm operators' age on farm efficiency and technological endowment, and consequently on agricultural productivity in sub-Saharan Africa has been inexact. To contribute to an improved understanding of the age-efficiency-productivity nexus, this study investigates the impacts of farm operators' age on agricultural productivity by evaluating the managerial performance and technological endowment of the operators, disaggregated across three age cohorts, viz. the youth, middle-aged and the aged. We fit the meta-stochastic frontier statistical framework to a country-wide sample of 24,596 farm households, spanning three decades of data collection in Ghana. The results show that relative to the potentials of each age cohort, more output can be generated using currently allocated inputs, but under improved farm management practices. Whereas we did not find evidence for possible age-related technological differences in agricultural production in Ghana, we did find strong support for possible age-induced managerial differences in farm production, with youth operators being more efficient than their middle-aged and aged peers. Consequently, the age of farm operators significantly affects agricultural productivity in Ghana through their efficiency of resource allocation. We find these results relevant for policy attention, in terms of the targeting of support to farm operators in the various age cohorts and in the country's quest to achieve greater agricultural productivity. [ABSTRACT FROM AUTHOR]

Aims: Fertilization methods have affected the development of dryland agriculture. There is a need to understand the impacts of different fertilizer application depths on crop production and gas emissions to facilitate the sustainable development of dryland agriculture. Methods: A field experiment was conducted for two years (2019–2020) in a dryland agroecosystem in the Loess Plateau region of China. Five fertilizer placement depths were tested comprising 0 cm (FD0), 5 cm (FD5), 15 cm (FD15), 25 cm (FD25), and 35 cm (FD35). N-P-K fertilizer was applied to all treatments as base fertilizer at the same rate. After sowing, the gas emission fluxes were measured 17 consecutive times. We systematically analyzed the effects of different fertilization depths on the summer maize (Zhengdan 958) yield, ammonia (NH3) volatilization, and greenhouse gas emissions (nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4)). Results: The results showed that the NH3 volatilization, N2O emissions, CO2 emissions, and global warming potential (GWP) decreased as the fertilization depth increased. Compared with the traditional fertilization depth (FD5), deep fertilization at 15 cm clearly reduced the NH3 volatilization, N2O emissions, CO2 emissions, GWP, and greenhouse gas intensity. In addition, compared with FD25 or FD35, FD15 increased the capacity of the soil to absorb CH4. Critically, compared with the traditional fertilization depth, FD15 can effectively improve the summer maize biomass yield (4.2%) and grain yield (18.1%) at the final harvest. Conclusion: FD15 can promote the sustainable development of dryland agriculture in the Loess Plateau region of China by improving crop production and reducing gas emissions. [ABSTRACT FROM AUTHOR]

Equilibrium among water, food, energy, and climate actions is necessary for life to exist, quality, and sustainability. This article explored how to ensure sustainability, and equilibrium in the irrigation processes by proposing irrigation equilibrium indicators (IEIs) for sustainable irrigated agriculture (SIA). The primary purpose of IEIs is to achieve a state of sustainable climate and environmental balance. The pressures driving agriculture and irrigation professionals to enhance the irrigation scheme performance are tremendous in all agricultural communities. Monitoring, assessment, and improvement of agriculture practices and irrigation schemes for enhancing the Climate, water, food, and energy (CWFE) nexus is a must. As an auspicious climate action, IEIs were developed to enhance the irrigation scheme's efficiency, within the scope of SIA. Subsequently, water, agricultural, food, and energy productivity could be optimized. Then, the appropriate equilibrium indicators could identify the actual performance of the CWFE nexus as a whole and the performance of each component. The effective irrigation scheme is the backbone of SIA. IEIs could measure the degree of achieving the overall and specific objectives and designated irrigation processes. The ultimate measure of equilibrium is optimizing sustainable agricultural yields and productivity, ensuring environmental balance, strengthening life quality, and maximizing economic returns. [ABSTRACT FROM AUTHOR]

Background: We are facing the challenge of sustainable phosphorus (P) use due to high P inputs in farmland and low P-use efficiency of crops, which leads to critical soil P imbalances and accelerates consumption of limited P resources. Organic P is an essential component of the soil P cycle, and if managed properly, may compensate partly for decreased usage of P fertilizers. However, organic P dynamics in the rhizosphere has received little attention even though it is important for P availability to plants. Scope: We review biological turnover of organic P in soils, focusing on the processes driven by roots and microbial communities and their interactions in the rhizosphere. We also consider recent progress in understanding of organic P acquisition strategies that integrate soil carbon (C) cycling. We address several perspectives to achieve more sustainable P use in agriculture via rhizosphere management aimed at recycling and re-using soil organic P. Conclusions: Organic P mineralization by microorganisms may be limited by low P and/or C availability. In these processes, plants interact with microorganisms to alter stoichiometric ratios of C to P in the rhizosphere soils or microbial community, thus impacting on turnover of organic P into inorganic P for plant absorption, which is manifested in plant-microbial facilitation/competition in the rhizosphere. We highlight future research in improving rhizosphere management of agroecosystems by optimizing nutrient inputs and enhancing rhizosphere interactions to manipulate P and C dynamics towards sustainable organic P use. [ABSTRACT FROM AUTHOR]

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers. [ABSTRACT FROM AUTHOR]

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China's 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges. [ABSTRACT FROM AUTHOR]

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture. [ABSTRACT FROM AUTHOR]

Rice output has grown globally, yet abiotic factors are still a key cause for worry. Salinity stress seems to have the more impact on crop production out of all abiotic stresses. Currently one of the most significant challenges in paddy breeding for salinity tolerance with the help of QTLs, is to determine the QTLs having the best chance of improving salinity tolerance with the least amount of background noise from the tolerant parent. Minimizing the size of the QTL confidence interval (CI) is essential in order to primarily include the genes responsible for salinity stress tolerance. By considering that, a genome-wide meta-QTL analysis on 768 QTLs from 35 rice populations published from 2001 to 2022 was conducted to identify consensus regions and the candidate genes underlying those regions responsible for the salinity tolerance, as it reduces the confidence interval (CI) to many folds from the initial QTL studies. In the present investigation, a total of 65 MQTLs were extracted with an average CI reduced from 17.35 to 1.66 cM including the smallest of 0.01 cM. Identification of the MQTLs for individual traits and then classifying the target traits into correlated morphological, physiological and biochemical aspects, resulted in more efficient interpretation of the salinity tolerance, identifying the candidate genes and to understand the salinity tolerance mechanism as a whole. The results of this study have a huge potential to improve the rice genotypes for salinity tolerance with the help of MAS and MABC. [ABSTRACT FROM AUTHOR]

The application of nitrogen (N) fertilizer both underpins high productivity of agricultural systems and contributes to multiple environmental harms. The search for ways that farmers can optimize the N fertilizer applications to their crops is of global significance. A common concept in developing recommendations for N fertilizer applications is the "mass balance paradigm" – that is, bigger crops need more N, and smaller less – despite several studies showing that the crop yield at the optimum N rate (Nopt) is poorly related to Nopt. In this study we simulated two contrasting field experiments where crops were grown for 5 and 16 consecutive years under uniform management, but in which yield at Nopt was poorly correlated to Nopt. We found that N lost to the environment relative to yields (i.e., kg N t-1) varied +/- 124 and 164 % of the mean in the simulations of the experiments. Conversely, N exported in harvested produce (kg N t-1) was +/- 11 and 48 % of the mean. Given the experiments were uniformly managed across time, the variations result from crop-to-crop climatic differences. These results provide, for the first time, a quantitative example of the importance of climatic causes of the poor correlation between yield at Nopt and Nopt. An implication of this result is that, even if yield of the coming crop could be accurately predicted it would be of little use in determining the amount of N fertilizer farmers need to apply because of the variability in environmental N losses and/or crop N uptake. These results, in addition to previous empirical evidence that yield at Nopt and Nopt are poorly correlated, may help industry and farmers move to more credible systems of N fertilizer management. [ABSTRACT FROM AUTHOR]

Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience. [ABSTRACT FROM AUTHOR]

Northeast China is the main food production base of China. Extreme precipitation (EP) events can seriously impact agricultural production and socioeconomics, but the understanding of EP in Northeast China is still limited. In this study, using the non-stationary generalized extreme value (GEV) model, we investigate the trend and potential risk of EP in Northeast China during 1959–2017, especially in early and mid-summer (periods of high frequency of EP). Then, the relationships between EP and large-scale circulation over Northeast China in early and mid-summer are analyzed separately. The EP in Northeast China mainly presents positive trends in early summer but negative trends in mid-summer. Meanwhile, the EP with all the return periods presents apparently increasing trends in early summer, corresponding to more frequent EP events. Nevertheless, in mid-summer, the EP with 2-year return period decreases with location parameter, and the EP with 20-year, 50-year, and 100-year return periods slightly increases with scale parameter. The EP with 2-year return period occurs frequently in Liaoning Province, while the EP with 100-year return period is more likely to occur in Jilin Province and Heilongjiang Province. Moreover, the increase of the EP in early summer is mainly influenced by the northeast cold vortex; the effect of cold air on the EP is stronger in mid-summer, giving a clear explanation why the EP in mid-summer does not increase significantly. Overall, the outcomes of this study would be beneficial for the disaster prevention and mitigation in Northeast China. [ABSTRACT FROM AUTHOR]

Food availability is one of the dimensions of food security, and it is necessary to analyze the crop production scenario to estimate the availability of food in a region. Cropping sequence and cropping intensity indicate the seasonal crop production, thereby indicating the seasonal availability of food. Seasonal variation of per capita or per household availability of the cropped land determines the food security status of a given region. In Indian Sundarbans region, people's livelihood is seriously threatened by the food insecurity. The present study aimed to determine the seasonality of cropped land as well as the cropping intensities of Gosaba CD block of Indian Sundarbans during 2017–2018, 2018–2019 and 2019–2020 cropping years using Multi-dated Sentinel-2 data. Rule-based classification was applied for cropping sequence and cropping intensity mapping. Winter season cropped land was the lowest (< 16% of the village area). The area under crop–fallow–crop sequence (200% cropping intensity) decreased, while the area under crop–fallow–fallow (100% cropping intensity) sequence increased. Area under 300% cropping intensity gradually decreased. The average cropping intensity changed from 150% in 2017–2018 to 124% and 136% in 2018–2019 and 2019–2020, respectively. Large variation of the seasonal cropped land per household was estimated, and it became the worst during winter when it became less than 0.5 bighas (0.07 ha). Crop cultivation during dry season depended on the rainfall pattern and surface water availability. The present study successfully addressed the cropping scenario and food insecurity of the study area, and hopefully, it will help the planners and policy makers to take necessary actions for cropping intensification and ensuring food security in the Indian Sundarbans region. [ABSTRACT FROM AUTHOR]

Background: Water-scarce locations necessitate the deployment of creative and sustainable techniques for managing water for agricultural production. Field experiment was conducted at the Experimental Research Farm of National Research Centre, Nubaria region, Egypt to alleviate the harmful effect of water stress on the yield of Mediterranean barley varieties (Giza 125, Tombari, Ksar Megrine and Tamellat) by compost (0.0, 2.0, 4.0, 6.0-ton fed−1) and arginine application (0.00 and 1000 ppm) under deficit irrigation. The amounts of irrigation water applied were "900 and 450" m3 fed−1 to sufficient irrigation and deficit irrigation, respectively. Results: The greatest and most significant values of the chlorophyll values and relative water content values obtained at the treatment supplied with 6.0-ton compost fed−1 and sprayed with Arginine. There was a significant dramatic decrease in proline content with increasing compost application rates and treated barley plants by Arginine for all the studied barley varieties under both studied irrigation treatments. Increasing compost application rate is associated with significant increase in number of spike m−2 without or with arginine. Barley Tombari variety received 6.0-ton compost fed−1 gained changes to give a greatest significant value of grain (ton fed−1) under sufficient irrigation and Tamellat under deficit irrigation situation. The significant maximum values of the grain yield (1.96- and 2.09-ton fed−1) were attained at Tombari and Tamellat varieties which received 6.0-ton fed−1 compost with or without arginine under sufficient irrigation. The increases in compost rate increment changes to incremented grain yield values with arginine application more than untreated one. The greatest and significant grain yield was found at the treatment received 6.0-ton compost fed−1 with arginine foliar application. Conclusion: Compost application has an important role in maintaining greatest water use efficiency for plant and arginine application reported to contribute in reduction in destructive effects of a biotic stress thus their importance in increasing the barley production under water stress. [ABSTRACT FROM AUTHOR]

This research aimed at evaluation of a given land resource suitable for selected rain fed crops production (malt barley, wheat and teff) in Ambesh watershed. It also quantified suitable land and classified into the land mapping units (LMUs), and presents a land suitability map. Land suitability assessment (LSA) made using climatic condition, topography, soil physical and chemical properties as a major factor integrated with a multi criteria decision making (MCDM) analysis in Ambesh watershed. Fourteen composite soil samples were taken by categorizing the watershed into different land mapping units (LMUs) and analyzed in a soil laboratory. Climatic data, rainfall was obtained from two meteorological stations nearby to the study area. Temperature data derived from Landsat 8 satellite thermal bands data. Data obtained from the soil laboratory and others were finally analyzed using ArcGIS environment and priority estimation tool (PriEsT) software's. Weighted Sum Overlay was implemented to investigate the final LSA map of the watershed. Results revealed that LMUs, VRe–LPq and LPK.Pq–FLc LMUs has higher overall suitability for all the selected rain fed crops. However, LMUs (VRe–NTu and NTu–VRe) has lower overall suitability values particularly for S1 suitability class (0.05% and 10.6%, respectively). The least suitable LMU is VRe–NTu with 0.05% S1 suitability class and above 99% of the land laid under the suitability classes of moderately suitable, marginally suitable and not suitable for the selected land utilization types. Moreover, about 219.06 ha (17.76%), 217.6 ha (17.64%), 168.9 ha (13.7%), of land are highly suitable for malt barley, teff and wheat crop production, respectively. In conclusion, during MCDM, classifying the land into closer homogeneities (LMU) an important application of LSA integrated with remote sensing and GIS for a better decision making. Meanwhile, majority (above two third's) of the land in the watershed is under moderate and marginally suitable, it needs intensive land management activities to increase the land qualities and obtain high yields. LSA recommended before land utilization decision has to be made. It is also important to classifying the land into LMUs to make it more homogeneous for sample taking and reducing the prestigious soil laboratory analysis costs. [ABSTRACT FROM AUTHOR]

Keeping the relationship between economic development, humankind and the environment in balance has been one of the main challenges of economic policies and science in recent years. Such concerns are recurrent when it comes to climate change and the dependence of the economic process on finite resources. In this way, the present study aims to assess Greenhouse Gases (GHG) emissions from the agricultural and livestock production and the Rural Development Index (RDI), seeking to identify the spatial relationship of these two variables among Brazilian states in 2010. The methodology used to meet the objective was the Exploratory Analysis of Spatial Data (EASD), which incorporates spatial factors. The main results obtained show that both RDI and emissions are positively influenced by space. Different patterns of association were observed among Brazilian states: states characterized by low emissions and high RDI; high emissions and high RDI; and low emissions and low RDI. Most parts of the states with high emissions are relevant livestock producers. It indicates, thus, that investments in more sustainable practices for livestock production allied with the promotion of welfare shall be encouraged. Although being a time cut, this study brings evidence that high levels of rural development, in its broad sense, do not always mean high emissions rates, the latter depending more on the type of agricultural and livestock activity practiced. [ABSTRACT FROM AUTHOR]

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Crops need adequate mineral nutrition to ensure optimal growth and yield. Nitrogen (N) and phosphorus (P) are two major elements that are essential for crop growth. However, situations of N-P colimitation are frequent in agroecosystems. Hence, our ability to optimize crop production under these conditions depends on our ability to analyze and predict the response of crops to colimitation. Traditionally, agronomists rely on the law of the minimum (LM) to manage colimitation situations. This law states that crop growth is constrained by the most limiting element. In contrast, the multiple limitation hypothesis (MLH) argues that crops can adapt by balancing their resource allocation with the best compromise to maximize their growth. These two hypotheses result in contrasting growth response patterns. The aim of the present review is to identify the crop response pattern to N-P colimitation through an assessment of experimental results against a conceptual framework and to report the main mechanism involved in this interaction. Finally, an inventory of existing crop models handling N-P colimitation is presented and possible ways of improvement are proposed. This review allowed us to (1) remind of the published theories used to classify colimitation types, (2) highlight the fact that a large range of crops mostly showed MLH-response patterns, (3) report that the variability in crop response patterns is linked to pedoclimatic variation, (4) identify multiple mechanisms that may be involved in plant adaptation to N-P colimitation, (5) suggest that the interplay between the different mechanisms results in complex responses that are difficult to understand experimentally, (6) report that few models handle N-P colimitation and that most of them rely on the law of the minimum, and (7) recommend possible ways to improve model formalization for a better simulation of crop responses under N-P colimitation. [ABSTRACT FROM AUTHOR]

Compound droughts with low soil moisture (SM) and high vapor pressure deficit (VPD) pose significant threats to terrestrial carbon sink and agricultural production. However, the frequency and intensity of compound droughts and their adverse impacts on the carbon cycle remain highly uncertain. Here, we define and identify vegetation compound droughts (VCDs) when low SM and high VPD severely limit and adversely affect vegetation carbon uptake. We find frequent and severe VCDs and their considerable adverse impacts on carbon uptake in mid- and low-latitude regions, particularly in drylands. Risks of VCDs have been greatly underestimated as the widely adopted quantile-based approach identifies only 11% of VCDs and 26% of global GPP anomalies due to VCDs. The frequency and intensity of VCDs and their adverse impacts on carbon uptake are projected to increase further, irrespective of whether the CO2 fertilization effect on vegetation growth and photosynthesis is considered or not. These findings improve our understanding of current and future risks of VCDs and underline the importance of adaptation measures to cope with the adverse impacts of ever-increasing compound droughts. [ABSTRACT FROM AUTHOR]

Background: In Lebanon, poultry production is one of the major components of the agricultural sector; however, it suffers from increasing energy costs necessary to cover poultry heating requirements. This affects the profits of brooding farms, namely, small-scale farms in rural areas. Few studies have addressed the use of renewable energy in the poultry industry in Lebanon, with most having focused on modelling ventilation and air quality requirements in poultry houses. Therefore, there is a need to investigate the efficiency of renewable energy sources in providing heating requirements for poultry production. Accordingly, this study evaluates the performance of a solar-assisted, localized heating system in providing heat requirements for chicks in a renovated green poultry house in the Bekaa semi-arid rural region in Lebanon. For this aim, two brooding cycles were conducted during the warm and cold seasons in a greenhouse and were later replicated in a conventional poultry house. Results: The energy inputs in the green and conventional houses, respectively, were 33,995.39 and 40,656.97 MJ (1000 birds)−1 in the warm season, and 37,058.25 and 45,770.05 MJ (1000 birds)−1 in the cold season. Calculated energy efficiency values for the green and conventional poultry houses were, respectively, 0.58 and 0.50 in the warm season, and 0.46 and 0.41 in the cold season. The net return was negative for both systems and the benefit-to-cost ratio from broiler production was calculated to be 0.49 and 0.50 in the green and conventional houses, respectively. Life cycle cost analysis showed that adopting the green heating system in the studied farm would entail an 18.89% increase in cost over a period of 20 years as compared to the conventional system. Conclusion: It was concluded that poultry production is not profitable in small-scale farms in the studied area in Lebanon. The use of renewable energy might be more suited for large-scale broiler operations to achieve their purpose in reducing overall production costs. Optimization of the green system to fully satisfy the poultry energy requirements would render it more economically competitive. [ABSTRACT FROM AUTHOR]