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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]

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%. [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]

Seed health plays a pivotal role in profitable crop production. However, seed-borne pathogens deteriorate seed health and reduce the market value of crops. Curvularia lunata is an important fungus associated with seeds of several crops. In the present study, broken, discolored and unhealthy seeds were observed during the inspection of soybean germplasm entries indicative of a fungal disease. The suspected fungus was isolated from these seeds using blotter paper and potato dextrose agar. Purified growth of fungus on nutrient media was smooth, regular and conidia were curved which appeared to be C. lunata. DNA was isolated and Internal transcribed spacer (ITS) region was amplified. The amplicon (550 bp) of three isolates NBGCLI-III were sequenced. NCBI BLASTn confirmed assembled sequences as C. lunata. These sequences were submitted to NCBI GenBank with accession numbers OP093623-25. Soybean seeds were also subjected to culture filtrate of these isolates. Inoculated seeds showed reduced germination and the seedlings produced were abnormal as observed earlier when compared to untreated control. From infected seeds fungal DNA was isolated and ITS region was amplified and sequenced. The sequence results confirmed the presence of C. lunata. Hence, the present study has shown the deleterious effects of C. lunata on seed health in soybean and suggests the use of diseased-free seeds for better germination and vigorous crop. [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]

This study examined the nexus between climate change and food security in Sub-Saharan African Region (SSA). With focus on 30 countries within the region, the study employed the dynamic panel data analysis using the one-step and two-step system generalized method of moments (GMM) model. The time observed spanned from 2000 through 2019. The study found that increase in greenhouse gas emission would lead to an increase in prevalence of malnourishment rate, resulting in a decrease in food security in SSA. In addition, climate change and food price have a negative significant effect on food security, while income and food supply have a positive significant impact on food security in SSA. The findings also revealed that the decline in carbon emission is expected to boost agricultural supply and productivity, reduce the prevalence of malnourishment rate and promote food security. Thus, the study recommends that SSA region should be more deliberate about meeting its targets towards achieving zero net emission. Furthermore, the region should improve its domestic food production capacity by implementing policies that will support improvement in agricultural production in the region. [ABSTRACT FROM AUTHOR]

Policymaking and administration of national tactics of action for food security rely heavily on advances in models for accurate estimation of food output. In several fields, including food science and engineering, machine learning (ML) has been established to be an effective tool for data investigation and modelling. There has been a rise in recent years in the application of ML models to the tracking and forecasting of food safety. In our analysis, we focused on two sources of food production: livestock production and agricultural production. Livestock production was measured in terms of yield, number of animals, and sum of animals slaughtered; crop output was measured in terms of yields and losses. An innovative hybrid deep learning model is proposed in this paper by fusing a Dense Convolutional Network (DenseNet) with a Long Short-Term Memory (LSTM) to do production analysis. The hybridised algorithm, or A-ROA for short, combines the Arithmetic Optimisation Algorithm (AOA) and the Rider Optimisation Algorithm (ROA) to determine the ideal weight of the LSTM. The current investigation focuses on Iran as a case study. Therefore, we have collected FAOSTAT time series data on livestock and farming outputs in Iran from 1961 to 2017. Findings from this study can help policymakers plan for future generations' food safety and supply by providing a model to anticipate the upcoming food construction. [ABSTRACT FROM AUTHOR]

Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall. [ABSTRACT FROM AUTHOR]

The organic agricultural production system is considered to be the best alternative to the conventional system in order to solve agricultural externality problems. The adoption of such systems provide environmental, social, and financial benefits to the related communities. The related community may receive economic benefits although they might not recognize those benefits. The current paper examines prospective organic wheat production's positive impacts on Turkish economy. This research seeks to find the optimal cultivated land requirement to be allocated for organic wheat production, and contributes to the available literature by measuring environmental and social effects using the proxy values of regular wheat production in the country. Results dictate that the social optimum amount of conventional wheat production must be 1.3 million hectares. If the annual negative externality of wheat production, which is 227.5 USD/ha, is taken into account, the total annual external cost would be 1,416,061,536 USD/year. The importance of conversion and superiority of organic farming are stressed in the literature only at the micro-level or farm-level rather than the macroeconomic level. Macroeconomic results examined in the current paper complete micro-level studies in the context of agricultural externalities. The study indicates that macroeconomic efficiency of organic production is higher than the conventional system. However, it is suggested that a mild transition path be implemented for better land conversion in developing countries such as Turkey. The system of good agricultural practices may have some advantages for this path. [ABSTRACT FROM AUTHOR]

This paper investigates the environmental impact of agricultural systems used to convert sugarcane into non-centrifugal cane sugar (NCS) in the region of the Hoya del Río Suárez (HRS) at an average sugarcane mill in the region through a life cycle analysis (LCA). Based on an operational approach from crop to production, LCA considers the scenarios of different transportation methods (traditional beasts of burden and the newfangled gravity aerial ropeway based as well as self-propelled areal ropeway systems). This paper, to be best of our knowledge, is one of the first that deals to analyze LCA in the transportation systems of process of NCS production. Primarily, the LCA approach identifies the impact of each NCS production process using the Umberto NTX Software, and the environmental footprint 2.0 Midpoint as well as ReCiPe Endpoint Impact Assessment Methods, which help sugarcane producers understand the area being significantly affected. For example, it determines whether the human health, ecosystem, or natural resources are affected by comparing the possible sugarcane transportation systems used for panela production. The LCA conducted on NCS industry in HRS revealed that electric motors significantly affect human health in long term. This is associated with elements, such as selenium, barium, and manganese, which can remain in the environment over time due to the high demand of energy they consume. This study also demonstrates that the transportation system exhibits similar environmental burdens (the traditional beasts of burden: 54.56 points; the self-propelled aerial cableway system: 54.77 points; and the gravity-based aerial cableway system: 54.61 points), which indicates that sugarcane mill' technology and logistics must be improved. The identification of production points where the environmental burdens are more significant accelerated the discussions toward the best practices in the sugarcane and NCS industry, thus providing producers with process guidance toward prospective development lines within the industry. [ABSTRACT FROM AUTHOR]

Transport infrastructure is an important determinant of agricultural productivity. Using various new spatial data, the paper measures different types of transport accessibility and estimates their impacts in Ethiopia. The paper takes advantage of a historical event that Ethiopia, a landlocked country, ceased freight rail operations connecting its capital and the main seaport in the late 2000s. Using the substantial changes in transport accessibility, the spatial autocorrelation panel regression is applied to show that the proximity to close markets and the access to the port are of particular importance for agricultural production. The elasticity is estimated at about −0.05 to −0.13, depending on type of accessibility. It is also found that there are considerable spillover effects that come from the spatial autocorrelation errors, meaning that crop production at one place is affected by its neighborhood environment, possibly including land fertility and weather conditions. [ABSTRACT FROM AUTHOR]

This paper explores the under-appreciated role of evolving temperature variability in crop production in the overall relationship between crop production and climate change. Prior studies can be characterized by two broad approaches: estimation of the impact of climate change over some observed period and forecasting crop production levels under feasible climate scenarios. A standard method for predicting crop production under climate change involves regressing logged crop production on quadratic weather variables and region-level fixed effects (Lobell and Burke, I Agricultural and Forest Meteorology i , 2010). [Extracted from the article]

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community. [ABSTRACT FROM AUTHOR]

The study aimed to explore how participatory communication can be used as an instrument for interventions enhancing agricultural production to improve the livelihoods of rural people. Specifically, it was designed to explore the nature of participatory approaches in the Ethiopian agricultural sector. The top-down approach that prevailed in the past lost its faith, and consequently, stakeholders practiced a participatory communication approach. Thus, explorative qualitative research was applied through focus group discussions and in-depth interviews to collect data. Data were transcribed, documented, coded, and thematically analyzed based on recurring themes. The findings show that if properly implemented, development agents and agricultural experts believe that participatory communication is instrumental in mobilizing the community. However, the routine does not allow development agents and experts to use the participatory approach. Besides, farmers are not given sufficient time due to insufficient interaction and communication on agricultural issues. Farmers and development agents have no ongoing, programmed, and frequent contact. The communication between development agents and higher-level agricultural experts is mainly top-down. Provincial and regional experts do not regularly visit and observe farm sites; they depend on the monthly paper report to execute modifications to the annual plan. There is no practical obligation with the consensus that demands farmers, development agents, and experts accomplish their tasks and duties on time with maximum effort. No dialogical session is available for the community to discuss their matters. Therefore, we suggest stakeholders use a multilevel and inclusive intervention in rural agriculture. [ABSTRACT FROM AUTHOR]

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.Article Highlights: This article aims at the creation of an efficient recommendation model for fruit cultivation in soil medium by analyzing the soil nutrient contents and the existing climatic conditions. The most suitable fruit plant corresponding to the existing conditions and soil type is recommended for an enhanced yield of the plant. Three climatic parameters, i.e., temperature, humidity, and rainfall; along with four soil-based parameters, i.e., pH, N content, P content, and K content are considered as the required growing condition for eleven varieties of fruits. To ensure enhanced accuracy, a hundred entries for each fruit type is entered in the dataset. The created dataset is then divided in the proportion of 7:3 as training data: testing data and Light Gradient Boosting Machine (Light GBM) model is applied to the created dataset. The correlation of all the parameters is checked for an efficient recommendation of fruits. Finally, the model is evaluated and its efficiency is checked. [ABSTRACT FROM AUTHOR]

This paper studies the impact of sectoral productivity growth on welfare in Sub-Saharan Africa. Using the analytical framework of a DSGE model, the main finding is that, for the estimated values of structural parameters, the allocation of scarce resources to the tradable agricultural sector for boosting productivity leads to a greater increase in overall welfare than would be the case if they were allocated to the non-traded goods sector. [ABSTRACT FROM AUTHOR]

This paper presents a multipurpose optimization algorithm (MOA) to optimize crop patterns under climate change, minimizing water use and maximizing crop revenue while enforcing food security and regional water security constraints. An application of the MOA yields a total of 12 Pareto fronts for 20-year horizons centered on 2030, 2050, 2070, and 2090 under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5, each of which is associated with specific land use conditions. The results show that crop production must increase due to population growth. However, climate projections for the study region in eastern Iran indicate unsuitable conditions to support the incremental production. This paper's optimization results show that 89%, 73%, and 48% of optimal crop production are achievable considering food-safety constraints in 20-year periods centered on 2050, 2070, and 2090, respectively. This paper's results indicate that revenue would increase, water use would decline, and environmental sustainability would be reached in the study area under the optimized cropping patterns. [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 global farming conditions have gone profound mutations that steadily increased vulnerability among smallholder farmers. As consequence, rural households have set mechanisms of livelihood adaptation in order to preserve consumption requirements and secure the family living. They define livelihood through a complex system and interactions, taking place across scales that lead to emergent properties and self-regulatory mechanisms. This paper provides a detailed account on how traditional agriculture in Burundi, has evolved over time, what triggered the changes and how they have affected the household food security and farmer's attitude in the communities. The country is an agricultural based economy faced by land constraints, market and policy failures, and rapidly changing local climate patterns. The paper gives a historical overview of the organisational and functional features of the agrarian system, and the adaptive changes in farming practices. This brings a good understanding of the relationships between them and their implication on farmer's behaviour and livelihoods. Results show that despite the deteriorating conditions, farmers have managed, over time, to adapt agricultural production to new opportunities and constraints. The paper concludes by showing that farmer's adaptation is not everlasting. The findings of this study highlight that endogenous adaptation has reached some limits in Burundi. Today, the rates of conflicts over resources, poverty and food insecurity among households are very high. Stringent policy support is needed to help farmers sustain agricultural intensification and restore the country's self-sufficiency in food production. [ABSTRACT FROM AUTHOR]

Many public debates about the societal significance and impact of agriculture are usefully framed by Paul Thompson's distinction between the "agrarian" and the "industrial vision." The key argument of the present paper is that the ongoing debate between these visions goes beyond academic philosophy and has direct effects on the political economy of agriculture by influencing the scope of rent-seeking activities that are undertaken primarily in the name of the agrarian vision. The existence of rent-seeking activities is shown to reflect the fact that the agrarian vision is not universally supported, which is certainly true of the industrial vision as well. The key argument of the present paper is that these two philosophical visions of agriculture are not radically incongruent. Rather, they share a common ground within which they are even mutually supportive. If agricultural policy making is oriented toward this common ground, it may reduce overall dissatisfaction with the resulting institutional regime of agricultural production. Such an agricultural policy may also stimulate the emergence of new business practices that not only enable efficient agricultural production but also minimize negative ecological impact and preserve cultural landscapes. [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]

Agricultural production systems in semi-arid areas are vulnerable to a myriad of risks. Using a systems approach of risk framework and a mixed-methods research design, this paper sought to explore selected biophysical and socio-economic risks that contributed to vulnerability of agricultural production systems in the semi-arid lowlands of Mwanga District, Kilimanjaro Region, Tanzania. Despite the lack of statistically significant relationships between amounts of rainfall and crop production in the district, 30 focus group participants perceived that spatial and temporal changes of rainfall distribution as coupled with increased crop pest/disease outbreaks and soil loss contributed to vulnerability of agricultural production systems in terms of frequent crop failure and famine particularly amongst farmers who practised rain-fed farming in the semi-arid lowlands. Furthermore, participants perceived that crop production and yields were negatively influenced by poor marketing and institutional structures and that crop production and yields were negatively influenced by farmers' poor access to appropriate technologies including seeds, fertilizers, agrochemicals, agricultural machinery and infrastructure including modern irrigation schemes and all-weather roads. By way of conclusion, reduced vulnerability of agricultural production systems calls for integrated enhancement of farmers' capacity in addressing the biophysical, agro-industrial and institutional risks. [ABSTRACT FROM AUTHOR]

Agricultural sectors worldwide are under direct threat from climate change conditions. In Iran, agricultural production has decreased due to droughts originating in an increase in annual maximum temperatures—with the corresponding increase in crop respiration and evapotranspiration—and a decrease in accumulated precipitation. Based on a static computable general equilibrium approach, this paper reports implication for the Iranian economy from the effects of climate change on agriculture––as modeled through three scenarios relying on assumptions about the magnitude of continued reduction in total agricultural production. Reductions of 6%, 12%, and 18% in total agricultural production reasonably cover the range of impacts that climate change is expected to impose on the Iranian agricultural sector––under the assumption that no behavioral adaptations or policy interventions are in place. Our simulations suggest that effects on the Iranian economy imply a reduction in GDP ranging between 3.7 and 6.3%. In addition, 5–17% of labor moves away from the agriculture sector––this labor relocation occurs due to declining agriculture incomes. Findings illustrate that climate change will reduce households' consumption and income in all economic sectors, particularly among rural households. We suggest that policies in Iran should focus on improving cultivation methods to save water resources and alleviate the expected effects of climate change. The current study's outcomes are helpful for policymakers, especially in countries with water scarcity. [ABSTRACT FROM AUTHOR]

Jilin Province is one of the important grain-producing regions in China, the frequent drought and waterlogging events in this region have seriously impacted local agricultural production, therefore, it is particularly necessary to explore the spatiotemporal variations in drought and waterlogging and how they effect on maize yields. In this paper, we use the daily meteorological data recorded at 27 meteorological stations in Jilin Province from 1961 to 2020 to calculate the modified crop water deficit index (mCWDI), which were based on the daily crop coefficient (Kc) corresponding to different growth stages for each station. The spatiotemporal evolution processes of drought and waterlogging during the growing season in Jilin Province were analysed by the linear regression model, and the impacts of drought and waterlogging conditions on maize yields in Jilin Province under different growing seasons were quantified by means of the correlation analysis and multiple regression analysis methods. The results showed that the effective precipitation during the whole reproductive period showed a spatial distribution pattern of decreasing from southeast to northwest, with precipitation totals ranging from 335.02 to 677.38 mm, while the spatial distribution of the water demand showed the opposite trend. The south-eastern region of Jilin Province was in a state of water surplus, while the precipitation in other areas could not meet the water requirements of maize, resulting in decreasing drought frequency trends from northwest to southeast and from the early stage to the developmental stage of maize; in addition, increasing trends were observed in the middle and late reproductive stages of maize. The waterlogging frequency in the south-eastern region showed the spatial distribution characteristics of being higher in the early and late reproductive periods and lower in the development period of maize, and the growth rate of the maize-waterlogging frequency was higher in central Jilin Province than in other areas. Moreover, drought showed a more significant negative correlation with the maize yield in Jilin Province, while waterlogging showed a positive correlation. The relative importance results show that drought has a greater impact on maize yields than waterlogging, and the impacts of drought and waterlogging events on maize yields are mainly concentrated in the middle and late growth periods. The findings could inform the development of contingency plans for farmers to minimize crop losses and ensure food security in the region. [ABSTRACT FROM AUTHOR]

China is the largest soybean-consuming country in the world, but its self-sufficiency rate (SSR) of 16% is very low and it therefore has to heavily rely on imports. To solve the soybean dilemma in China, it is necessary to examine the maximum amount of soybean that could be grown on the land currently used, how much land could reasonably be used to expand soybean acreage, and whether China could sustainably increase soybean self-sufficiency to reduce the risks of import reliance. To answer these questions, our paper presents a high-resolution spatial analysis of potential soybean production in China using primary data of weather and crop production practices that govern this potential. We employed a "bottom-up" scaling protocol to estimate gaps between potential yield with optimal management and current yields in three major soybean-planting regions, namely, Northeast China, Central China, and South China. We found that current soybean yield gap (Yg) in China is 49% and 45% of potential yield under irrigated and rainfed cropping systems, respectively. By closing the yield gap, Northeast China could provide additional soybean production equivalent to 32% of the current national total. Our results show that SSR could only be increased to 21–23% in 2030 by Yg closure alone but could be increased to a maximum of 52% by combining Yg closure and a reasonable area expansion. Even so, at least 61.08 million tons of soybean accounting for 38% of global soybean trade would still need to be imported to meet future domestic demand. We discuss strategies for soybean production increase based on Yg closure in the most valuable areas and cropland expansion in a sustainable manner in order to increase SSR as well as lessen the import pressure on the global market. [ABSTRACT FROM AUTHOR]

A crucial aspect analysed during the last years, aiming to improve sugarcane production, is the impact of climate change on sugarcane productivity. One of the strategies to mitigate climate change's impact on sugarcane yield is the development of new varieties known to positively affect crop production. This paper analysed how climate change impacts sugarcane production regarding the different planted varieties. Data regarding sugarcane harvest were collected from a cooperative in the south of Goiás state—Brazil, the second biggest national sugarcane producer. Results indicate that climate impact on sugarcane yield is irrelevant when controlling for different varieties. Considering the results presented in this work, the Brazilian government should keep the incentives for the development of new sugarcane varieties and, at the same time, spur sugarcane producers to use the new sugarcane varieties. The results imply that if the variety is correctly chosen, sugarcane can be produced without harming the environment, contributing to reaching SDG 15. Moreover, it is less probable that an adverse climatic event will destroy the planted area, preventing sugarcane producers from severe loss and contributing to achieving SDGs number 1 and 2. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the major threats to the economy and food security of a country. Detection of such diseases timely and accurately on large scale is prone to human error. Techniques like machine learning (ML) and deep learning (DL) provide alternatives to build automated models that can detect such diseases efficiently. Several researchers have used deep learning techniques for plant disease detection. Fruit crops are major part of agricultural production. This paper proposes a lightweight and accurate deep learning model based on convolutional neural network (CNN) for the detection of diseased leaves in banana, guava and mango fruit crops. The model is proposed with the concept of feature reuse at three different levels. The model was trained using open database which consists of eight distinct classes of diseased and healthy leaves from three different fruit species. From the experiment, it was found that the model uses 101,000 numbers of parameters and achieves 99.14% success rate for disease leaves identification. Also, it outperforms 15 different state-of-the-art pre-trained models, namely VGG16, VGG19, ResNet50, ResNet50V2, ResNet152, ResNet152V2, InceptionV3, InceptionResNetV2, Xception, DenseNet121, DenseNet169, DenseNet201, MobileNetV2, ConvNeXtBase and ConvNeXtLarge, in terms of both accuracy and model complexity. [ABSTRACT FROM AUTHOR]

Floods affect more than 21 million people yearly, principally in poor countries. Using 3-year panel microdata from Tanzania and satellite flood data, this paper investigates the impacts of two successive large floods on households' value of crop production, income, expenditures and life satisfaction. Using a kernel weighting difference-in-differences approach, we find a 34% decrease in the value of crop production for households living in affected villages or clusters in the year following the shock. We find no effects on total expenditures or child nutrition, but a significant negative effect on self-employment income and a persistent decrease in life satisfaction. Finally, access to safety nets or transfer income, and to forests in a village appears to have important mitigating effects. [ABSTRACT FROM AUTHOR]

It is widely expected that agriculture would contribute to farmers' food security and nutrition in developing countries. However, studies that directly explore the link between agricultural productivity and micronutrients intake by farmers are scarce. In this paper, we contribute to filling this gap by exploring two key channels by which agricultural production can influence dietary outcomes: a food consumption pathway and a cash revenue pathway. To achieve this, we used three-years panel data of rice farmers collected in the Vakinankaratra region of Madagascar. The results suggest that rice yield is positively and significantly associated with farmers' calorie and micronutrients intake, though the observed elasticities are low. Secondly, raising rice yield has a positive significant impact not only on rice consumption but also on the share of the output sold and the cash revenue from rice sales. Lastly, the results suggest that households with higher cash revenue from rice sales purchase more nutritious foods. Therefore, we conclude that the market represents the channel through which increased rice yield translates into improved micronutrient intake. The findings of this study imply that in order to improve farm households' nutrition through agricultural production, interventions that target yield enhancement should be accompanied by market access measures. [ABSTRACT FROM AUTHOR]

The scientific literature dealing with food security is vast and fragmented, making it difficult to understand the state of the art and potential development of scientific research on a central theme within sustainable development. The current article, starting from some milestone publications during the 1980s and 1990s about food poverty and good nutrition programmes, sets out the quantitative and qualitative aspects of a vast scientific production that could generate future food security research. It offers an overview of the topics that characterize the theoretical and empirical dimensions of food security, maps the state of the art, and highlights trends in publications' ascending and descending themes. To this end the paper applies quantitative/qualitative methods to analyse more than 20,000 scientific articles published in Scopus between 2000 and 2020. Evidence suggests the need to find more robust links between micro studies on food safety and nutrition poverty and macro changes in food security, such as the impact of climate change on agricultural production and global food crises. However, the potential inherent in the extensive and multidisciplinary research on food safety encounters limitations, particularly the difficulty of theoretically and empirically connecting the global and regional dimensions of change (crisis) with meso (policy) and micro (individual behaviour) dimensions. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the economy-wide and regional effects of climate change-induced productivity decrease in Brazil. Our methodological framework was based on the General Equilibrium Analysis of the Brazilian Economy Project—PAEGDyn, a dynamic CGE model. The results show that the projected falls in agricultural productivity impose reductions in the performance of Brazilian GDP over time. Even with the use by other sectors of the economy of the factors unemployed in agriculture, there is no intersectoral compensation in economic production over time able to bring it back to the reference trajectory. In addition, the impact will be greater in warmer and poor regions, which depend on agriculture and present greater income inequality, accentuated by the free mobility of production factors within the national border. Therefore, the main implication of this study is the need to allocate scarce resources for adaptation and mitigation policies primarily for these regions, including broadly stimulating economic development with more income distribution. This will allow these regions to protect themselves by making investments in new technologies and modern infrastructure for the agricultural sector. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents an improved methodological approach for studying life cycle impacts (especially global warming) from changes in crop production practices. The paper seeks to improve the quantitative assessment via better tools and it seeks to break down results in categories that are logically separate and thereby easy to explain to farmers and other relevant stakeholder groups. The methodological framework is illustrated by a concrete study of a phosphate inoculant introduced in US corn production. Methods: The framework considers a shift from an initial agricultural practice (reference system) to an alternative practice (alternative system) on an area of cropland A. To ensure system equivalence (same functional output), the alternative system is expanded with displaced or induced crop production elsewhere to level out potential changes in crop output from the area A. Upstream effects are analyzed in terms of changes in agricultural inputs to the area A. The yield effect is quantified by assessing the impacts from changes in crop production elsewhere. The field effect from potential changes in direct emissions from the field is quantified via biogeochemical modeling. Downstream effects are assessed as impacts from potential changes in post-harvest treatment, e.g., changes in drying requirements (if crop moisture changes). Results and discussion: An inoculant with the soil fungus Penicillium bilaiae has been shown to increase corn yields in Minnesota by 0.44 Mg ha−1 (~ 4%). For global warming, the upstream effect (inoculant production) was 0.4 kg CO2e per hectare treated. The field effect (estimated via the biogeochemical model DayCent) was − 250 kg CO2e ha−1 (increased soil carbon and reduced N2O emissions) and the yield effect (estimated by simple system expansion) was − 140 kg CO2e ha−1 (corn production displaced elsewhere). There were no downstream effects. The total change per Mg dried corn produced was − 36 kg CO2e corresponding to a 14% decrease in global warming impacts. Combining more advanced methods indicates that results may vary from − 27 to − 40 kg CO2e per Mg corn. Conclusion and recommendations: The present paper illustrates how environmental impacts from changes in agricultural practices can be logically categorized according to where in the life cycle they occur. The paper also illustrates how changes in emissions directly from the field (the field effect) can be assessed by biogeochemical modeling, thereby improving life cycle inventory modeling and addressing concerns in the literature. It is recommended to use the presented approach in any LCA of changes in agricultural practices. [ABSTRACT FROM AUTHOR]

It is argued in this paper that agriculture plays a here-to-for unrecognized role in the process of structural change. In very poor countries agriculture is the key sector in the economy and agricultural prices greatly influence the overall price level in such an economy. It is hypothesized that if agricultural productivity grows faster than manufacturing productivity this will, under certain conditions, cause the price of the former to fall relative to the latter which in turn implies that the overall price level in the economy should fall, ceteris paribus. This results in a fall in the real effective exchange rate, ceteris paribus. This decline increases the competitiveness of producing tradable goods, in particular manufactured goods. Thus the process of structural change (shifting employment and resources out of agriculture) becomes easier. These hypotheses are empirically analyzed utilizing a data set for twelve Asian countries for an extended time period. [ABSTRACT FROM AUTHOR]

Salinity stress is a serious abiotic stress that affects crop quality and production. Rhizospheric microbes have immense potential in synthesizing and releasing various compounds that regulate plant growth and soil physicochemical properties. The aim of the present study was to evaluate the efficacy of indole-3-acetic acid (IAA)-producing rhizobacteria as biofertilizers under salt stress. Among the isolated strains from various soil samples, Bacillus megaterium strain PN89 with multifarious plant growth-promoting traits was selected and used as a monoculture and co-culture with two other standard strains. The plant promoting activity was evaluated using the paper towel method and pot test to observe the effects on the early stage and vegetative growth of wheat (Triticum aestivum L.). The treatment using PGPR strain presented noticeable but varying effects on plant growth under salt stress, that is, PGPR treatment often displayed a significant increase in germination percentage, root and shoot length, and other growth parameters of wheat compared to those in the non-inoculated control. Thus, these results suggest that B. megaterium PN89 can be applied as a bio-fertilizer to alleviate salt stress in T. aestivum. [ABSTRACT FROM AUTHOR]

Background: This paper aims to analyze the possibilities of the agricultural sector of the Western Balkan to assess compliance with the European Green Deal, which provides for the implementation of activities, which should enable the transition to sustainable agriculture and climate change mitigation. This paper is among the first to present the causality of agriculture and climate change (status, mitigation, and perspectives) in general and in light of the European Green Deal for the Western Balkan territory. Main text: Agricultural production is a leading industry in the Western Balkan. Climate change and predictions that temperatures will increase by 4 °C in the coming decades pose a risk not only to agricultural production but also to the safety of the population, because agriculture is the main source of income for a significant part of it. Uncontrolled floods and droughts caused by climate change are a particular danger for agriculture and human existence. This paper demonstrates that agriculture in the WB can be considered critically affected by climate change. Conclusions: Unless appropriate measures are taken and risk management for water resources and agriculture is improved, there will be a further decrease in precipitation and an increase in dry days by 20%. Such a scenario endangers not only the already vulnerable climate sustainability and biodiversity of the region but also the existence of a population employed in agriculture and the contribution of the agricultural sector to the gross domestic product. However, future planning based on the Common Agriculture Policy (CAP) and European Green Deal, the adoption of a related regulatory framework, the establishment and regular monitoring of supporting financing mechanisms, regional cooperation, and improving risk management (with emphasis on the local level) can mitigate the present impact and decrease the expected negative impact of climate change on agriculture and biodiversity in the WB region. [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]

Global warming is a life-threatening risk to mankind and its survival; to combat this risk, the considerable contribution of renewable energy cannot be overlooked for sustainable growth globally. The aim of this paper is to scrutinize the threshold level and asymmetric connection among renewable energy consumption, foreign direct investment, financial inclusion, and agricultural productivity in dissimilar regimes of the different income levels of 123 countries from 1995 to 2019 by applying an advance technique PSTR (panel smooth transition regression) model. The PSTR model results imply that the connection between renewable energy consumption and agricultural productivity at all the estimates is non-linear. Moreover, in all countries, there is a positive and significant connection among renewable energy consumption, foreign direct investment, financial inclusion, and agricultural productivity in both low and high regimes, except the carbon emission, which has a negative and significant impact on agricultural productivity. Based on the results of this study, the recommendations are as follows: (i) to increase renewable energy consumption, efficient-energy resources should be used by farmers for the agricultural process; (ii) the dependence on non-renewable energy resources should be minimized and shifted towards natural and renewable resources. [ABSTRACT FROM AUTHOR]

A long-standing item of interest in Canadian economic history is the "agricultural crisis" that apparently plagued the large colony of Quebec during the first half of the nineteenth century. One particularly resilient explanation of the crisis claims that cultural conservatism made the colony's French-Canadian population reluctant to embrace modern farming techniques developed in Britain and the US. This has been supported through comparisons with the English farmers in the colony. Using data from the census of Quebec in 1851, this paper shows that there was no such reluctance. French-Canadian farmers were no less likely to adopt "scientific" farming techniques than English-Canadian farmers in the region. [ABSTRACT FROM AUTHOR]

This paper analysed the effect of freshwater withdrawals and management on agricultural and industrial sectors productivity in the emerging market economies. The auto-regressive distributed lag model and the panel analyses were employed in our estimations. Our result revealed that Brazil had better water use efficiency in agricultural production with annual withdrawals which contribute significantly and positively to the increase in crop and livestock index. In contrast, annual withdrawals for agriculture were considered to be least efficient in Russia, followed by China and India, although, in South Africa, the result suggested an insignificant positive effect in the incremental index. Furthermore, our analysis revealed that freshwater withdrawals have a significant positive impact on industrial outputs in South Africa. Similarly, water withdrawals were positively related to industrial sector productivity in China and Russia. Brazil and India appear to be the least efficient countries where withdrawals impacted negatively (and significantly for Brazil) on industrial sector outputs. Our panel analyses showed that freshwater withdrawals were positively associated with crop and livestock production index and industrial outputs in the BRICS economies. However, the magnitude of the impacts was only significant for the industrial sector. Moreover, investments and private participation in water and sanitation projects impacted significantly and positively in productivity in both sectors. [ABSTRACT FROM AUTHOR]

This paper established an open general equilibrium model to study the effect of the change of rural–urban migrants' remittances rate on agricultural pollution. We found that the increase of migrants' remittances rate would not influence the output of agricultural pollution factor production sector or the agricultural pollution in the capital specific case; and in the mobile capital case, the increase in the rural–urban migrants' remittances rate will decrease the output of the agricultural pollution factor production sector and then decrease the agricultural pollution. In addition, we investigated the numerical characteristics of the effects of the change in the rural–urban migrants' remittances rate on the relevant variables, and we also found that the increase in the migrants' remittances rate would increase the national income and improve the level of gross national welfare under certain conditions, no matter in the capital specific case or in the mobile capital case. [ABSTRACT FROM AUTHOR]