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Bamboo has being used as alternative raw material for construction, reinforcing fibers, paper production, among other applications. Although its recognized potentials as raw material, there are doubts about its environmental performance compared to traditional wood-based products, including paper production, which hinders bamboo-based paper plants in large scales. This study aims to assess the environmental performance of producing office paper from bamboo. Emergy synthesis (with 'm') and global warming potential indicators are calculated and compared with the traditional eucalyptus paper-based production. Results highlights the importance in including the renewability fractions of each input resources into emergy calculations for production systems with high human-labor intensity such as the bamboo agricultural production. Office paper produced from bamboo has similar renewability (28%), moderate environmental load (3.23 vs. 2.49), and emergy unsustainability (0.34 vs. 0.60) compared to paper produced from eucalyptus, but bamboo showed lower performance for global efficiency (568 vs. 442 sej/ton paper), emergy yield (1.09 vs. 1.49), and emergy investment (10.79 vs. 2.05). Focusing on global warming potential, office paper produced from bamboo releases 98 kgCO 2 eq. /ton paper compared to 56–267 kgCO 2 eq. /ton paper for eucalyptus. Notwithstanding, bamboo-based office paper demands four times more land area of agricultural production than eucalyptus, but it has a positive social aspect by requiring higher amount of direct human labor. This work shows the advantages of using eucalyptus rather than bamboo in producing office paper from an emergy and land demand perspectives, while global warming can still be considered inconclusive. Future efforts should consider a quantitative and qualitative analysis of human labor availability in both systems, as well an economic analysis to support discussions towards more sustainable office paper production from different raw materials. • Environmental load of bamboo and eucalyptus for office paper production are compared. • Bamboo has lower emergy performance than eucalyptus. • Including the renewability fraction of labor and services better reflects emergy results. • Bamboo paper emits 98kgCO2eq/ton, eucalyptus paper emits 56–267kgCO2eq/ton. • Bamboo paper needs 4x land compared to eucalyptus and requires more human labor. [ABSTRACT FROM AUTHOR]

A completely lab-made paper/graphene-based 3D pop-up device (3D-PP) for the analysis of carbaryl (CBR), a pesticide widely used in crop production, is proposed. The device is composed of a nitrocellulose sensor equipped with reduced graphene oxide (rGO) and a foldable 3D paper pop-up. The rationale of the device is based on 3D and 2D configurations to allow the whole analytical procedure. In the 3D arrangement sample pre-concentration and hydrolysis occur; after folding (2D), contact with the complementary nitrocellulose-based sensor is ensured. The sensor, constituted by nanostructured graphene film, allows to obtain the necessary electroanalytical performance. 3D-PP has been shaped and nanostructured using a CO 2 -laser plotter and finished using low-cost benchtop instruments, ensuring in-series manufacturing of the device modules. 3D-PP includes the main steps required for CBR determination, i.e. (i) pre-concentration/purification, (ii) hydrolysis, (iii) medium neutralization, and (iv) electrochemical detection. 3D-PP was challenged for CBR quantification in model solutions and different grain samples. The response linearity (1.5–33 μM) and LOD (0.4 μM) obtained ensures CBR determination at the European-law admitted maximum residue levels (MRL). As proof of applicability, to simulate contaminates samples, different grains (i.e. soft and durum wheat, kamut, and barley) were spiked, before extraction, with CBR at three concentration levels corresponding to 0.5x, 1x, and 1.5x MRL; in all cases, quantitative and reproducible recoveries were obtained (93–109%; RSD ≤ 6%, n = 3). 3D-PP is produced using cellulosic components and solvent-free approaches, includes all CBR analysis steps, and returns reliable results in 15 min, resulting portable and usable with a smartphone [Display omitted] • A paper/graphene-based device for integrated carbaryl (CBR) analysis is proposed. • CO 2 -laser was used to shape and nanostructure the paper-based platform. • Complete CBR analysis is strategically enclosed in a 3D foldable paper pop-up (3D-PP). • 3D-PP design and sensing ability allow CBR residues analysis at EU-law limits. • 3D-PP analysis ability was proven in soft and durum wheat, kamut, and barley. [ABSTRACT FROM AUTHOR]

Green hydrogen production from biomass is an eco-friendly and sustainable solution for reducing carbon emissions and promoting sustainable development. This paper presents potential green hydrogen production from available biomass resources, including agricultural crops and livestock waste in Kazakhstan in 14 regions. The study explores the potential of biomass-to-hydrogen production in Kazakhstan, including the availability and quality of biomass resources and environmental sustainability. The gasification process is chosen as the most efficient technology for biomass resource consumption to obtain green hydrogen. This is the first study in Kazakhstan contributes assessment of the geographic distribution of available biomass resources in the country. The total amount of green hydrogen production from agricultural waste is 432 tonnes/km2/year. Turkestan, Kyzylorda, Almaty, East Kazakhstan, Zhambyl, and North Kazakhstan are listed as regions with the highest green hydrogen production potential with an annual 122.2, 71, 57.6, 35.3, 29, and 28.5 tonnes/km2/year, respectively. Annual green hydrogen production from livestock waste is 5262 × 10−6 tonnes/km2/year which equals to 856 tonnes/year. The top three three regions with H 2 production are Turkestan, Almaty and East Kazakhstan regions with 208, 112.5, and 93 tonnes/year, respectively. The results provide a perspective contribution for different regions to the sustainable energy future and carbon neutrality in the country. • Estimation of H 2 production potential from biomass resources in Kazakhstan. • Gasification technology appears as the most promising alternative for H 2 production. • First study in Kazakhstan assesses the geographic distribution of available resources. [ABSTRACT FROM AUTHOR]

• PtCu 3 alloy nanocrystals with an excavated rhombic dodecahedral shape were successfully fabricated by a hydrothermal method. • This material showed high peroxidase-like activity and could be used for UV–Vis and colorimetric detection. • Paper strips could be made for colorimetric detection of acetylcholinesterase (AChE) and organophosphorus pesticides (OPs). • By simply attaching to the peel of apple, pear and peach, the paper strips can be successfully used for the determination of OPs. • This work can make a positive contribution to food safety warning. Nowadays, the widespread use of organophosphorus pesticides (OPs) in agricultural production leads to varying degrees of residues in crops, which pose a potential threat to human health. Conventional methods used in national standard for the detection of OPs in fruits and vegetables require expensive instruments or cumbersome sample pretreatment steps for the analysis. To address these challenges, in this work, we took advantage of the peroxidase-like activity of PtCu 3 alloy nanocrystals (NCs) for a colorimetric and smartphone assisted sensitive detection of OPs. With the assist of a smartphone, the concentration of OPs on the peel of fruits could be obtained by comparing the B/RG value (the brightness value of blue divided by those of red and green) of a test strip with a calibration curve. This work not only provides a facile and cost-effective method to detect pesticides but also makes a positive contribution to food safety warning. [ABSTRACT FROM AUTHOR]

While chemical fertilizers and pesticides have significantly enhanced crop productivity per unit of land in China, the excessive utilization of these chemicals has led to a range of environmental issues that are not conducive to the sustainable development of China's agriculture. Controlling the use of fertilizers and pesticides in crop cultivation is therefore necessary and has been adopted as an action plan since 2015 in China. However, the effects of fertilizer and pesticide reductions have not been systematically investigated. This paper explores how yields and land use for rice, wheat, corn, and soybean cultivation shift when reducing their fertilizer and pesticide inputs using China's provincial crop-land integrated partial equilibrium model (the CPMCL model). Simulation results for different scenarios show that, among the studied crops, rice and wheat exhibit greater adaptability than corn and soybeans. Reducing the use of fertilizers and pesticides within the interval from 0% to 3.5% may increase the total demand of land resources for crop production from 0.220% to 4.21%. Limited land resources will be allocated to more profitable crops to maximize output, which means the land use for wheat production may increase over 20% while for rice, corn and soybean may decrease around 4% under the shock of fertilizer and pesticide reductions. Merely reducing fertilizer and pesticide inputs in crop cultivation may exacerbate the conflict between crop production and land resource conservation in China, which means the technology and efficiency of fertilizer and pesticide use needs to be improved. The findings of this paper can help formulate more province-specific and crop-targeted plans to properly reduce fertilizer and pesticide use, thus promoting sustainable land use while ensuring food security and protecting the environment. • China's provincial PE model integrates crop production and land use is formulated • Interrelationship between chemical inputs, land inputs, and crop yields is revealed • Impacts of fertilizer and pesticide reduction are analyzed nationally and provincially [ABSTRACT FROM AUTHOR]

• This paper presents a Bayesian multi-task learning model for pest detection. • The model estimates size of aphids, which is an indicator of infestation severity. • It also quantifies the predictive uncertainties enabling confident decisions. • It performs thorough audit studies that offer insight for future model refinements. Aphids are persistent insect pests that severely impact agricultural productivity. The detection of aphid infestations is critical for mitigating their effects. This paper presents an artificial intelligence approach to detect aphids in crop images captured by consumer-grade RGB imaging cameras. In addition to detecting the presence of aphids, the size of the aphid is an important indicator of infestation severity. To address these, we present a Bayesian multi-task learning model to detect the presence of aphids and estimate their size simultaneously. Our model employs a joint loss function, combining a classification loss and a customised size loss. The classification component aims to identify images containing aphids, whilst the customised size loss function estimates the size of the aphids. The latter is specifically designed to account for discrepancies between the estimated and actual ground truth sizes, enhancing the accuracy of the size estimation. The model utilizes a ResNet18 backbone, ensuring robustness and adaptability across various conditions. The proposed model was evaluated using an agricultural pest dataset consisting of images of corn, rape, rice, and wheat crops. It achieved aphid presence detection accuracies of 75.77%, 66.39%, 70.01%, and 59% for corn, rape, rice, and wheat images, respectively. An in-depth evaluation of predictive uncertainties revealed areas of high confidence and potential inaccuracies for both size and presence of aphids in images, offering insight for future model refinement. We also conducted an ablation study to thoroughly analyse the contributions of each component in proposed model. Our model offers a valuable tool that can be used in pest management strategies for facilitating more sustainable and efficient agricultural practices. [ABSTRACT FROM AUTHOR]

Under the dual challenges of food security crisis and environmental costs, promoting agricultural green development (AGD) is an urgent need. Agricultural cleaner production place-based policy (ACPBP) is an important tool for promoting AGD. Based on the entropy value method to measure the AGD, DID is applied in this paper to investigate whether ACPBP can promote AGD. The conclusions of the paper are as follows: (1) The AGD is impacted differently by various types of ACPBP. The policy of replacing chemical fertilizers with organic fertilizers for fruits, vegetables and teas (RCFOFFVT) can inhibit AGD, national agricultural green development pioneer zone policy (NAGDPZP) can promote AGD. There is persistence in the effects of both RCFOFFVT and NAGDPZP. (2) There are regional heterogeneities in the effects of RCFOFFVT and NAGDPZP on AGD. The RCFOFFVT has a stronger inhibitory effect on AGD in low economic development counties and major grain-producing counties. The NAGDPZP has a stronger promotional effect on AGD in high economic development counties and non-major grain-producing counties. For low-carbon policy counties, the inhibitory effect of RCFOFFVT is insignificant and the promotional effect of NAGDPZP is weaker. (3) Agricultural Science and Technology Innovation (ASTI) moderates the impact of RCFOFFVT and NAGDPZP. ASTI would weaken the inhibitory effect of RCFOFFVT on AGD, and would strengthen the role of the NAGDPZP in promoting the AGD. (4) There is a policy mix effect of RCFOFFVT and NAGDPZP that can jointly promote AGD. The conclusions of the paper indicate that ACPBP are effective approaches to deal with the food crisis and environmental pressures, but in the process of policy implementation, different types of ACPBP need to be implemented based on the realities of the situation. [ABSTRACT FROM AUTHOR]

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

Plastic pollution has become a global and persistent challenge, posing threats to ecosystems and organisms. In recent years, there has been a rapid increase in scientific research focused on understanding microplastics in the soil‒plant system. This surge is primarily driven by the direct impact of microplastics on agricultural productivity and their association with human activities. In this study, we conducted a comprehensive bibliometric analysis to provide an overview of the current research on microplastics in soil‒plant systems. We systematically analysed 192 articles and observed a significant rise in research interests since 2017. Notably, China has emerged as a leading contributor in terms of published papers, closely followed by Germany and the Netherlands. Through co-authorship network analysis, we identified 634 different institutions that participated in publishing papers in this field, with the Chinese Academy of Sciences having the most collaborations. In the co-occurrence keyword network, we identified four clusters focusing on the diversity of microplastics within the agroecosystem, transportation, and quantification of microplastics in soil, analysis of plastic contamination type and impact, and investigation of microplastic phytotoxicity. Furthermore, we identified ten research priorities, categorized into the effects of microplastics in "soil" and "plant". The research hotspots were found to be the effect of microplastics on soil physicochemical properties and the synergistic phytotoxicity of microplastics with other pollutants. Overall, this bibliometric analysis holds significant value, serving as an important reference point and offering valuable suggestions for future researchers in this rapidly advancing field. [Display omitted] • Bibliometric analysis on the microplastic (MP) in the soil-plant system was conducted. • The research hotspots and progress of MPs in the soil-plant system were demonstrated. • A remarkable increase in research on MPs in the soil-plant system has been recorded since 2017. • Identified ten research priorities on microplastic in the soil-plant system. • Synergistic toxicity of MPs and other pollutants poses a greater risk to soil organisms and plants. [ABSTRACT FROM AUTHOR]

Over 195 countries developed Nationally Determined Contributions (NDC) under the Paris Agreement to mitigate CEs (CE). Mongolia commits to a 22.7% reduction in CE by 2030 under the NDC goal. The study assessed the CE impact of local food production on the NDC of ceasing food imports under the Food Revolution Program (FRP). Findings indicated that Mongolia could not sustainably feed the population solely with domestic foods while achieving carbon mitigation goals concurrently. Relying on domestic production, it was projected that there would be a 7% increase in CE within agricultural food production, which constitutes 15% of the total emissions of the agriculture sector. The NDC targets an 18% (5238 Gg CO 2eq) reduction in the agriculture sector by 2030; the study concluded that the mitigation goal would drop to 16.5% if foods are produced domestically under the FRP. This underscores the necessity of a strategic combination of local production (such as carrots, hen eggs, and rapeseed) and imports (like cabbage, cucumber, tomatoes, and chicken from the People's Republic of China) could effectively mitigate the CE. From a national perspective, combining import and local production proves beneficial; however, on the global scale, prioritizing local food production would be valuable to reduce the CE in food transportation and imported food production. [Display omitted] • The Food Revolution Program would have a negative impact on NDC goals. • If domestic foods replace imports, there will be 7% GHG increase by 2030. • 1st paper comparing NDC with the food system of Mongolia. • Raising awareness for policymakers to look at national or global perspectives of GHG. • Further study of carbon footprint must be conducted in food system of Mongolia. [ABSTRACT FROM AUTHOR]

The development of practical and robust automated diagnostic systems for identifying plant pests is crucial for efficient agricultural production. In this paper, we first investigate three key research questions (RQs) that have not been addressed thus far in the field of image-based plant pest identification. Based on the knowledge gained, we then develop an accurate, robust, and fast plant pest identification framework using 334K images comprising 78 combinations of four plant portions (the leaf front, leaf back, fruit, and flower of cucumber, tomato, strawberry, and eggplant) and 20 pest species captured at 27 farms. The results reveal the following. (1) For an appropriate evaluation of the model, the test data should not include images of the field from which the training images were collected, or other considerations to increase the diversity of the test set should be taken into account. (2) Pre-extraction of ROIs, such as leaves and fruits, helps to improve identification accuracy. (3) Integration of closely related species using the same control methods and cross-crop training methods for the same pests, are effective. Our two-stage plant pest identification framework, enabling ROI detection and convolutional neural network (CNN)-based identification, achieved a highly practical performance of 91.0% and 88.5% in mean accuracy and macro F1 score, respectively, for 12,223 instances of test data of 21 classes collected from unseen fields, where 25 classes of images from 318,971 samples were used for training; the average identification time was 476 ms/image. • Development of an accurate and practical framework for plant pest identification. • Evaluation of the model requires images, such as those collected elsewhere. • Foreground extraction significantly improves pest discrimination performance. • Class integration and cross-crop training boosts model performance. [ABSTRACT FROM AUTHOR]

• The ISP method allows for gathering a large number of historical training samples. • The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2. • A distinct northward expansion trend is evident for all four major crops, in Heilongjiang Province, China. Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history. [ABSTRACT FROM AUTHOR]

[Display omitted] • Farmers must be an allrounder for plant growth, plant protection, legislation and different economic fields. • AI service integration holds great potential for assistance, documentation, education, interpretation forecasts or data-driven predictions. • LLMs depict a fundamental step to reduce the gap between AI-driven data analysis and common user. • Reproduction of processed data would increase confidence in the model support farmer's interpretation and guide the LLM for more precise results. • Introducing these technologies requires not only new training but also significant effort in structural transformation. Since the launch of the "Generative Pre-trained Transformer 3.5", ChatGPT by Open, artificial intelligence (AI) has been a main discussion topic in public. Especially large language models (LLM), so called "intelligent" chatbots, and the possibility to automatically generate highly professional technical texts get high attention. Companies, as well as researchers, are evaluating possible applications and how such a powerful LLM can be integrated into daily work and bring benefits, improve their business or to make the research outcome more efficient. In general, underlying models are trained on large datasets, mainly on sources from websites, and online books and articles. In combination with information provided by the user, the model can give an impressively fast response. Even if the range of questions and answers look unrestricted, there are limits to the models. In this paper, possible use cases for agricultural tasks are elucidated. This includes the textual preparation of facts, consulting tasks, interpretation of decision support models in plant disease management, as well as guides for tutorials to integrate modern digital techniques into agricultural work. Opportunities and challenges are described, as well as limitations and insufficiencies. The authors describe a map of easy-to-reach topics in agriculture where the integration of LLMs seems to be very likely within the next few years. [ABSTRACT FROM AUTHOR]

• Butterfly detection by Quancttum mechanics adaptation on a multi-layered graph. • Local contrast estimation. • Backgroundw and foreground guidance for butterfly segmentation. • Regional Background connectivity measure. • Diffusion of contrast scores using Schrödinger equation on a multi-layered graph. Butterflies have a prominent role in the agro-ecological ecosystems. Some butterfly populations can injure wildlife, vegetation, and even humans in addition to causing harm to flora and fauna. By contrast, the presence of some other ones can help in improving agricultural productivity and preserving the agro-ecological ecosystems. Butterfly segmentation is therefore an initial process that precedes species recognition. In this paper, we propose a new segmentation process that adapts quantum mechanics to be deployed on a multi-layered graph. To achieve a proper butterfly segmentation, we implement efficiently the Schrödinger equation in a propagation process across the different layers of the graph. Furthermore, It is supported by both background and foreground priors guidance coupled to local contrast information. Comparative evaluation suggests that our method has higher resistance than competing methods to artefacts that are inherent to agro-ecological photographs. Our algorithm shows a considerable advantage over single-layered graph based versions when dealing with some image details. It also outperforms some deep learning based methods that achieve high segmentation performance. Unlike these methods, ours does not involve any training step. Thus, it doesn't require high performance equipments or supplementary human labelling operation and does not fall in the problem of generalization. [Display omitted] [ABSTRACT FROM AUTHOR]

Water scarcity threatens food security and leads to various economic, social, and environmental challenges, particularly in arid and semi-arid regions. As the agricultural sector consumes the most significant amount of water, conserving water resources in this sector is particularly important. Cultivation of low-water-demand (LWD) crops with high value-added is one of the effective measures to reduce agricultural water demand and improve farmers' livelihoods. However, changing water-intensive crops to LWD crops (e.g., medicinal plants) is voluntary pro-environmental behavior. Considering the role of psychological theories in understanding and explaining human behaviors, the main purpose of the current paper is to explore the socio-psychological determinants underlying farmers' intention to adopt an LWD cropping pattern using the Health Belief Model (HBM). In this regard, data were collected using face-to-face interviews with 184 farmers living in Zanjan province, Iran to examine their intention to cultivate LWD medicinal plants. Using structural equation modeling for data analysis, the HBM explained 49% of the variance in farmers' behavioral intentions. The structural equation modeling outcomes also revealed that self-efficacy, perceived barriers, cues to action, and perceived benefits are significantly related to farmers' intention to choose medicinal plants. Overall, the findings indicated the effectiveness and applicability of the HBM in illustrating farmers' intention to change their cropping pattern. Applying some strategies such as providing the necessary facilities and training programs for farmers, as well as, advertisements aimed at promoting the cultivation of medicinal plants can significantly contribute to conserving water resources in the study area. • Evaluating farmers' intention to medicinal crops using the Health Belief Model. • The HBM accounted for 49% of the variance of the farmers' behavioral intentions. • Self-efficacy, perceived expectations, and cues to action affect farmers intentions. • Self-efficacy was identified as the main predictor of farmers behavioral intentions. • Cultivating medicinal crops help conserving water resources in the study area. [ABSTRACT FROM AUTHOR]

Climate change has led to extreme weather events and food security issues, which have become urgent global problems. Crop models and hydrological models play important roles in analyzing these issues, but crop models have limitations in large-scale simulations, and hydrological models have shortcomings in depicting underlying surface details. Jointly using the two models to complement each other's shortcomings is becoming an important method, playing an important role in improving the quantitative characterization ability of different facets of agricultural production and is crucial to improving the efficiency of water resources utilization. This paper reviews the development process of crop and hydrological models and summarizes the widely used models at present, providing reference for researchers to select crop and hydrological models; secondly, it summarizes the types, methods, and examples of joint use of models, and analyzes possible problems in model combination; finally, some suggestions are put forward for the joint use of crop and hydrological models, providing a reference for researchers to combine models. • Retrospect on the development process of crop and hydrological models. • Summarized case studies on the combination of crop and hydrological models. • Provide an overview of cases where crop and hydrological models are used in conjunction. • General considerations and suggestions for future development. [ABSTRACT FROM AUTHOR]

• By 2020, crop technologies developed in collaboration with CGIAR centers had spread to 221 million hectares worldwide; • Adoption of these technologies had raised economic welfare by about $47 billion per year; • In addition to improved varieties, the CGIAR has contributed to improved management of crop pests and natural resources; • CGIAR-related variety adoption was initially dominated by cereals, but has expanded to roots & tubers and grain legumes; • At least 92 countries have adopted CGIAR-related crop technologies, with India, China and Nigeria the largest beneficiaries. The international agricultural research centers that comprise the CGIAR got their start in the 1960s. They soon made major contributions to crop improvement in developing countries, particular in rice and wheat in Asia. Today, farmers can acquire new technologies from many sources, and evidence of whether the CGIAR continues to play an important role in crop improvement has become dated. This paper brings together an expanded set of evidence on the diffusion and productivity impact of CGIAR crop research through 2020, and breaks out these impacts by crop, region, and over time. By 2016–2020, CGIAR-related crop technologies had been adopted on at least 221 million hectares across Asia, Africa and Latin America, generating economic welfare gains of $47 billion annually. In the 2010s, technology adoption and welfare impacts were increasing by about $600 million annually, almost as much as in the 1990s. In the early days of the "Green Revolution," these welfare impacts were largely confined to rice and wheat in Asia, but in recent decades have grown to include a larger range of crops and geographies, notably cassava and maize in Sub-Saharan Africa. Although improved crop varieties have been the main technology through which CGIAR crop centers have achieved these impacts, CGIAR-related integrated pest management and natural resource management technologies have also made significant contributions to crop productivity. In addition to raising farm income, productivity gains in staple crops have lowered food prices, thereby benefitting the whole population. This is a key reason why agricultural productivity growth, and food crop productivity growth in particular, has had greater impacts on poverty reduction in low-income countries than comparable productivity growth in other sectors. [ABSTRACT FROM AUTHOR]

Increased global demand for food and energy implies higher competition for agricultural land. Photovoltaic installations contribute to more sustainable solutions to satisfying energy requirements, however, they also require land. To address this dilemma, agrivoltaics has been proposed, combining energy and agricultural production on the same area. Our objectives were to review and synthesise the current agronomic knowledge on agrivoltaics and its future development possibilities. A systematic literature search was conducted in Web of Science on 17 December 2022, resulting in 54 articles that met the inclusion criteria and concentrated primarily on food production. Most studies focused on combining electricity generation with crop production. Vegetables, especially lettuce and tomato, were the focus of many papers. The success of a crop under an agrivoltaic system depends on many factors, yet mainly on location and season. Additionally, even light-demanding crops such as maize could be grown under certain conditions. Therefore, we propose to define an optimal daily light integral for each species, rather than a shade level. Given climate change, agrivoltaics may reduce inter-annual yield fluctuation by buffering the negative effects of both frost and high temperatures on crops, as well as reducing water consumption. Future research should focus mainly on berries and on plants whose production can be affected by high temperatures. Experiments on larger areas, over several years, and with solar panels allowing a separation of the light spectrum are needed to promote development of agrivoltaics without affecting crop yields. • Impact on yield is highly variable between crop and geographical location. • Plants considered intolerant to shading could be grown under solar panels under certain conditions. • Benefits of agrivoltaics are also linked to reduced water consumption, improved crop protection and increased animal welfare. [ABSTRACT FROM AUTHOR]

Environmental regulations are crucial for addressing pollution and achieving green development. However, the role of agri-environmental regulations in promoting green agriculture is understudied. Using Chinese data (1999–2020) and a nonlinear panel threshold model, this paper explores their relationship. We measure the intensity of agri-environmental regulation by the difference in the real and theoretical polluting input consumption and compute agri-green growth by adjusting agricultural output using the cost of agricultural carbon emissions. Results reveal a two-stage impact of regulations on green growth in agriculture. At lower intensity, negative effects are observed, but beyond the threshold, positive impacts emerge. Evidence suggests the mechanism involves the nonlinear influence of agri-environmental regulations on agricultural production methods. We also find that negative impacts reverse faster, and positive impacts are more significant in major grain-producing regions and areas with high state-owned arable land. This study therefore suggests that the government subsidizes farmers at the initial stage of regulation to reduce the environmental costs borne by farmers to motivate them to adopt environmentally friendly production methods for agricultural sustainable green development. • We propoase a PLSR-based agri-environmental regulation measure and examine it'simpact on agriculture's green growth. • The panel threshold model is employed to find the economic threshold of effective agri-environmental regulation. • It exhibits a cost-benefit effect in the initial phase and an innovative compensation effect in the high-intensity phase. • Innovation in the agricultural production method is the underlying mechanism, accounting for 23.184% of the total effect. [ABSTRACT FROM AUTHOR]

The ability to ensure nutritional and food security is seriously threatened by the ever-growing global population. Overreliance on a few staple food crops such as wheat, rice and maize will no longer be able to satisfy the rising demand for future food. Even with increasing agricultural production, over 820 million people are still facing food insecurity and at least 2 billion are facing nutrition insecurity. Therefore, it is imperative to focus on finding simple and sustainable solution to the present threat of global food insecurity. Besides staple food crops, harnessing nutrient-dense, climate-resilient, and locally available crops for production, marketing and consumption as " Smart Foods " for the future is needed. Buckwheat is a potential smart food because of its stress resistance and nutritional properties as well as strong antioxidant activity due to presence of flavonoids. This paper comprises a comprehensive review of the scientific literature on the valorization of buckwheat crops in terms of germplasm resources, breeding and genetic improvements for yield, bioactive compounds, biological activities and its potential application in food and pharmaceutical industries. Identification of elite cultivars and exploitation of buckwheat derived gluten-free bakery and non-bakery food stuffs and beverages such as tea, beer and wine should be explored for human consumption. Abundance of bioactive compounds results in high anti-oxidant, anti-microbial, anti-glycemic, anti-cancer properties and other health-promoting activities highlighting buckwheat's potential application as nutraceuticals and pharmaceuticals. • With a burgeoning world population, pressure on the production of adequate food. • Homogenization of human diets has a significant bearing on health and nutritional security of the masses. • Buckwheat is a potential smart food because of its balanced amino acid composition and rich bioactive compounds. • Buckwheat-fractions are used to develop several health promoting food stuffs. [ABSTRACT FROM AUTHOR]

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development. • Digital credit resulted in a significant decline of the consumption of traditional energy products. • Impacts of digital credit on electricity consumption and the new-style energy products consumption were not significant. • Digital credit reduced traditional energy products consumption via boosting agricultural mechanization. • Digital credit reduced traditional energy products consumption by adopting precision fertilizer applying technique. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

This study explores the intricate relationship between agricultural productivity and the pursuit of a just energy transition in China. As the world's largest emitter of greenhouse gases and a nation grappling with environmental degradation and socio-economic disparities, China faces the imperative to transition toward a more sustainable and equitable energy system. This paper investigates the role of agriculture in this transition, recognizing its pivotal position as a significant emitter of greenhouse gases while also being vulnerable to the impacts of climate change. By examining the potential synergies and conflicts between agricultural productivity enhancement and the goals of reducing emissions and promoting social justice, this study aims to shed light on strategies that can effectively align agricultural development with the objectives of a just energy transition. The result indicates substantial internal disparities and strong agglomeration effects (referring to areas with similar agricultural total factor productivity) within China's agricultural total factor productivity distribution. Over the past 17 years (2005–2021), the average agricultural productivity has shown a commendable upward trend, marking a 2.8% growth rate. This increase signifies ongoing efforts and advancements in the agricultural sector, leading to improved productivity levels. Our results also show a significant positive spatial correlation in agricultural total factor productivity, suggesting a tendency for spatial agglomeration. The years 2005 and 2021 stand out as periods marked by significant adverse effects resulting from the interplay of diverse factors on agricultural productivity. By integrating findings from various sectors, this study provides insights into the complex interplay between agriculture and energy transition, emphasizing the need for integrated, holistic approaches that prioritize sustainability, fairness, and resilience in China's pursuit of a just energy future. • This study explores the relationship between agricultural productivity and energy transition. • Our aim is to align agricultural development with the objectives of energy transition. • The analysis connects productivity and equity. • We emphasize sustainability and resilience in China's pursuit of a just energy future. [ABSTRACT FROM AUTHOR]

Pesticide-free but non-organic production systems have been recently introduced in public and private programs in Europe. However, a key challenge is to define what 'pesticide-free' comprises and means. This paper aims to give insights in and critical reflections on the definition and regulation of pesticide-free crop production systems in real-world programs. To this end, we review four examples of public and private pesticide-free production programs in European crop production (i.e. from France, Germany and Switzerland). Our analysis reveals several semantic and regulatory inconsistencies and potential confusion that exists around the term pesticide-free crop production, i.e. we find that it is far from clear-cut what pesticide-free production systems are. For example, programs differ with respect to which part of the crop management cycle is regulated. Moreover, all programs offer various exceptions for pesticide use. Definitions and regulations differ widely across case studies. For example, to participate in governmental programs on pesticide-free production farmers in one country have different requirements than in another. Moreover, governmental regulations not match with those in private programs and vice versa. Pesticide-free production programs can be a useful and viable addition to European agricultural and food systems. However, based on the here presented insights, we formulate eight recommendations for policy and industry how to clarify, align and harmonize pesticide-free crop production. [Display omitted] • Pesticide free but non-organic production is of increasing relevance in European agriculture. • We investigate the design of real-world examples of governmental and private pesticide free production programs. • Definitions of pesticide free production differ widely and are often inconsistent. • We draw conclusions for policy and industry how to clarify and harmonize pesticide free production programs. [ABSTRACT FROM AUTHOR]

The study aims to point out the effect of municipal solid waste compost (MSWC) and traditional mineral fertilizers use, on rainfed forage barley herbage and grain production, nutritive value, and on soil chemical characteristics. During a three-year field trial, the MSWC treatments were COM1, 20 Mg ha−1 (DM) applied each year; COM2, 20 Mg ha−1 applied only the first year. The mineral fertilization was MIN50 (50 kg ha−1 of N + 46 kg ha−1 of P 2 O 5), and MIN100 (100 kg ha−1 of N + 92 kg ha−1 of P 2 O 5), applied each year. Fertilization treatments were compared with unfertilized control (COM0 and MIN0). Two years out of three COM1 showed a significant higher barley herbage dry matter (HDM) production and CP, when compared to COM0. COM1 in one year increased grain yield (YLD) whereas increased CP all years and decreased starch grain content two years out of three. MSWC strongly increased levels of sodium (Na) in herbage and grain. Mineral fertilizers levels significantly increased HDM production and YLD and their CP content compared to MIN0. Moreover, it lightly decreased herbage NDF, ADF, WSC and Total phenols content, raising in vitro dry matter digestibility (IVDMD). In soil, after three years of fertilization, COM1 increased SOC (Soil Organic Carbon), Total N, available P, exchangeable K and Mg content, but also exchangeable sodium percentage (ESP) and electrical conductivity (EC), showing higher values than COM2 and COM0. Mineral fertilization influenced only the available P soil level, increasing the content compared to MIN0. This paper pointed out that mineral fertilizers had clear short-term effects on the barley growth, yield, and nutritional value, but did not contribute to soil long-term fertility improvement. MSWC increased barley growth and yield reaching similar values obtained with mineral fertilizers. Above all, MSWC showed capability to improve soil fertility mainly in terms of organic matter and macronutrients and its use is consistent with the recent EU regulation on organic fertilizer productions (2018/84) that aims to "maintaining the long-term fertility of soils". • Municipal solid waste compost (MSWC) increased barley growth and yield in short and mid-term. • MSWC increased grain CP and strongly sodium herbage content. • MSWC improved soil long-term fertility mainly in terms of organic matter. • MSWC increased soil exchangeable sodium percentage (ESP) and electrical conductivity (EC). • Mineral fertilizers had short-term effects in the barley yield and nutritional value, but did not improve soil fertility. [ABSTRACT FROM AUTHOR]

• Melatonin improves seed germination and photosynthesis under abiotic stresses. • Melatonin mediates transcriptional regulation of stress adaptive responses. • Regulation of ROS-Ca2+ hub and H+-ATPase is critical for melatonin regulated ionic homeostasis. • Melatonin may operate in tandem with polyamines to regulate phytohormone biosynthesis. Horticultural crops are very important component of our food chain, and their production is highly sensitive to different soil related abiotic stresses (SRAS). These stresses reduce plant growth and development by reducing seed germination (SG), seedling establishment, photosynthesis, reproduction, thus reduce fruit yield and quality. Nonetheless application of plant growth regulators has been shown a very promising technique to confer SRAS tolerance in horticultural crops. Melatonin (Mel)-a high conserved and stress regulatory molecule regulates a plethora of stress adaptive responses in plants. This paper summarizes the current state of knowledge of Mel involvement in regulating stress adaptive responses under SRAS. The major topic covered in this review included: (i) the effects of exogenous Mel application on seed germination, photosynthesis, flowering, and fruit yield under different SRAS, (ii) Mel crosstalk with stress regulatory mechanism to confer stress tolerance in horticultural crops. Latter topic further discussed the role of Mel in regulating (i) redox regulation, (ii) ionic homeostasis, and (iii) hormonal regulation. We show that Mel regulates stress adaptive responses and improves plant growth by allowing the direct scavenging of ROS, up-regulation of H+-ATPase activity and vacuolar sequestration of toxic ions in cytosol and activation of ROS-Ca2+ hub, which latter prevent the negative effects of ROS and membrane depolarization on the ROS-activated and voltage-activated ion channels. Such Mel mediated desensitization of ion channels may play a crucial role in reducing stress induced efflux of mineral ions such as K, Zn, Mg from cytosol, thereby improving plant growth. Moreover, Mel may operate in tandem with polyamines to regulate the biosynthesis of different phytohormones. However, studies are required to reveal the molecular identity of genes relating to Mel-polyamine tandem and to light signaling that could directly regulate phytohormone biosynthesis and the interaction of Mel with light signaling in regulating SG. [ABSTRACT FROM AUTHOR]

In recent years, the escalating labor costs in agricultural production have emerged as a major concern. The use of inspection robots to achieve automated inspection of fruit and fruit bunches for ripeness not only enhances production efficiency and cost savings, but also simplifies the tasks for workers. To address this issue, an improved YOLOv7-based multi-task deep convolutional neural network (DCNN) detection model, called MTD-YOLOv7, is proposed in this paper. Initially, the dataset labels were expanded to meet the requirements of multi-task classification. Two additional decoders were then added on the basis of YOLOv7 to detect tomato fruit clusters, fruit maturity and cluster maturity. Subsequently, the loss function was designed based on the characteristics of multi-task and the Scale-Sensitive Intersection over Union (SIoU) was used instead of Complete Intersection over Union (CIoU) to improve the model's recognition accuracy. Finally, to verify the effectiveness of the algorithm, tests were conducted on the cherry tomato dataset, and comparisons were made with common target detection algorithms, classification models, and cascade models. The experimental findings reveal that MTD-YOLOv7 achieved an overall score of 86.6% in multi-task learning, with an average inference time of 4.9 ms (RTX3080). It excels in simultaneous detection of cherry tomato fruits and bunches, fruit maturity, and bunch maturity, offering exceptional precision, rapid detection, and robust generalization capabilities. Its suitability extends to various applications, notably in inspection tasks. • A multi-task detection network for cherry tomato based on YOLOv7 was proposed. • Improving the cherry tomato dataset format for multitasking experiments. • Achieving three tasks: cherry tomato detection, fruit and bunch ripeness grading. • The proposed model has high speed while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Agricultural food production is both affected by and contributing to climate change. At the global scale, agri-food systems are responsible for one-third of total greenhouse gas emissions. With progressing climate change, the risks of crop failure increase. Thus, an urgent need is to reduce emissions from food systems while increasing their resilience to climate change. Enormous untapped potentials to achieve these dual goals lie in transforming agri-food systems towards more diverse, plant-based, and regional food production systems. In this paper, we present an innovative approach for identifying climate-adapted alternative food crops that could (1) help to diversify existing cropping systems and thus increase their climate resilience and can be (2) nutritious elements of plant-based regional diets with reduced emissions. The approach builds on the model ecocrop to select food crops that could benefit from regionally projected changes in climate. The model-based analysis is complemented with a literature review to examine the ecocrop results for their plausibility and provide a broader assessment of potentials for cultivation, utilization, and nutritional values of model-selected crops. The approach is applied to Switzerland, where we identify eight alternative crops with the potential to increase climate resilience while contributing to healthy human diets of regional consumers with benefits for climate mitigation (almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage). The literature review indicated that the increasing demand for many of these crops suggests great potential for regional marketing of crop products. The results produced in this study provide an initial guide for researchers and innovative farmers interested in experimenting with alternative crops in Switzerland, thus promoting climate-smart food system transformation from the production side. Using our unbiased bottom-up screening approach, we identified climate-adapted alternative crops that can provide essential nutrients, cover nutritional gaps in Switzerland, diversify existing production systems, and improve sustainability. [Display omitted] • Innovative bottom-up screening approach identifies climate-adapted alternative crops. • Almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage are identified. • Identified crops provide essential nutrients and cover nutritional gaps in Switzerland. • Selected crops can diversify existing production systems to improve sustainability. [ABSTRACT FROM AUTHOR]

The core of dual circulation in the Loess Plateau and the greening Yellow River is sustainable development. Gully agricultural sustainable production is extremely important to ensuring a more sustainable Loess Plateau. Here, we investigate the overall characteristics of gully agricultural production sustainable transformation (GAPT) in the Loess Plateau from a macro perspective. We also analyze the evolution, stage characteristics, and trends in gully agricultural production space using theoretical analysis and empirical research from the multi-dimensional perspective of spatio-temporal-development. The findings reveal that: (1) From 2000 to 2020, the gully agricultural production structure in the Loess Plateau exhibited a trend of hierarchical upgrading. In general, gully agricultural production in the Loess Plateau has entered a relatively stable new normal stage of structural adaptation adjustment. (2) Throughout the past two decades, gully agricultural production development in the Loess Plateau has gradually improved, progressing from a low to a high level. Additionally, overall regional disparity has diminished over time. (3) The overall fluctuation of GAPT in the Loess Plateau has increased, with more pronounced changes observed in agricultural functions and ecological security maintenance. At different stages, various factors had demonstrated varying effects on the GAPT, leading to significantly diverse outcomes in production sustainable transformation in response to changes in factor levels. Overall, understanding sustainable gully agricultural production is immensely important for exploring human-earth interactions and forging a sustainable path within this intricate balance. Our research outcomes offer valuable insights for fostering gully rural transformation, optimizing crop planting modes, enhancing the gully living environment, and ultimately propelling the Loess Plateau towards a more sustainable and prosperous future. • This paper proposed a multi-dimension analysis framework for gully agricultural production sustainable transformation (GAPT). • We quantified GAPT 's trends and characteristics of Loess Plateau, China. • Policy suggestions were given for strategy for more sustainable Loess Plateau. • This research has significance for the gully rural revitalization and green sustainable development. [ABSTRACT FROM AUTHOR]