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Producing sugar from lignocellulosic biomass is a promising resilient food solution to counter the near-total global failure of food production due to the agricultural collapse that would likely follow an abrupt sunlight reduction catastrophe such as a nuclear winter, a supervolcanic eruption, or a large asteroid or comet impact. This study examines how quickly edible sugar production could be ramped up globally by repurposing pulp and paper mills, sugarcane biorefineries, corn biorefineries, and breweries for lignocellulosic sugar production. A sub-unit component comparison to the NREL 2017 Biochemical Sugar Model indicates that 84%, 65%, 37%, and 39% of ISBL unit components are present, respectively. Fast construction methods were studied to analyze how this and other industrial foods could be rapidly leveraged in a catastrophe. Results suggest that the world’s current sugar demand could quickly be fulfilled by repurposing pulp and paper mills for lignocellulosic sugar production, given 5 months of production ramp-up and 24/7 construction. This method could reduce construction time to an estimated 32% of the original at an increased labor cost of 1.47 times, resulting in sugar production beginning 5 months after the catastrophe at a retail cost of $0.82 USD/kg. This could not only contribute a significant share of the food requirement after the catastrophe (∼28% within the first year), but also be key to preventing global starvation between the time at which global food storages run dry and other resilient food solutions can scale up significantly. This study aims to serve as the basis for more comprehensive scenario analyses. More research is needed to characterize material and labor constraints to fast response in more depth; repurposing and fast construction pilot studies and food safety studies are recommended. [ABSTRACT FROM AUTHOR]

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]

Accurate crop mapping is of great significance for crop yield forecasting, agricultural productivity development and agricultural management. Thanks to its all-time and all-weather capability, integrating multi-temporal synthetic aperture radar (SAR) for crop mapping has become essential and challenging task in remote sensing. In recent years, deep learning (DL) has demonstrated excellent crop mapping accuracy to interpret crop dynamics. However, existing DL-based methods tend to be incapable of capturing spatial and temporal features at different scales simultaneously, and this often leads to severe mis-classification due to the complex and heterogeneous distribution of crops and diverse phenological patterns. In this paper, we propose a novel spatio-temporal multi-level attention method, named as STMA, for crop mapping using time-series SAR imagery in an end-to-end fashion to increase the capability of crop phenology retrieval. Specifically, the multi-level attention mechanism is designed to aggregate multi-scale spatio-temporal representations on crops via cascaded spatio-temporal self-attention (STSA) and multi-scale cross-attention (MCA) modalities. To ensure a fine extraction of multi-granularity features, a learnable spatial attention position encoding is proposed to adaptively generate the position priors to facilitate multi-level attention learning. Experimental results on Brandenburg Sentinel-1 dataset, public PASTIS-R dataset and South Africa dataset demonstrated that STMA can achieve state-of-the-art performance in crop mapping tasks, with the accuracy of 96.54% in the Brandenburg Sentinel-1 dataset, 86.77% in the PASTIS-R dataset and 83.37% in the South Africa dataset, validating its effectiveness and superiority. Further comparison of spatio-temporal generalization capability reflected its excellent performance in spatio-temporal modeling on different crops and scenarios. This research provides a viable and intelligent spatio-temporal framework for large-area crop mapping using time-series SAR imagery in complex agricultural systems. The Brandenburg Sentinel-1 dataset and the STMA code will be publicly available at https://github.com/hanzhu97702/ISPRS_STMA. [ABSTRACT FROM AUTHOR]

The synthesis, mechanism, properties and applications of hydrogels for agriculture were reviewed in this paper. [Display omitted] Agricultural production is facing challenges such as water scarcity, declining soil quality, and excessive use of chemical fertilisers and pesticides, and there is an urgent need to find sustainable solutions. Hydrogel, as a novel functional polymer material, is considered as a potential agro-material to solve these problems due to its excellent water retention, swelling, slow release, biocompatibility and biodegradability. However, there are still challenges in designing efficient agrohydrogels, such as sustainability of the materials, environmental impacts of cross-linking methods, adaptability of the network structure to the crop growing environment, as well as the cost of the materials and the effectiveness of the practical applications. Therefore, a systematic review of the design, properties and applications of agrohydrogels is of great theoretical and practical significance. This paper reviews the design methods of agricultural hydrogels, including network structure design, material source selection, crosslinking technology and its mechanism research. Then, the key properties of agricultural hydrogels, such as water retention, swelling, slow release, biocompatibility and biodegradability, are discussed in detail. Finally, the applications of hydrogels in the fields of soilless cultivation, soil improvement and smart agriculture are presented. This paper concludes that with the continuous progress of technology, agricultural hydrogels will play an important role in future agricultural production. [ABSTRACT FROM AUTHOR]

This paper develops a new method to construct the household-based Human Development Index to examine the effects of extreme temperature shocks on human development at the household level. Four waves of the Vietnam Household Living Standard Survey from 2012 to 2018 are used to analyze the effects of temperature shocks on households in different regions. The findings reveal that if temperatures deviate from the long-run average by more than two standard deviations, the Human Development Index score will decline by 2 to 3.5 percentage points, with the most significant effect identified for households in the South and Central Highlands. Additionally, the paper identifies agricultural production, alcohol consumption and energy poverty as channels through which extreme temperatures affect human development. Finally, the findings suggest that policies aimed at mitigating the effects of climate change on vulnerable regions should focus on supporting households in these areas through measures such as developing climate-resistant crops, encouraging agricultural adaptation strategies, integrating local labor markets, and improving energy access. [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]

The escalating salinity levels in cultivable soil pose a significant threat to agricultural productivity and, consequently, human sustenance. This problem is being exacerbated by natural processes and human activities, coinciding with a period of rapid population growth. Developing halophytic crops is needed to ensure food security is not impaired and land resources can be used sustainably. Evolution has created many close halophyte relatives of our major glycophytic crops, such as Puccinellia tenuiflora (relative of barley and wheat), Oryza coarctata (relative of rice) and Glycine soja (relative of soybean). There are also some halophytes have been subjected to semi-domestication and are considered as minor crops, such as Chenopodium quinoa. In this paper, we examine the prevailing comprehension of robust salinity resilience in halophytes. We summarize the existing strategies and technologies that equip researchers with the means to enhance the salt tolerance capabilities of primary crops and investigate the genetic makeup of halophytes. • Developing halophytic crops is needed to ensure food security is not impaired and land resources can be used sustainably. • The study of these halophytes will help us improve the salt tolerance of our existing crops and diversify the human diets. • We conclude the available strategies and technologies that enable the researchers to improve the salt tolerance performance of the major crops and the genetics of halophytes. [ABSTRACT FROM AUTHOR]

Control of meat quality traits is an important goal of any farm animal production, including poultry. A better understanding of the biochemical properties of muscle fiber properties that drive muscle development and ultimately meat quality constitutes one of the major challenging topics in animal production and meat science. In this paper, the existing classification methods of skeletal muscle fibers in poultry were reviewed and the relationship between contractile and metabolic characteristics of muscle fibers and poultry meat quality was described. Finally, a comprehensive review of multiple potential factors affecting muscle fiber distribution and conversion is presented, including breed, sex, hormones, growth performance, diet, muscle position, exercise, and ambient temperature. We emphasize that knowledge of muscle fiber typing is essential to better understand how to control muscle characteristics throughout the life cycle of animals to better manage the final quality of poultry meat. [ABSTRACT FROM AUTHOR]

• Research status of 3D reconstruction technology in agriculture was summarized. • Sensors, systems and methods used in 3D reconstruction were summarized. • The applications of 3D reconstruction methods in agriculture were summarized. • Challenges and future trends of 3D reconstruction in agriculture were reported. Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction technology serves as a transformative tool, enriching our ability to perceive the genuine shape and stereo structure of objects and scenes in the real world. Through combining advanced sensors, image processing algorithms and 3D reconstruction methods, it captures the shape and structural information of targets from multiple perspectives and dimensions, and creates highly realistic 3D models in the virtual environment. With the rapid modernization of agriculture and ongoing technological progress, the demand for more efficient and precise management and monitoring methods in agricultural production is increasing. Traditional observation and measurement methods face challenges such as low efficiency and incomplete data. 3D reconstruction technology provides more accurate and intelligent management tools for smart agriculture. This paper provides a detailed introduction to the research progress based on 3D reconstruction technology in smart agriculture. It delves into the characteristics and development of various sensors and sensing systems, discussing various methods to implement 3D reconstruction technology. Different from applications in industrial environments, agricultural environments and crops are usually complex and variable, and consideration of diverse factors is required for the selection of suitable sensors and reconstruction methods. Therefore, several aspects of applications are summarized, such as agricultural robotics, crop phenotyping, livestock, and the food industry. Finally, the challenges and potential future trends of 3D reconstruction in agriculture are given. [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]

• 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]

Agricultural production is transitioning from traditional tools to IoT-connected automation devices. The integration of computer vision and agricultural automation is becoming closer with the rapid development of deep learning technology. Agricultural vision tasks, nevertheless, are relatively simple and less diverse compared to large-scale vision tasks, and there is a scarcity of plant data. This is also the reason why researchers need to make significant adjustments when directly applying advanced algorithms to the agricultural domain. In practice, downstream vision models should be designed to be concise and efficient. In this paper, we meticulously designed a deep convolutional network called TasselELANet for agricultural vision applications. It features a concise and efficient global architecture, including a 16-fold downsampling layer encoder and a decoder utilizing only 2 feature layers, which is greatly different from the general approaches. At its core is the efficient layer aggregation network, which utilizes the cross-stage fusion strategy to effectively optimize the gradient propagation path, thereby enhancing the learning ability and inference speed of the network. We validated the performance of TasselELANet on three challenging publicly available plant detection and counting datasets: maize tassels, wheat ears, and rice panels. The experimental tests achieved an average Accuracy of 0.899 and an average determination coefficient R2 of 0.867, remarkably outperforming other advanced computer vision methods in terms of accuracy and efficiency, and demonstrating sufficient generalizability. We firmly believe that TasselELANet can serve as a powerful and reliable vision tool for agricultural practitioners while also providing a solid foundation for future research. The code, datasets can be accessed at https://github.com/Ye-Sk/TasselELANet.git. • A powerful agricultural vision tool for future research and applications. • Concise and efficient architecture with 16-fold downsampling encoder and 2-layer decoder. • Efficient layer aggregation network for lightweight, high-performance, and robust modeling. • Reporting state-of-the-art performance on three publicly available plant datasets. [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]

• 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]

• 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]

• For the first time in Lithuania, food losses in primary production are estimated. • Data come from a survey and Farm Accountancy Data Network. • Food loss rates range from less than 1% for milk to more than 20% for beets. • Environmental factors and quality issues are critical for crop products. • Diseases are the most important for animals. Food loss and waste occur along the whole food supply chain and need to be addressed by dedicated strategies. Among all the stages of the food supply chain, food loss in primary production has been the least studied to date. In order to address this gap, this paper focuses on on-farm food loss, using Lithuania as a case study. The research comprises eleven crop products and five livestock products prevailing in Lithuanian agricultural production. A survey-based method is utilized to collect primary data from farmers. The final data set consists of 408 completely answered questionnaires. The survey-based data are merged with those from the Farm Accountancy Data Network in order to devise an effective framework for estimating self-reported on-farm food loss. The results show that there exists substantial variation in on-farm food loss rates ranging from less than 1% for milk to slightly more than 20% for beets. Environmental factors and those related to consumer and buyer requirements are the main reasons for losses of crop products, while diseases appear as the main causes of death for animals. The food loss and waste reduction measures are derived based on the results of the survey. [ABSTRACT FROM AUTHOR]

The nexus of agri-food and sustainability in economic development has recently attracted the interest of policymakers, as global challenges like climate change and food security are revisited and reassessed. The critical role of food production in economic development has been emphasized through targeted agricultural quality policies. Many developed countries worldwide, including EU member states, have introduced food quality policies that could support sustainability. This paper combines knowledge obtained by several groups in a broad EU study and the reflections on policy-related results by EU-stakeholders, streamlined by a Delphi analysis. Current work presents research-based policy recommendations and statements on various quality schemes, introductory inferred from expert opinions throughout Europe, gauged through a modified policy Delphi framework. A roadmap of policy and practical proposals have been identified for all key stakeholders involved in these initiatives, implying the need to reshape the supply chain dynamics to continuously improve producers, processors, retailers, and consumers within the EU and definitively worldwide. Furthermore, implementing a holistic approach considering environmental and socio-economic features can improve the effectiveness of EU food quality policies. • Research-based results using various approaches and targeting all Food Supply Chains are synthesized and emerged. • Policy-related recommendations extracted directly and indirectly from a pool of synthesized results. • Policy-related results to contributing to the overall sustainability of food systems has identified. • Food quality schemes will be enhanced by employing policy recommendations discussed in a stakeholders participatory process (Delphi framework). [ABSTRACT FROM AUTHOR]

Existing studies have primarily examined the impact of farmland fragmentation on agricultural production from the perspective of crop yield per unit of land. However, they generally overlook the effects of farmland fragmentation on the productivity of both labor and capital. Drawing upon micro-level data from rice, wheat, and maize farmers across 14 provinces in China for the years 2019 and 2020, this paper measures the effects of farmland fragmentation on the labor productivity, land productivity, and capital productivity of these three different crops, within the same temporal and spatial dimensions. We utilize the internal instrumental variable method (KV-IV) to address potential endogeneity issues in the model. The research findings indicate: (1) Farmland fragmentation hampers the use of machinery, resulting in a significant decrease in the labor productivity of farmers. On average, for every 1% increase in the level of farmland fragmentation, labor productivity for rice, wheat, and maize farmers drops by 21.3%, 19.9%, and 21.9% respectively. (2) Farmland fragmentation has significantly increased the land productivity of rice. With every 1% increase in farmland fragmentation, the land productivity of rice farmers increases on average by 3.4%. However, the impact of farmland fragmentation on the land productivity of wheat and maize is not significant. (3) The capital productivity of rice, influenced by farmland fragmentation, mirrors the changes seen in land productivity. Rice farmers with higher levels of farmland fragmentation exhibit greater capital productivity. On average, with every 1% increase in the level of farmland fragmentation, the capital productivity of rice farmers increases by 6.7%. Conversely, farmland fragmentation has a significant negative impact on the capital productivity of both wheat and maize farmers. On average, for every 1% increase in farmland fragmentation, the capital productivity of wheat and maize farmers drops by 9.1% and 7.0% respectively. Based on the empirical results of this paper, we propose corresponding policy recommendations. In the conclusion, we delve into the underlying causes of farmland fragmentation in China and ascertain that, to address the issue of farmland fragmentation in the country, the government must take a leading role. The significance of this paper lies in analyzing the varying degrees and directions of the impact of farmland fragmentation on different crops, providing a basis for the government to implement differentiated farmland fragmentation management policies in different crop-producing regions in the future. [ABSTRACT FROM AUTHOR]

Acetochlor is a widely used herbicide in agricultural production. Studies have shown that acetochlor has obvious environmental hormone effects, and long-term exposure may pose a threat to human health. To quantify the hazards of acetochlor in drinking water, a health risk assessment of acetochlor was conducted in major cities of China based on the data of acetochlor residue concentrations in drinking water. The approach of the Species Sensitivity Distributions (SSD) method is used to extrapolate from animal testing data to reflect worst case human toxicity. Results show that hazard quotients related to acetochlor residues in drinking water for different age groups range from 1.94 × 10−4 to 6.13 × 10−4, so, there are no indication of human risk. Compared to the total estimated hazard quotient from oral intake of acetochlor, the chronic exposure imputed to acetochlor residues in drinking water in China accounts for 0.4%. This paper recommends 0.02 mg/L to be the maximum acetochlor residue concentration level in drinking water and source water criteria. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasingly inter-connected global food system is becoming more vulnerable to production shocks owing to increasing global mean temperatures and more frequent climate extremes. Little is known, however, about the actual risks of multiple breadbasket failure due to extreme weather events. Motivated by the Paris Climate Agreement, this paper quantifies spatial risks to global agriculture in 1.5 and 2 °C warmer worlds. This paper focuses on climate risks posed to three major crops - wheat, soybean and maize - in five major global food producing areas. Climate data from the atmosphere-only HadAM3P model as part of the "Half a degree Additional warming, Prognosis and Projected Impacts" (HAPPI) experiment are used to analyse the risks of climatic extreme events. Using the copula methodology, the risks of simultaneous crop failure in multiple breadbaskets are investigated. Projected losses do not scale linearly with global warming increases between 1.5 and 2 °C Global Mean Temperature (GMT). In general, whilst the differences in yield at 1.5 versus 2 °C are significant they are not as large as the difference between 1.5 °C and the historical baseline which corresponds to 0.85 °C above pre-industrial GMT. Risks of simultaneous crop failure, however, do increase disproportionately between 1.5 and 2 °C, so surpassing the 1.5 °C threshold will represent a threat to global food security. For maize, risks of multiple breadbasket failures increase the most, from 6% to 40% at 1.5 to 54% at 2 °C warming. In relative terms, the highest simultaneous climate risk increase between the two warming scenarios was found for wheat (40%), followed by maize (35%) and soybean (23%). Looking at the impacts on agricultural production, we show that limiting global warming to 1.5 °C would avoid production losses of up to 2753 million (161,000, 265,000) tonnes maize (wheat, soybean) in the global breadbaskets and would reduce the risk of simultaneous crop failure by 26%, 28% and 19% respectively. • Climate data from the atmosphere-only HadAM3P model were used to estimate the risks of climatic extreme events in the global breadbaskets. • To analyse the risks of simultaneous breadbasket failure, the copula methodology was applied. • Projected wheat, maize and soybean yield losses in the global breadbaskets increase disproportionately between 1.5 and 2 °C global warming. • The highest simultaneous climate risk increase in the breadbaskets between the two warming scenarios was found for wheat, followed by maize and soybean. [ABSTRACT FROM AUTHOR]

The adoption of sustainable residue management practices is considered as a cleaner production option for the agricultural sector. However, limited information is available from Pakistan on the current use and adoption of sustainable residue management practices and different socio-physiological factors are involved in the process of shaping farmers' intentions. Based on the protection motivation theory framework, this study analyzes the socio-physiological factors affecting the farmers' intentions on adopting sustainable crop residue management practices. The paper is based on cross-sectional data collected from 420 farmers across Punjab and employs a structural equation modeling to explore research objectives. The research findings show that farmers, who perceive high risk from crop residue burning or who are aware of its negative consequences, are more intended to adopt sustainable crop residue management practices. However, their adoption intentions do have significant relationship with the self-efficacy implying the lack of resources, time, knowledge, competency, and skills as constraints limiting the adoption of cleaner production strategies. Further, the research findings also confirm the positive and significant role of habits of farmers in shaping their intentions to use sustainable crop residue management practices. In addition to this, the paper found a positive and significant role in social interaction, sources of information and access to extension services in shaping farmers' decision regarding sustainable residue management practices at the farm level. The research findings suggest enhancing awareness to highlight the negative impacts of crop residue burning and to train them regarding useful and sustainable crop residue management options that are not only cleaner production options but also generate economic benefits for farmers. • Socio-psychological factors affecting farmers' SCRMPs intentions are assessed. • Perceived high risks of residue burning are more intended to adopt SCRMPs. • Socio-cognitive attributes significantly influence farmers' adoption intentions. • Farmers' intentions can improve by promoting ecological benefits of these practices. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, a new method is proposed for estimating soybean defoliation. • Regression and convolutional neural networks were employed in our method. • We also presented three approaches to generate synthetic images. • The proposed method was trained only with synthetic images. • Experiments on real images showed excellent results. Abstract Agriculture plays an important role in the economy of several countries, contributing from the production of food and income to the generation of jobs. To improve productivity in agriculture, proper crop management should be accomplished through pest control. One approach is to monitor the defoliation level, that is, the percentage of leaf damaged by insects. Despite the importance, this monitoring is performed manually most of the time, which affects reliability as well as it is considered to be a time-consuming task. In this paper, we propose a new fully-automatic method to estimate the defoliation level based on convolutional neural networks (CNN). The main CNN architectures (AlexNet, VGGNet and ResNet) were adapted from classification to regression by replacing the softmax layer with a fully-connected layer with one neuron and a sigmoid activation function. Since CNNs require a large number of training examples, this paper also proposes approaches for generating synthetic defoliation images. In this way, our method is trained only with synthetic images and evaluated using real images. In the experiments, we obtained a root mean square error of only 4.57 even for images with severe defoliation. Additionally, we presented experimental evidence that the proposed method reconstructs the damaged leaf parts to then estimate the defoliation level. [ABSTRACT FROM AUTHOR]

Because of the world's rapid population growth and growing technology, the need for energy is increasing sharply, while current energy resources with limited reserves are depleting. To meet the increased need for energy, the manufacture of biofuels from renewable sources (solid waste) offers enormous potential. Bio-oil production is now seen as a prominent possibility as a technology promising the production of drop-in transportation and boiler fuels. The selection of feedstock, characterization and pretreatment, reactor architecture, and selection of process parameters are all critical elements in the conversion of biomass to bio-oil. Several lignocellulosic biomasses have been investigated for bio-oil production by pyrolysis and hydrothermal liquefaction. This paper presents a comparative analysis of these two methods in terms of product output, process efficiency, and significant constraints. Furthermore, the paper discusses certain significant concerns related to bio-oil from biomass, such as bio-oil basics (e.g., features, chemistry), upgradation, application, environmental and economic evaluation. [Display omitted] • Bio-oil offers prominent possibility as a transportation fuel from biomass. • Comparative analysis of hydrothermal liquefaction and pyrolysis derived bio-oil. • Factors affecting bio-oil yield from biomass, its upgradation and application. • Compilation of operational bio-oil plants based on HTL and pyrolysis. • Techno-economic aspects covering production by HTL and pyrolysis. [ABSTRACT FROM AUTHOR]

Iraq's population will have reached 50 million by 2030, according to the Ministry of Planning. This population growth must be handled by ensuring food security stability in the face of expected obstacles resulting from global warming. The agricultural sector encounters limitations due to ineffective detection of persistent agricultural obstacles like pathogens, flawed soil analysis, insect infestations, inadequate irrigation, and drainage. More proven farming strategies must be transferred using modern technological leaps from this vantage point. It is clear from the outset that this paper aims to discuss how artificial intelligence might aid in the growth of conventional methods in agriculture. This involves tracking and guiding each stage of agriculture through the adoption of an intelligent system, which in turn includes managing a set of intelligent systems that control and supervise the rest of the processes (System of Systems or SoS), beginning with ploughing, crop pattern, type of fertilizer, and the appropriate type of irrigation. On the other hand, this technology collection could give farmers an accurate view of market trends and customer preferences, resulting in greater output. This paper gives an insight into the concept of a SoS and the application of artificial intelligence in various fields of agricultural engineering in Iraq. It also proposes an intelligence framework model for self-cultivation and incorporates competitive strategies to develop a decent ecosystem to transform traditional agriculture into smart and accurate agriculture. [ABSTRACT FROM AUTHOR]

Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. The literature search produced 11,908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. [Display omitted] • Recent advances in models and data used to predict crop yield were examined to identify any patterns and trends. • Statistical models were the most common but there was a surge in the use of machine learning models after 2018. • Quantitative analytical techniques were used to identify factors that affected the model's predictive ability. • Machine learning models were more accurate than process-based models but not statistical models. • Combining agro-meteorological with remotely sensed data was more accurate than using agro-meteorological data only. [ABSTRACT FROM AUTHOR]

• A method for probabilistic evaluation of annual energy production is presented. • The horizontal extrapolation and wake effect are incorporated. • Numerical example based on wind speed data and a wind farm in flat terrain is used. • The contribution of uncertainty of each variable is studied. • The uncertainty of annual energy production is 9.0% in numerical example. The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses can provide a suitable microclimate for crops to thrive under extreme weather conditions. The operations of these greenhouses are expensive due to the energy requirement of the active thermal conditioning systems required to maintain the growing environment for crop production. Engineering of these greenhouses to utilize clean renewable energy sources is critical and necessary to mitigate their carbon footprint, paving the way to a more sustainable agricultural industry. This paper presents numerical modelling of a net-zero passive solar greenhouse in Alberta, Canada with winter temperatures below freezing. The indoor microclimate of the greenhouse is modelled using the detailed radiation model of a transient simulation tool, TRNSYS. The paper investigates the effects of ground parameters and the solar-to-air fraction on the numerical results. The paper includes a cost comparison between crop production in the traditional and passive style greenhouse. [Display omitted] • The temperature profile of a net zero passive solar greenhouse in Alberta is simulated. • The effects of the solar-to air factor and ground parameters are studied. • The daily spike in the indoor temperature profile is due to the solar-to-air factor. • The indoor temperature profile is not sensitive to the changes in ground temperature. • Tomatoes production in the greenhouse is economically feasible. [ABSTRACT FROM AUTHOR]

This paper investigates whether the relative self-esteem level of spouses can lead to within-household competition for inputs and affect economic gender inequality in the home. Using data on smallholder farmer couples from Côte d'Ivoire, we examine the relationship between spouses' self-esteem and income-earning in agriculture. Although the link between own self-esteem and crop income-earning is positive, we find that one spouse's self-esteem is negatively related to the other's income-earning, particularly income-earning in higher-value export-oriented agriculture. Women's outcomes are more sensitive both to their own self-esteem (positively) and to their partners' (negatively) than men's. This negative relationship is driven by individuals during middle age, when self-esteem is considered most stable. A key channel through which self-esteem appears to matter is by increasing control over household land: a crucial input to agricultural production. In addition to confirming the importance of noncognitive skills for poverty reduction in rural settings, our findings highlight the importance of their impact on intra- and inter-household inequality, especially in the presence of interlocking market failures constraining the supply of inputs to production. [ABSTRACT FROM AUTHOR]

Salinization is one of the greatest threats in agriculture field limiting the growth and productivity of crops. Soil salinization directly affects the physiological, biochemical, and molecular functions of plants. The Plants adopt various tolerance mechanisms to combat salinity stress by involving complex physiological traits, metabolic pathways, and molecular or gene networks. Various techniques have been used to improve plant growth and productivity through genetic approach, genetic engineering and plant breeding. However, economic feasibility and ease of application can create a huge scope for priming techniques as a "stress reliever" in agricultural crop production. Seed priming is a simple, low-cost technique that enhances germination and seedling establishment by activating various physiological and metabolic processes. Priming regulates molecular mechanisms through increased expression of various stress related genes and proteins, which accelerates stress and cross tolerance. Priming memory and epigenetic changes enables the plants to withstand salinity stress by alterations in key signaling molecules, transcription factors, and change in chromatin states, that will be crucial for the second stress. In this way, priming can both mediate stress tolerance and initiate overarching stress tolerance to a wide range of stresses that further modify gene expression and enhance crop production. This review paper addresses some physiochemical, molecular and trans-generational mechanisms regulating plant adaptation and tolerance/cross tolerance to salinity in primed seeds/seedlings. • Seed priming is the state of readiness which keep plants in an alert mode. • Seed priming is a cost effective strategy and thus reduces the energy expenditure during stress condition. • Cis and trans/cross priming are effective for developing tolerance against salinity. • Seed priming induces different defense mechanisms in seeds/seedlings against salinity stress. • Seed priming activates priming memory and results in trans-generational memory. [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]

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]

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]

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]

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]

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]

Abstract The paper investigates the impact of rainfall shocks on agricultural productivity and crop-specific agricultural land productivity. The paper also examines the impact of negative rainfall shocks on household consumption as well as its distributional impact by initial wealth and geographical zones. We use nationally representative panel datasets from Nigeria merged with georeferenced rainfall information. Negative rainfall shocks have heterogeneous effects on crop-specific agricultural productivity and based on geographical zones. We use an instrumental variables regression approach, where agricultural land productivity is instrumented with negative rainfall shocks. A negative rainfall shock decreases agricultural productivity and hence decreases household consumption by 37%. We also show considerable differential impacts of rainfall shocks on household consumption by initial values of wealth and geographical zones. Rainfall shocks have a negative, significant impact for asset-poor and nonpoor households, but has a higher impact on household consumption for asset-poor households. Similarly, it has higher impact for land-poor households and households in northern Nigeria. Highlights • The impact of rainfall shocks on crop-specific agricultural productivity was investigated. • The paper examines the impact of negative rainfall shocks on household consumption. • The distributional impact of rainfall shocks was examined. • Heterogeneous effects of rainfall shocks were identified. • Differential impacts of rainfall shocks on household consumption was observed. [ABSTRACT FROM AUTHOR]

Agriculture is the main consumer of freshwater in the world. The paper described the process of virtual water content and flow in crops. Differences in climatic conditions of different regions in China result in large differences in water consumption during crop growth. The virtual water trade in crops connects water flows within and between regions, linking the actual water consumption with the invisible water trade as a whole. The objective of the paper was supplying the agricultural products virtual water trade evaluation system, determining quantitatively the virtual water flow within the region & regional. Using meteorological and agricultural data from 2003 to 2010, a comprehensive analysis of China's domestic & international virtual water trade of agricultural products has been undertaken. The virtual water for the three primary crops and virtual water trade are discussed. The virtual water content of grain crops in northern & southern China was 1293 m 3 /t & 942 m 3 /t, respectively; the national average value was 1117 m 3 /t; and the regional differences in virtual water content for each crop were significant. China's inter-regional agricultural products virtual water trade was not consistent with water resource endowment expectations. The transfer of crops from northern to southern regions would have a significant impact on the sustainable utilization of water resources and would exacerbate water resources shortages in northern regions. China had a trade surplus in global virtual water trade of agricultural products. The exported agricultural products virtual water amounted to 31.5 billion m 3 /yr., and the imported amount was 145 billion m 3 /yr. The net import of virtual water embedded in agricultural products increased from 44 billion m 3 /yr. in 2003 to 178 billion m 3 /yr. in 2010. It is further concluded that the trend for agricultural products total virtual water, green water, and blue water is that China is increasing its imports year on year. A large increase in imports of agricultural products has led to a decline in the rate of self-sufficiency in domestic agricultural production. The paper provided the basis for the comprehensive evaluation of crop planting structure adjustment, grain import & export, and the potential of regional water resources development and utilization. [ABSTRACT FROM AUTHOR]

Highlights • The project’s impacts were positive farmers’ productivity increase through a cooperative-centred strategy. • The cooperative-centred strategy determined an inclusive durum wheat value chain development. • The project’s impacts were positive for different categories of farm size. • The changes induced by project seemed not to have occurred at the expense of rotation practices. Abstract Food security and agricultural-led industrialisation are pivotal development objectives in Ethiopia. One of the main challenges this country faces is increasing agricultural productivity by integrating smallholder farmers into a high-value agricultural commodity supply chain. This paper examines an integrated project—the Agricultural Value Chains Project in Oromia (AVCPO)—that aims to improve the livelihoods of smallholders in the Bale Zone by involving them in the production of high-quality durum wheat and linking them to the pasta industry via farmers’ cooperatives. Using primary data collected in 2014 and retrospective information, this paper investigates the AVCPO’s effects on the quantity of cereal production, the share of cereals that have been sold through cooperatives, food security, and education. In order to account for potential violations of the exclusion restriction assumption, an instrumental variable approach is applied, together with three additional estimation strategies. The results suggest that the project has had a large and positive effect on gross and net values of cereal production per hectare, as well as on the share of production sold to pasta makers through cooperatives. These benefits accrue equally to land-rich and land-poor farmers. Furthermore, our analysis suggests that the AVCPO has improved educational outcomes and reduced food insecurity, without affecting crop rotation practices. Overall, our findings point to the effectiveness of the project. Before replicating or scaling up this intervention, however, it is necessary to understand how to better involve poorer farmers and which adjustments are needed if the areas selected have a lower potential than Bale Zone. [ABSTRACT FROM AUTHOR]

This paper deals with vegetable cultivation that could be faced in a space mission. This paper focusses on optimization, light, temperature and the harvesting process, while other factors concerning cultivation in space missions, i.e. gravity, radiation, were not addressed. It describes the work done in preparation of the deployment of a mobile test facility for vegetable production of fresh food at the Neumayer III Antarctic research station. A selection of vegetable crops was grown under varying light and temperature conditions to quantify crop yield response to climate factors that determine resource requirement of the production system. Crops were grown at 21 °C or 25 °C under light treatments varying from 200 to 600 μmol m −2   s −1 and simulated the dusk and dawn light spectrum. Fresh food biomass was harvested as spread harvesting (lettuce), before and after regrowth (herbs) and at the end of cultivation. Lettuce and red mustard responded well to increasing light intensities, by 35–90% with increasing light from 200 to 600 μmol m −2  s −1 , while the other crops responded more variably. However, the quality of the leafy greens often deteriorated at higher light intensities. The fruit biomass of both determinate tomato and cucumber increased by 8–15% from 300 to 600 μmol m −2  s −1 . With the increase in biomass, the number of tomato fruits also increased, while the number of cucumber fruits decreased, resulting in heavier individual fruits. Increasing the temperature had varied effects on production. While in some cases the production increased, regrowth of herbs often lagged behind in the 25 °C treatment. In terms of fresh food production, the most can be expected from lettuce, cucumber, radish, then tomato, although the 2 fruit vegetables require a considerable amount of crop management. Spread harvesting had a large influence on the amount of harvested biomass per unit area. In particular, yield of the 3 lettuce cultivars and spinach was ca. 400% than single harvesting. Increasing plant density and applying spread harvesting increased fresh food production. This information will be the basis for determining crop growth recipes and management to maximize the amount of fresh food available, in view of the constraints of space and energy requirement of such a production system. [ABSTRACT FROM AUTHOR]