Crop Distribution Research Articles

Gender role in food rights and sovereignty in West Africa: A rapid review

Recognizing the critical role gender plays in food systems within West Africa, this review paper explores the contributions women make throughout the food value chain and the challenges they face in achieving food and nutrition security. A search across the databases; Hinari, Ovid MEDLINE, Scopus and Web of Science targeted peer-reviewed articles reporting on studies published between 2000 and 2023 in English or French and focused on women in West Africa. Data were extracted using a predefined form in Covidence and a narrative synthesis was used to summarize women’s roles and challenges within food systems. Thematic analysis of 69 peer-reviewed articles out of a total of 9,789 screened revealed West African women as central figures in regional food systems. Beyond their well-documented roles in land preparation, planting, and harvesting, women actively engage in processing, distribution, marketing and household consumption of diverse food crops (fruits, vegetables, grains and tubers) across rural and urban agri-food systems. This multifaceted engagement extends far beyond production, demonstrably impacting key socio-economic and nutrition metrics including food security, food sovereignty, and sustainable livelihoods of countless communities across the region. Nonetheless, women are disproportionately affected by challenges such as unequal access to resources, limited decision-making power, and restrictive cultural norms and education. These factors hinder their participation in different activities within the food system. To address these gendered disparities and promote equitable food systems, targeted interventions are crucial. These interventions must prioritize education and training to equip women with knowledge, skills, and modern technology. Additionally, policy changes are necessary to promote equitable access to resources, financing and land ownership. Furthermore, dismantling discriminatory cultural norms and promoting gender equality are essential steps along with implementing infrastructure and technologies that address women's specific needs. By prioritizing women's empowerment through a combination of education, policy changes, technological upgrades, and collaborative efforts with stakeholders, West Africa can strive towards a more sustainable and equitable food system for all. Key words: food sovereignty, food rights, food systems, gender dynamics, West Africa

¿Libre de la Maleza Estatista? Assessing Neoliberal Promises and Water Markets in Chile

AbstractNeoliberal approaches to water governance, pioneered in Chile in the 1980s, are reappearing today on the centerstage of the water policy debate. While advocates claim that strong property rights, limits on government authority, and water markets can enhance environmental sustainability, efficiency, neutrality, and equity in the distribution of water rights, limited empirical evidence exists on whether neoliberal policies have delivered on these key promises. In this paper, we combine hydrological analysis with a nationwide data set on government water rights allocations between 1981 and 2021 to determine when and where water has been allocated beyond sustainable limits. We then integrate water market transaction and agricultural data to assess how allocations and scarcity conditions relate to spatial and temporal patterns in irrigation, crop distribution, and water market activity. Our results indicate that 30% of catchments are overallocated, and that continued government allocations of water rights during scarcity exacerbate already‐high inequalities in the distribution of water. We find no evidence that scarcity or water markets induced improvements in numerous efficiency metrics. Overall, our results support growing claims that the neoliberal water model fails to fulfill its key promises, notably to the detriment of nature and marginalized rural communities.

Mutual impact of salinity and climate change on crop production water footprint in a semi-arid agricultural watershed: Application of SWAT-MODFLOW-Salt

Sustainable agriculture in intensively irrigated watersheds, particularly those in arid and semi-arid regions, requires enhanced management practices to maintain crop production, which depends on climate, available water resources, soil conditions, irrigation practices, and crop type. Among these factors, soil salinity and climate change are significant challenges to agricultural productivity. To investigate the long-term influence of salinity and climate change on crop production from 1999 to 2100 in irrigated semi-arid regions, we apply the water footprint (WF) concept using the hydro-chemical watershed model SWAT-MODFLOW-Salt, driven by five General Circulation Models (GCMs) and two climate scenarios (RCP4.5 and RCP8.5), to a 732 km2 irrigated stream-aquifer system within the Lower Arkansas River Valley (LARV), Colorado, USA. The study focused on calculating the green (WFgreen), blue (WFblue), and total (WFtotal) crop production WFs for 29 crops in the region, with and without including salinity effect on crop yield. Results reveal that during the baseline period (1999–2009), the total annual average WFgreen, WFblue, and WFtotal increased by 7.6 %, 4.4 %, and 6.5 %, respectively, under salinity stress, as crops experienced reductions of up to 4.6 %, 1.6 %, and 2.3 % in green, blue, and total crop yield. The mutual impact of salinity and the worst-case climate model (IPSL_CM5A_MR) under the higher emission scenario (RCP8.5) led to a 3.3 %, 1.9 %, and 3 % increase in green, blue, and total crop production WFs. Furthermore, the study highlighted that the proportion of green, blue, and total crop production WFs in the LARV exceeded the world average. This discrepancy was attributed to various factors, including different spatial and temporal crop distribution, irrigation practices, soil types, and climate conditions. Notably, salinity stress affected green crop yield and green WF more significantly than blue crop yield and blue WF across all GCM models. This finding underscores the importance of prioritizing management practices to control salinity-associated challenges within the region.

Light distribution at the fruit tree-crop interface and consequences for yield in sloping upland agroforestry

Agroforestry can improve soil conservation and overall farm productivity compared with sole-crop systems, but its benefits are limited by competitive interactions between tree and crop components. Studies on light competition have been performed on relatively flat land, but slope can influence light distribution. Little is known about optimizing light utilization and enhancing system productivity and/or income from agroforestry on sloping land.This study examined how slope influences light distribution and performance of maize and coffee crops in fruit tree-crop agroforestry. Starting hypotheses were that 1) crops upslope of tree rows receive and intercept greater amounts of light than those downslope; and 2) position of the crop is more important for light interception and yield when fruit trees have a large, dense canopy.Five-year-old fruit-crop agroforestry experiments on west-southwest facing slopes were revisited. Each agroforestry treatment was divided into nine zones relative to the tree rows (zone 5), with zones 1–4 upslope and 6–9 downslope of the fruit tree row. Light distribution was assessed using Hemiview and SunScan and compared with that in sole-maize and sole-coffee systems. Crop growth and yield were also recorded.Incident light to the crop was higher in the sole-crop system than in agroforestry. In agroforestry, incident light to the crops was lower downslope of trees than upslope but increased with increasing distance from the tree rows. On average, 0.40–0.50 fraction of total light reached the soil surface. Downslope had a stronger negative effect on light distribution and crop yield than upslope. The available light at the soil surface provides scope for additional components. Further studies on the light demands of different crops during the season could improve system design.

Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.

Amino Acid Transporters on Amino Acid Absorption, Transport and Distribution in Crops

Amino Acid Transporters on Amino Acid Absorption, Transport and Distribution in Crops

A new transferable deep learning approach for crop mapping

ABSTRACT Accurate crop mapping provides important information for government decision-making and agricultural management. Recent advancements in deep learning have significantly enhanced the capabilities of remote sensing-based crop mapping. In this study, we developed a new deep learning approach, DSH, which comprises three modules, DeepLabV3+ (D), channel self-attention (S), and histogram matching (H). The DeepLabV3+ module learns the spatial distribution of crops in the spatial and spectral dimensions. The channel self-attention module was used to enhance the weights of the important features. The histogram-matching module addresses the domain gap between the training and testing images and enhances the transferability of the DSH. In addition, the input data of the DSH are monthly synthesized Sentinel-2 data, thus relaxing the data collection requirements. The performance of the DSH was evaluated and benchmarked against HRNetV2, DeepLabV3+, and random forest (RF) at eight sites in the U.S. China, and France with different farming structures, climate conditions, and landscape complexities. Temporal transfer revealed that the classification performance of DSH improved as the growth season progressed, peaked in the peak growth season (August), and then declined at each study site. The DSH model trained during the peak growth season demonstrated superior temporal transferability. Spatial transfer experiments demonstrated that transferring DSH to sites with similar planting structures achieved higher precision than full spatial transfers. Spatiotemporal transfer experiments conducted at eight sites over three years (2020–2022) demonstrated an average overall accuracy (OA) of 88.1% for DSH, highlighting the importance of similar planting structures for effective spatiotemporal transfer. DSH outperformed HRNetV2, DeepLabV3+, and RF in OA, producer’s accuracy, and user’s accuracy at all eight study sites. Notably, DSH exhibited comparable performance during both the peak and full growth seasons, with an average OA of 93.9%. The classification accuracy of test images after histogram matching remained stable from 2020 to 2022, whereas the accuracy of raw test images fluctuated annually. This indicates that histogram matching is effective in improving the transferability of DSH. Embedding the channel self-attention module into the position of the high-level features had a more significant impact on improving DSH’s performance than other configurations. Visualizing features from the channel self-attention and high-level layers revealed that integrating the channel self-attention module with DeepLabV3+‘s high-level features significantly improved crop mapping by enhancing relevant semantic information. Overall, DSH exhibits robust spatiotemporal generalizability and requires minimal input data, making it suitable for large-scale crop mapping.

Improving Northeast China’s soybean and maize planting structure through subsidy optimization considering climate change and comparative economic benefit

The planting structure of Northeast China (NEC) is greatly responsible for China’s food security and self-sufficiency. Climate change promoted the northward expansion of the suitable planting area for maize in NEC, and its higher comparative economic benefit largely helped it occupy the soybean planting space. Yet, knowledge on how climatic suitability and comparative economic benefit affected the northward expansion of maize planting area is limited. To bridge this gap, this study quantified the climatic suitability of maize and soybean from 1981 to 2020 using the fuzzy mathematical method. Combined with yield, crop distribution and social economic data in 2000, 2010 and 2020, this study further calculated the comparative economic benefit between the two crops. Furthermore, a crop planting decision simulation model was developed to clarify the impact of climatic suitability and comparative economic benefit on the planting area of soybean and maize. Results showed that from 2000 to 2010, the maize planting area increased dramatically. This was due to the continuous northward expansion of the most suitable region (S1) for maize from 47.63°N to 48.43°N before 2010, along with the fact that 65% of the total area was more profitable to plant maize. From 2010 to 2020, the maize planting area partially retreated, mainly due to the shrinking trend of the northern boundary of S1 for maize and the implementation of soybean producer subsidy. The improvement of maize climatic suitability played a fundamental role in the northward expansion of its planting area. On this basis, the comparative economic benefit was critical for planting proportion adjustments. The current soybean producer subsidy largely reduced the profit gap between the two crops, but it was still insufficient in nearly one-third of the total area. This study suggested to increase amount of soybean subsidies in the identified soybean-maize imbalanced regions. Specifically, the average recommended increase amounts ranged from 756 to 2052 CNY/ha depending on the regions. These findings could provide valuable references for understanding the motivation of crop planting decisions and optimizing the corresponding subsidy policies.

Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images

Abstract The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective; it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules.

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm’s pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers’ agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

Selenium spatial distribution and bioavailability of soil-plant systems in China: a comprehensive review.

Selenium (Se) has a dual nature, with beneficial and harmful effects on plants, essential for both humans and animals, playing a crucial role in ecosystem regulation. Insufficient Se in specific terrestrial environments raises concerns due to its potential to cause diseases, while excess Se can lead to severe toxicity. Thus, maintaining an optimal Se level is essential for living organisms. This review focuses first on Se transformation, speciation, and geochemical properties in soil, and then provides a concise overview of Se distribution in Chinese soil and crops, with a focus on the relationship between soil Se levels and parent materials. Additionally, this paper explores Se bioavailability, considering parent materials and soil physicochemical properties, using partial least squares path modeling for analysis. This paper aimed to be a valuable resource for effectively managing Se-enriched soil resources, contributing to a better understanding of Se role in ecosystems.

Metabolomics and transcriptomics analysis revealed theresponse mechanism of alfalfa to combined cold and saline-alkali stress.

Cold and saline-alkali stress are frequently encountered by plants, and they often occur simultaneously in saline-alkali soils at mid to high latitudes, constraining forage crop distribution and production. However, the mechanisms by which forage crops respond to the combination of cold and saline-alkali stress remain unknown. Alfalfa (Medicago sativa L.) is one of the most essential forage grasses in the world. In this study, we analyzed the complex response mechanisms of two alfalfa species (Zhaodong [ZD] and Blue Moon [BM]) to combined cold and saline-alkali stress using multi-omics. The results revealed that ZD had a greater ability to tolerate combined stress than BM. The tricarboxylic acid cycles of the two varieties responded positively to the combined stress, with ZD accumulating more sugars, amino acids, and jasmonic acid. The gene expression and flavonoid content of the flavonoid biosynthesis pathway were significantly different between the two varieties. Weighted gene co-expression network analysis and co-expression network analysis based on RNA-Seq data suggested that the MsMYB12 gene may respond to combined stress by regulating the flavonoid biosynthesis pathway. MsMYB12 can directly bind to the promoter of MsFLS13 and promote its expression. Moreover, MsFLS13 overexpression can enhance flavonol accumulation and antioxidant capacity, which can improve combined stress tolerance. These findings provide new insights into improving alfalfa resistance to combined cold and saline-alkali stress, showing that flavonoids are essential for plant resistance to combined stresses, and provide theoretical guidance for future breeding programs.

Global wheat planting suitability under the 1.5°C and 2°C warming targets.

The potential distribution of crops will be impacted by climate change, but there is limited research on potential wheat distributions under specific global warming targets. This study employed the Maxent model to predict the potential distribution of wheat under the 1.5°C and 2°C warming targets based on data from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) multimodel ensemble, and the effect of global warming on wheat planting suitability was analyzed. Our results indicated global warming would significantly change wheat planting suitability. Over half of the areas experienced changes in wheat planting suitability under two warming targets, and the effect became more pronounced with increasing temperatures. Additionally, global warming might promote wheat planting in more regions. The area with an increase in wheat planting suitability was observed to be 9% higher than those experiencing a decrease on average. Moreover, global warming could exacerbate the disparity between global wheat supply and demand in countries/regions. Traditional wheat-producing countries/regions are poised to benefit from the warming effects of climate change, while less developed and wheat import-dependent countries/regions may face greater challenges in achieving wheat self-sufficiency. To address this potential challenge, the promotion and inter-regional exchange of agronomic technologies, and the development of more rational trade standards are urgently needed. Since socioeconomic factors have a significant impact on wheat cultivation, further investigation is required to determine how the wheat planting distribution may change in the future under the combined impact of climate change, supply-demand relationship, and policy.

Effects of Different Crop Intercropping on the Growth, Root System, and Yield of Tiger Nuts

Intercropping is a vital cropping system that can create a conducive growth environment for crops and enhance land productivity. Tiger nuts (Cyperus esculentus L.) have high oil content and are adaptable to various soil types, making them a promising new oil crop with significant development potential. This study evaluated the plant height, leaf area, tiller numbers, biomass, land equivalent ratio (LER), and root morphological characteristics of tiger nuts. The agronomic traits and root distribution of tiger nuts and other crops were further investigated to achieve the goal of high yield for tiger nuts. Seven intercropping systems were implemented in the experiment: maize–tiger nut intercropping (MT), soybean–tiger nut intercropping (ST), cotton–tiger nut intercropping (CT), monoculture tiger nut (T), monoculture maize (M), monoculture soybean (S), and monoculture cotton (C). The results indicated that under different planting systems, the agronomic traits of tiger nuts in MT and ST modes were superior, with plant height and tiller numbers increasing by 7.6% to 11.6%. However, the plant height and Soil Plant Analysis Development (SPAD) values in CT mode were slightly lower than in T mode. Additionally, intercropping reduced the leaf area by 6.2% to 37.9%. Root development was more pronounced in intercropping modes, with the ST mode showing the most significant improvement, increasing the 0–20 cm root length density (RLD) by 12.2% to 45.7%. Therefore, each of the three intercropping modes demonstrated distinct advantages. The LER of the intercropping systems ranged from 1.10 to 1.24, enhancing land utilization, with tiger nuts being the dominant species. Compared to monoculture, the ST mode exhibited the best overall effect. Understanding the impact of different planting systems on tiger nuts provides valuable insights for developing tiger nut cultivation in Xinjiang.

Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions

Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.

Honey bee (Apis mellifera L.) foraging rewards in sunflowers: effect of floral traits on visitation and variation in pollen quality over two consecutive years

Sunflower production depends on pollination by honey bees, Apis mellifera L., and wild bees. For wild sunflower oligoleges, preference for various sunflower lines seems to reflect the quantity (or accessibility) of floral rewards, with floret size (≈accessibility of sunflower nectar) appearing most important. However, it is less clear how honey bees, a key generalist, are affected by sunflower floral traits. The honey bee-sunflower interaction was explored to test if accessibility of nectar (≈floret size) and related floral traits explain foraging preferences and to assess potential nutritional variation in sunflower pollen. In the first two years, honey bee foraging preference increased with decreasing floret length similar to previous observations with wild pollinators. In the second year, no similar response was seen, though nectar rewards (by volume or volume × concentration) were remarkably low compared to the previous year. Pollen collected from bagged plants in the second year showed sunflower lines differed in concentrations (µg/mg ± SE) of total fatty acids and essential fatty acids. In general, it appears that honey bee responses to floral traits are similar to those of wild bees responsible for pollinating much of the sunflower crop in the central United States and that variation in the nutritional quality of sunflower pollen is greater than previously known. Because of the broad geographic distribution of the sunflower crop, additional research on how the environment influences floral rewards and sunflower-pollinator interactions may be needed.

Sustainable Food Security: An Ethnobotanical Study of Dlingo Village, Grobogan Regency

Abstract The United Nation Sustainable Development Goals were designed as interrelated guidelines for the development of a better future for all mankind. the Covid 19 pandemic related shut down of trade and the post pandemic food shortages have put increasing pressure to develop locally based sustainable food security. This food security can be found in the self-sufficient semi-subsistence Javanese village community in Dlingo, Grobogan Regency. The efficient allocation of land resources through the systematic distribution of food crops from the sawah (paddy fields), kebun (gardens), common areas, and pekarangan (home gardens), along with dietary practices that utilize locally sourced components, contribute to an ideal state of food self-sufficiency. Through semi structured interviews and field observations, all local food plant species were catalogued and categorized by taxonomy, utility and cultural significance index. The staple foods: Rice (Oryza sativa L.) (50.00), corn (Zea mays L.) (44.70), and Casava (Manihot esculenta L.) (24,30) demonstrated the highest scores reflecting both the agricultural practice of corn-rice crop rotation and the dietary habits of Dlingo village that produce sego jagung, processed corn with a rice-like texture. Traditional salads such as urap-urapan, pecel, and rujak along with traditional soups such as jangan lodeh, jangan asem, and sop still dominates the dietary habits of people from Dlingo optimizing the local fruits, vegetables, and spices further sustainably providing food security.

The influence of regional industrial structure on the suitability of biomass energy development: A case study of 30 counties and cities in a cold region of China

The development of the biomass economy contributes to economic growth and social development. The study of biomass energy economy from the perspective of industrial structure type has the significance of promoting the optimization and upgrading of industrial structure, promoting the development of green industry and other aspects. It helps to promote the sustainable development of the economy and society. And industrial structure is a major component of the socio-economic system. Therefore, it is necessary to study the impact of material energy supply and demand from the perspective of regional industrial structure type. The objective of this study is to explore the relationship between the type of industrial structure and the development of biomass energy from the perspective of the economic direction, so as to fill the gap in this research perspective. Based on the three major industries generally regarded to constitute GDP - agriculture, industry, and service - the regression results of STATA software were used to calculate biomass potential according to crop distribution status. Using the ArcGIS analysis tool, the biomass break-even model related to resource supply-demand has been used to obtain the break-even status of the research area under six industrial structures in the past 20 years. According to research results, the break-even degree of biomass energy supply is between 0.109 and 1.236 when the industrial structure types of counties and cities are service-agriculture-industry and industry-agriculture-service. This is suitable for biomass energy development. When the industrial structure type of counties and cities is service-industry-agriculture, the break-even degree of biomass energy supply is between -0.428 and 0.277, which is not suitable for biomass energy development.

Spatial and temporal distribution of Scirtothrips dorsalis (Thysanoptera: Thripidae) and their natural enemies in Florida strawberry fields.

Given the recent invasion of Scirtothrips dorsalis Hood in North America, there is limited information regarding their distribution and population dynamics in cultivated small fruit crops. Therefore, we investigated the spatial and temporal distribution of S. dorsalis and their natural enemies in commercially produced strawberry fields in Florida. During 2 consecutive strawberry production seasons, 4 and 6 geographically separated strawberry fields were sampled and were divided into grids with 30-40 sampling points per field. At each sampling point, 4-5 leaf and flower samples were collected, and sticky traps were deployed. We quantified the occurrence of S. dorsalis as well as potential natural enemies, including Orius spp., Geocoris spp., and other predators such as long-legged flies. During both years, most of the S. dorsalis and natural enemies were found on field borders, and counts progressively diminished further into the interiors of plots and away from field edges. Cluster and outlier analysis revealed that S. dorsalis formed statistically significant clusters and that these "hot spots" remained in the same general locations throughout the season. There was a strong relationship between the occurrence of natural enemies and the presence of S. dorsalis, but the number of natural enemies was generally low compared to S. dorsalis. Our results indicate that targeting field borders for chemical control or planting strawberries away from natural areas containing potential alternative hosts for thrips may be an effective strategy for reducing agricultural inputs; however, future field assessments are needed to determine if these methods could replace the treatment of entire fields.

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.