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The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities. [ABSTRACT FROM AUTHOR]

Short-season cotton is a type of cotton variety characterized by its abbreviated cycle, rapid development, and concentrated flowering and boll setting. Compared with full-season cotton, short-season cotton facilitates an easier attainment of desirable maturation even when sown relatively late. This advantage of late sowing and early maturation eliminates the necessity for plastic film mulching, thereby creating opportunities for diversified double cropping, such as cotton–wheat, cotton–garlic, cotton–rape, and cotton–triticale systems. This paper provides a comprehensive review of the morphological, physiological, and molecular biological mechanisms underlying early maturity in short-season cotton. Furthermore, the significance and application of short-season cotton is discussed in relation to optimizing planting patterns and methods, promoting its cultivation in saline fields, developing machine-harvested cotton, and encouraging plastic mulch-free cotton planting. Based on these analyses and discussions, the paper proposes future strategies aimed at enhancing the breeding and cultivation of short-season cotton. These findings serve as valuable references for global breeding and cultivation research, and application of short-season cotton in the future. [ABSTRACT FROM AUTHOR]

Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China. [ABSTRACT FROM AUTHOR]

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the "Wheat × Maize System". Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding. [ABSTRACT FROM AUTHOR]

The ecological niche gradient is an important determinant of microbial community structure. In this paper, we studied variation in rhizosphere bacterial diversity and community composition along an ecological niche gradient. We used the high-throughput sequencing of 16S rRNA genes to study changes in the rhizosphere soil microbial communities of six grass and four shrub species during the secondary succession of abandoned farmland on the Loess Plateau of China. A structural equation model (SEM) was employed to disentangle the relative contribution of ecological niche and soil properties to bacterial diversity and community composition. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla of rhizosphere bacteria in all samples. During the dynamics of the plant niche from low to high, bacterial community composition transitioned from Actinobacteria + Acidobacteria to Proteobacteria + Bacteroidetes higher abundance. Moreover, the bacterial diversity and species richness changed with an increasing niche gradient, showing a clear differentiation in the rhizosphere bacterial community of grassland and shrubland. Further, diversity and species richness decreased from the middle niche of B. ischaemum to the poles, indicating that the succession process had not yet reached the climax community stage. Community assembly analysis suggested that the stochastic process gradually strengthened along the increasing ecological niche gradient, especially the drift effect. Furthermore, SEM analysis showed that the ecological niche had significant negative effects on soil properties and bacterial richness, while the effects on bacterial diversity and the stochastic processes of community assembly were weakened and insignificant. Altogether, our findings suggest that the complex interaction of the ecological niche with bacterial diversity and composition was determined by soil properties. Further, bacterial diversity was not necessarily higher with increasing ecological niche gradients. [ABSTRACT FROM AUTHOR]

Blueberries have high nutritional and economic value and are easy to cultivate, so they are common fruit crops in China. There is a high demand for blueberry in domestic and foreign markets, and various technologies have been used to extend the supply cycle of blueberry to about 7 months. However, blueberry grows in clusters, and a cluster of fruits generally contains fruits of different degrees of maturity, which leads to low efficiency in manually picking mature fruits, and at the same time wastes a lot of manpower and material resources. Therefore, in order to improve picking efficiency, it is necessary to adopt an automated harvesting mode. However, an accurate maturity detection model can provide a prerequisite for automated harvesting technology. Therefore, this paper proposes a blueberry ripeness detection model based on enhanced detail feature and content-aware reassembly. First of all, this paper designs an EDFM (Enhanced Detail Feature Module) that improves the ability of detail feature extraction so that the model focuses on important features such as blueberry color and texture, which improves the model's ability to extract blueberry features. Second, by adding the RFB (Receptive Field Block) module to the model, the lack of the model in terms of receptive field can be improved, and the calculation amount of the model can be reduced at the same time. Then, by using the Space-to-depth operation to redesign the MP (MaxPool) module, a new MP-S (MaxPool–Space to depth) module is obtained, which can effectively learn more feature information. Finally, an efficient upsampling method, the CARAFE (Content-Aware Reassembly of Features) module, is used, which can aggregate contextual information within a larger receptive field to improve the detection performance of the model. In order to verify the effectiveness of the method proposed in this paper, experiments were carried out on the self-made dataset "Blueberry—Five Datasets" which consists of data on five different maturity levels of blueberry with a total of 10,000 images. Experimental results show that the mAP (mean average precision) of the proposed network reaches 80.7%, which is 3.2% higher than that of the original network, and has better performance than other existing target detection network models. The proposed model can meet the needs of automatic blueberry picking. [ABSTRACT FROM AUTHOR]

In the context of the Chinese government's policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used "Heihe 43" soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 104 plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system. [ABSTRACT FROM AUTHOR]

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities and plays an important role in the hydrological cycle, as it is directly related to the development of agricultural water management. Therefore, it is a requirement to correctly simulate R and have the ability to separate the impacts due to climate change and human activities. In this paper, five single-type simulation models (Back Propagation Neural Network (BP), Non-Autoregressive (NAR), Radial Basis Function (RBF), Support Vector Machine (SVM) and TOPMODEL Hydrological Model (TOPMODEL)) were adopted to simulate the R to analyze the simulating quality by comparing the evaluation indexes like relative error (RE), relative mean squared error (RMSE) and Nash–Sutcliff Efficiency (NSE) with the combined hierarchical structure hydrological (CHSH) simulation model. In traditional studies, only the relative contribution of the impacts of human activities and climate change on R are considered; however, in this study, the relative contribution of each meteorological factor affecting R is included. To quantitatively analyze the impact of human activities and climate change on R, we used a CHSH simulation model to calculate runoff values for the Lancang River of China for a period of nine years (2005–2013). Our objective was to use this type of model to improve both the accuracy and stability of calculated values of R. For example, the RE, RMSE and NSE of simulated monthly R calculated with the CHSH model were 6.41%, 6.67 × 108 m3 and 0.94, respectively. These values substantiate the improved accuracy and stability of calculated values of R obtained with single-type simulation models (the SVM model, for instance, widely used in runoff simulations, and the RE, RMSE and NSE were 14.1%, 12.19 × 108 m3 and 0.87, respectively). The total contribution of human activities and climate change to R, respectively, accounted for 34% and 66% for the nine-year period based on the CHSH model. Furthermore, we adopted a vector autoregressive (VAR) model to analyze the impacts of the meteorological factors on R. The results from this analysis showed that R has a strong fluctuation response to the changes in precipitation (P) and potential water evaporation (Ep). The contribution rates of Ep, P and air temperature (Ta) to R were 15%, 14% and 2%, respectively. Based on the total climate change contribution, the corresponding contribution rates of Ep, Ta and P in the Lancang River of China were 32%, 30% and 5%, respectively. The values of R calculated with the CHSH model are more accurate and stable compared to values obtained with single-type simulation model. Further, they have the advantage of avoiding drawbacks associated when using a single-type simulation model. Moreover, moving away from the traditional method of separating the impact of meteorological factors on R, the vector autoregressive model proposed in this paper can describe the contribution of different meteorological factors on R in more detail and with precision. [ABSTRACT FROM AUTHOR]

Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction. [ABSTRACT FROM AUTHOR]

Soil salinization is detrimental to crop growth, agricultural yields, and environmental protection. Echinochloa frumentacea (Roxb.) Link is a pioneer species for the alteration of saline–alkali lands. In this paper, we examined the effects of intercropping between E. frumentacea and leguminous forages on saline land improvement in the saline–alkali soil of the Hetao-Ningxia Plain, China. We found that intercropping increased the diversity and richness of the arbuscular mycorrhizal fungi (AMF) community in the rhizosphere soil of E. frumentacea. Glomus was the dominant genus in the saline–alkali soil of the Hetao-Ningxia Plain, where Glomeraceae, VTX00067, VTX000193, and VTX000165 were the dominant species. Intercropping improved the activities of soil urease, sucrase, alkaline phosphatase, and catalase. The hay yields of E. frumentacea were correlated positively with soil enzyme activities, Chao1 index, and ACE index, and negatively with total water-soluble salt content. Together, intercropping between E. frumentacea and leguminous forages enhances AMF diversity and soil enzyme activities, which provides an agricultural practice for improving sustainability of the agro-ecosystem in saline–alkali areas. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas. [ABSTRACT FROM AUTHOR]

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc. [ABSTRACT FROM AUTHOR]

The functions of non-agricultural crops unearthed from archaeological sites mainly pertain to their usage as livestock feed. However, the studies of livestock feed have predominantly relied on qualitative analysis, which often lacks descriptive objectivity and relies heavily on subjective feelings and experiences. In this paper, we aim to address this gap by focusing on quantitative analysis, utilizing macro-plant remains from the data of seventy-five archaeological settlements and one archaeological investigation in northern China spanning the Neolithic Age to the Bronze Age, as well as stable isotope analyses of carbon and nitrogen. This research delves into various aspects, including the exploration of the plant resources and livestock farming and the categorization of feed types for cattle and pigs in captivity. By employing quantitative analysis, we can gain a more comprehensive and objective understanding of these subjects. This approach aligns with studies on ancient livestock management and feed diversity. In essence, the discussion of civilization development and social changes during the Pre-Qin period holds significant value when considering forage analysis, just as crop analysis has proven insightful. By focusing on the quantitative analysis of non-agricultural crops and their role as livestock feed, we can shed light on important aspects of ancient societies and their agricultural practices. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies. [ABSTRACT FROM AUTHOR]

Plant protection unmanned aircraft vehicles (UAVs) were developed rapidly in China. The operation performances of different models of UAVs were different. This paper systematically studied droplet deposition distribution; pesticide-mixture utilization rate; operational efficiency; wheat-disease control efficacy; and a comprehensive score of three types of UAVs, a boom sprayer, and a knapsack sprayer. The results showed the descending order of the droplet penetration rate of the pesticide application equipment (PAE) was boom sprayer; UAVs; and knapsack sprayer. The pesticide-mixture utilization rates of the UAVs and boom sprayer were more than 50% while that of the knapsack electric sprayer was only 27.8%. The UAVs' average labor productivity was 5.75 ha per man-hour, which was slightly less than that of the boom sprayer and 21.3 times that of the knapsack sprayer. The control efficacy of each machine on wheat Fusarium head blight was more than 90%. The average performance comprehensive score of the UAVs was 0.812, which was slightly lower than the score of 0.929 for the 3WPZ-700 self-propelled boom sprayer but much higher than the score of 0.399 for the 3WBD-18 knapsack electric sprayer. The results clearly showed the potential of UAVs for improving the pesticide-mixture utilization rate and operational efficiency, as well as the wheat Fusarium head blight control efficacy. [ABSTRACT FROM AUTHOR]

C, N and P are the key biogenic elements of terrestrial ecosystems, and their biogeochemical processes regulate nutrient cycling and play a key role in restoring degraded ecosystems. In this paper, the grassland ecosystem under artificial restoration measures (Dactylis glomerata (DG), Lolium perenne (LP), Lolium perenne + Trifolium repens (LT)), and the natural restoration measures (NG) in a typical karst plateau desertification control area of southwest China, were taken as the research object. The C, N, and P concentrations and the ecological stoichiometry of the plant–soil–microorganism system in grasslands under different restoration measures in the karst desertification area were explored. We established the following findings: (1) Compared with NG, the C, N and P concentrations of plants and soil in DG, LP and LT were higher, and LT was the highest. (2) The microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and microbial biomass phosphorus (MBP) concentrations in LT was also the highest. (3) The C:N ratio of plant and soil indicated that the N mineralization and nutrient release rate of DG, LP and LT were stronger than that of NG, and the plant growth of NG was most seriously limited by nitrogen. The N:P ratio in plant and soil indicated that the grassland was limited by P in the four treatments. (4) The result of correlation analysis showed that the cycling process of C, N and P in the plant–soil–microorganism system was coupled, and that the elements closely affected each other. In general, the effect of artificial restoration on a degraded ecosystem was relatively better than the natural restoration for increasing plant, soil and microbial nutrient concentrations, especially in the mixed-seed ecosystem of leguminous and gramineous forage. However, in the future, grassland management, appropriate N fertilizer or N-fixing plants and P fertilizer should be considered to improve the limitation of plant N and P, so as to realize the sustainable development of grasslands and the restoration of degraded ecosystems in the karst desertification control area. [ABSTRACT FROM AUTHOR]

Synergistically enhancing and realizing the value of grassland ecosystem services (ES) for economic activity is an important but challenging task for achieving sustainability in the karst desertification control (KDC). However, how to use grassland ES value characteristics in the KDC to make decisions on ES improvement, human well-being enhancement, and sustainable development remains unclear. In this paper, we took the contiguous region of karst desertification in Yunnan-Guangxi-Guizhou, China, a global hotspot, as the study area. Based on the valuation of the gross ecosystem product (GEP) and county economic intensity, we analyzed the structural and spatial characteristics of grassland GEP in the KDC using spatial analysis methods. We found that: (1) the grassland GEP in the KDC is mainly distributed in counties with low economic intensity (86.05% of the total number of counties) and vulnerable to losses caused by the livelihood of farmers; (2) the grassland GEP in the KDC is spatially small and scattered (the geographic concentration lies between 0.015 and 0.237), which makes it difficult to form industrial scale advantages; (3) the public product index (66.22–96.77%) and industry scale concentration (97.87–99.86%) of grassland GEP in the KDC are high, and most of the GEP is difficult to transform on the private market. Based on our findings, we proposed three corresponding recommendations for economic decision-making. The results of this study can provide a reference for economic decision-making regarding the management of grassland ES in karst areas with similar conditions and beyond. [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [ABSTRACT FROM AUTHOR]

In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (Rm-avg) in the middle area was 66.6%, while the Rm-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the Rm-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. [ABSTRACT FROM AUTHOR]

Soil food webs are extremely complex as they contain diverse organisms. Feeding preference, also known as prey selection, is an important determinant of soil community compositions. However, the feeding preferences of nematodes are commonly ignored in ecology research. In this paper, the population dynamics and feeding preferences of three bacterial-feeding nematodes (i.e., Caenorhabditis elegans, Protorhabditis spp., and Acrobeloides spp.) for eight bacterial prey species were evaluated. Protorhabditis and Acrobeloides were isolated from a paddy soil in subtropical China. C. elegans, the most common model system for biological research, was used as a control in this study, and it was revealed that C. elegans could feed on all the eight bacteria strains. Protorhabditis could only survive when fed E. coli and Bacillus thuringiensis. Acrobeloides could only survive when fed E. coli and B. aryabhattai. During 10 days of culture, C. elegans populations reached the maximum in 5–7 days, and most C. elegans populations exceeded 10,000 individuals. The two Protorhabditis populations on E. coli and Bacillus thuringiensis included less than 800 individuals during 10 days of culture. Acrobeloides population on B. aryabhattai reached the maximum (7799 individuals) on day 8, while on E. coli was its population included less than 500 individuals. These results indicate that different nematode species indeed have distinct feeding preferences. In addition, the population dynamics of the two soil nematodes isolated from soil could not fully match with their inferred life-history strategies (i.e., cp values, and a 1-5 colonizer-p-ersister series that range from r-strategists to K-strategists). Our findings highlights the existing deficiencies in the understanding of the feeding behavior and the life-history strategies of soil nematodes. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a non-essential, highly phytotoxic metal and damages ramie plant growth and development even at low concentrations. Ramie is one of the most significant crops in China, with excellent fiber quality and immense industrial importance. Planting Cd-tolerant ramie cultivars can prevent yield loss on contaminated soil. Previously, significant efforts have been made to develop Cd tolerance in ramie. However, the Cd tolerance mechanism is still not fully understood; hence, breeding industrial crops is critical to tackling the ongoing challenges. Cd tolerance is a complex genetic mechanism requiring high-level molecular studies to clarify the genes network. Genetic studies have identified several Cd-tolerant genes in ramie, which led to the development of several ramie cultivars suitable to grow on toxic soils; however, due to the continuous rise in Cd toxicity, potent molecular tools are critical in modern-day breeding programs. Genetic engineering, and transcriptome analysis have been used to develop abiotic stress tolerance in ramie, but QTL mapping and clustered regularly interspaced short palindromic repeats (CRISPR) are rarely studied. However, studies are still limited in addressing this issue. This review critically elaborated on using QTL mapping, transcriptomes, transcription factors, CRISPR/Cas9, and genetic engineering to enhance Cd tolerance in ramie. These genes/QTL should be transferred or edited into sensitive cultivars using genetic engineering or CRISPR/Cas9. CRISPR/Cas9 is highly recommended because it provides targeted gene editing in ramie, its use is limited and can address the research gaps, and it would revolutionize the field of agriculture. Limitations, gaps, and future potential are briefly discussed. This review paper presents new clues to help future researchers comprehensively understand Cd tolerance in ramie and develop tolerant cultivars for industrial purposes. [ABSTRACT FROM AUTHOR]

Tea (Camellia sinensis L.), as one of the most important cash crops in China, plays an important role in increasing farmers' incomes and guaranteeing a high quality of life. Tea production has been greatly influenced by both climate change and economic development in China. However, without a scientific understanding of the interaction mechanism of climate change and the impetus from rapid economic development on tea production practices in China, it is difficult to take adaptive actions to meet the climate change challenges for the tea industry. In this paper, we firstly assessed the potential impacts of climate change on tea climate suitability by empirical formula calculation using meteorological data; then, the effects from the additional climatic stress due to warming on tea production were detected with the annual statistical tea yield record on a municipal level. The contribution of socioeconomic development to the tea industry was evaluated with the comparison of the movement of China's national economy's and tea industry's gravity center during the period of 1987–2017. Finally, a conceptual adaptation framework was built to demonstrate the interaction mechanisms between climate change, tea production, and the economic development. The results showed that there was a negative impact of climate change on tea production in mainland China, with the percentage of high tea climate suitability (>0.9) areas dropping by 45% to 32%, while opportunities of enlarging the tea cultivating area emerged in the north tea production region where the tea climate suitability increased. We found that the tea planting area expanded northwards from 33° N in 1987 to 35° N in 2017 to take advantage of the favorable climatic resources due to warming, and tea planting decreased at an altitude of 100–400 m while increasing to higher altitude of 400–2000 m to avoid hot temperature damage and seek the optimum environment in high mountainous areas for tea production. In addition, the tea production moved westward along the longitude, decreasing obviously at 117–121° E while increasing significantly at 98–104° E and 107–110° E. Meanwhile, the tea production gravity center showed a westward movement consistent with the national economic gravity center moving trend, which means that tea industry development was driven by multiple socioeconomic factors and climatic forcings. A conceptual framework was built in this paper, aiming to show a robust adaptation mechanism for the tea system to maximize the benefits and minimize the damages from the altered climatic resources under rapid economic development in mainland China. The results in this study would help deepen the understanding of the adaptation process and practices for tea production in mainland China. [ABSTRACT FROM AUTHOR]

China is embarking on a new journey to build a comprehensive socialist modern state in the new era. Modernization of agriculture and forestry is the basis of agricultural modernization, but China's traditional agriculture and forestry industry are facing a more serious crisis of independent research and innovation. As the listed agroforestry companies are directly facing the demands of the market, it becomes essential to study the technological innovation of listed agroforestry companies. Therefore, this paper investigates the relationship between R&D innovation, corporate management, supply chain management, growth capacity, debt servicing capacity, and corporate performance of listed agroforestry companies. Based on the annual panel data of agroforestry listed companies in the CSMAR database from 2010–2021, the empirical study was conducted using panel PVAR models, OLS, 2SLS, LIML, and GMM estimation. The findings show that: (1) Granger causes affecting the supply chain management of listed companies in agroforestry are corporate management, debt servicing capacity, and growth capacity. Granger causes affecting the debt servicing capacity of listed companies in the agroforestry industry are R&D innovation, growth capacity, and corporate performance. Among them, there is a causal influence relationship between debt servicing capacity and corporate performance. (2) R&D innovation, corporate management, supply chain management, growth capacity, debt servicing capacity, and corporate performance contribute the most to its own impulse response, with an average contribution of 87.4%, 81.8%, 86.9%, 96.9%, 86.5%, and 94.7%, respectively. Compared to the other variables, the impulse response contribution of debt servicing capacity to corporate performance was the largest. (3) When supply chain management and growth capability play a fully mediating role, there is a significant positive effect of R&D innovation on corporate performance. Finally, we offer some policy recommendations and suggestions to the Chinese government, as well as some suggestions on how Chinese-listed companies in the agroforestry industry can improve their corporate performance. This paper provides a Chinese case study on the corporate performance of listed companies in the global agroforestry industry. [ABSTRACT FROM AUTHOR]

Greenhouse technology has advanced over the past few decades in terms of environmental control (e.g., indoor temperature, relative humidity, and CO2 concentration). Ventilation is an effective way to adjust the indoor climate. Natural ventilation has gained significant research attention recently because of its low energy requirement. To evaluate the ventilation effectiveness, the ventilation rate is often used. This review summarizes the published review papers related to greenhouse ventilation. Ventilation models are reported under different conditions, including wind-induced, buoyancy-induced, and combined effects-induced ventilation in greenhouses. The influencing factors are described, such as the wind and buoyancy strength and distribution, greenhouse geometry, and vent arrangement. Various methods assessing natural ventilation in greenhouses are introduced, consisting of tracer gas techniques, the pressure difference method, the energy balance method, the emptying fluid-filling box method, and numerical simulation. The values of the key coefficients deduced and used in the literature are listed. This paper reports what has been done in the world and where we can start to develop dynamic ventilation models for solar and tunnel-type greenhouses in China. Further valuable investigations are discussed. The pressure distribution function in greenhouses with horizontal openings, a model for cross-ventilation induced by combined wind and buoyancy force, and an analytical plant-considered ventilation model with higher applicability are described. To ensure the accuracy of the ventilation models, other environmental variables, especially geography-dependent ones, can be added. More criteria are suggested to evaluate the ventilation performance rather than the ventilation rate to provide a comprehensive assessment. [ABSTRACT FROM AUTHOR]

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China's winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Identifying the relationships between the yield of rainfed maize and planting densities as well as the hybrids used is crucial for ensuring the sustainable development of the grain industry in high latitude China. In this study, we collected 108 grain yield date points from our multiyear (2017–2020) field experiments and combined 213 data points collected from 21 published papers to appraise the impact of planting density and hybrids on maize yield. It was found that grain yield forms a curvilinear relationship with plant density as it increased from 22,500 to 112,500 plants ha−1. The optimum plant density (OPD) was determined to be 72500 plants ha−1, with a maximum maize grain yield of 10.56 Mg ha−1. The interannual variability in grain yields among hybrids with different planting densities was mainly due to the differences in dry matter (DM), especially post-silking. Grain yields increased significantly with a rise in the proportion of post-silking DM to DM at maturity. In addition, both the collected literature and our field experiments showed that the OPD was positively correlated with solar radiation accumulated during the maize growth period and with each hybrid's year of release. This study suggests that increasing plant density and selecting new hybrids with suitable growth periods are effective approaches for increasing grain yield in high latitude China. [ABSTRACT FROM AUTHOR]

Water and fertilizer play an important role in crop growth in dryland areas. It is a necessity to improve the water use efficiency (WUE) of the crop once the water resource is limited. In northern China, where there is a wide shortage of water resources, it is therefore necessary to investigate how fertilization affects the WUE of spring maize and to quantify the effects. A total of 33 published peer-reviewed papers were collected, and a meta-analysis and random forest model analysis were performed with 364 WUE comparisons, aiming to explore the effects of fertilization on the WUE of spring maize and to clarify the optimal conditions for WUE under fertilizer management. The results showed that fertilization significantly increased the WUE of spring maize by 56.72% (P < 0.01) when compared with non-fertilization. The WUE effect under the organic–inorganic fertilizer combination (MNPK) was approximately twice as high as that under inorganic fertilizer (NPK) or organic fertilizer (M). The greatest increase in WUE occurred at 0–100 kg ha−1 of nitrogen application (NA). Under environmental conditions including 7 ≤ mean annual temperature in the test year (T) ≤ 10 °C, 400 ≤ mean annual precipitation in the test year (P) ≤ 600 mm, and mean altitude (A) > 1500 m, and soil conditions including 10 ≤ soil organic matter content (SOM) ≤ 14 g kg−1 and available phosphorus (AP) < 5 mg kg−1, the fertilization optimally enhanced the WUE of spring maize when the agronomic measures of ridge–furrow planting (RFP) and mulching film (MF) were used. The random forest model analysis indicated that the influence factors (i.e., fertilizer regimes, environmental factors, soil factors, and agronomic measures) caused 65.62% of the variation in spring maize WUE effects, while in all influence factors, fertilizer types related to fertilizer regimes caused the most variation. The initial available potassium (AK) and available nitrogen (AN) of the soil were negatively correlated to the WUE effect, indicating that fertilization imposed a better effect on the WUE of spring maize when the soil was infertile. Fertilization significantly increased the WUE of spring maize, and organic and inorganic fertilizer application provided an effective measure for the sustainable development of spring maize in northern China. After clarifying the required conditions for fertilization increasing WUE, high-efficiency water use may be achieved. [ABSTRACT FROM AUTHOR]

In China, the treatment of pig manure for land application is an important issue. In this paper, green synthesized iron nanoparticles (G-nFe) were evaluated for their effectiveness in the aerobic composting of pig manure. G-nFe were evenly mixed into the compost, and the pH and oxidation-reduction potential (ORP) of the compost in the presence of G-nFe both decreased. FTIR showed that G-nFe promoted the formation of humus during composting. The addition of G-nFe (200 mL kg−1) to the compost also promoted bacterial growth, significantly increasing the colony-forming units (CFU, up to 25.6 × 104 CFU mL−1) and OD600 (up to 0.634) in 5 days. Furthermore, G-nFe promoted the fermentation of the pig manure, thus reducing the phytotoxicity of the compost produced. For example, the final volatile solids (VS) degradation rate and seed germination index (GI) of the compost were the highest and reached 20.8% and 0.76%, respectively. Finally, G-nFe improved both the bacterial diversity and community richness of the compost. This indicated that the addition of G-nFe could result in the prolonging of the acidic fermentation time during composting, leading to increased compost maturity. Overall, the beneficial impact of G-nFe on compost was far greater than the potential harm to bacterial communities in the compost and perceived phytotoxicity. [ABSTRACT FROM AUTHOR]

The widespread use of pesticides poses significant risks to food and environmental safety. Imidacloprid is one of the most effective neuroactive neonicotinoid insecticides and is effective against a broad spectrum of piercing–sucking pests. A rapid, efficient, and high-throughput analysis method for the determination of imidacloprid was developed in four minor crops with six matrices (bamboo shoot, winter jujube, fresh and dry D. officinale, and fresh and dry Fritillaria) by solid-phase extraction and HPLC-MS/MS. The procedure showed satisfying recoveries (72~111%) and RSDs (1~13%). A total of 288 samples were tested in China (Aba and Luan). Imidacloprid residues were 100% detected in fresh and dry D. officinale and winter jujube, with concentrations ranging from 0.048 to 1.550 mg·kg−1. Imidacloprid residues were also abundant in fresh and dry Fritillaria (maximal concentration of 0.021 and 0.063 mg·kg−1, respectively), followed by bamboo shoot, which had the lowest detection rate of imidacloprid (6%). Using the risk quotient (RQ) method, the long-term (RQc) and short-term (RQa) dietary risks of imidacloprid in bamboo shoot, winter jujube, fresh and dry D. officinale, and fresh and dry Fritillaria were further monitored. Based on the imidacloprid residues in this paper, the RQc and RQa were 15.03% and 0.0008~1.7604%, respectively. The RQ values were far less than 100%, showing that Chinese consumers face little health risk as a result of imidacloprid intake. [ABSTRACT FROM AUTHOR]

There is a great demand for dragon fruit in China and Southeast Asia. Manual picking of dragon fruit requires a lot of labor. It is imperative to study the dragon fruit-picking robot. The visual guidance system is an important part of a picking robot. To realize the automatic picking of dragon fruit, this paper proposes a detection method of dragon fruit based on RDE-YOLOv7 to identify and locate dragon fruit more accurately. RepGhost and decoupled head are introduced into YOLOv7 to better extract features and better predict results. In addition, multiple ECA blocks are introduced into various locations of the network to extract effective information from a large amount of information. The experimental results show that the RDE-YOLOv7 improves the precision, recall, and mean average precision by 5.0%, 2.1%, and 1.6%. The RDE-YOLOv7 also has high accuracy for fruit detection under different lighting conditions and different blur degrees. Using the RDE-YOLOv7, we build a dragon fruit picking system and conduct positioning and picking experiments. The spatial positioning error of the system is only 2.51 mm, 2.43 mm, and 1.84 mm. The picking experiments indicate that the RDE-YOLOv7 can accurately detect dragon fruits, theoretically supporting the development of dragon fruit-picking robots. [ABSTRACT FROM AUTHOR]

Dandong has become the largest strawberry production and export base in China. Strawberry black spot disease is widespread and causes significant economic losses to strawberry growers in both the growing and harvest seasons. Until now, no study has reported the presence of the Alternaria species, the pathogen of strawberry black spot disease, in Dandong, Liaoning province, China. In 2020–2022, 108 isolates were obtained from strawberry leaves with typical symptoms of strawberry black spot disease from 56 major professional growing operations. Combined with morphological and molecular characteristics, the majority of isolates were identified as A. tenuissima (78 isolates, 72.2%), which had established total supremacy, followed by A. alternata (30 isolates, 27.8%). The pathogenicity results show that A. tenuissima and A. alternata are the two main pathogenic factors of strawberry black spot disease, the disease indexes of which were designated as 49.6–100.0% and 20.4–59.5%. To our knowledge, this paper is the first to identify A. tenuissima and A. alternata as causing black spot disease in strawberries in Dandong, China. [ABSTRACT FROM AUTHOR]

Prof. Yuan devoted his whole life to the research of hybrid rice. On the first anniversary of Prof. Yuan Yuan's death, our research team wrote a paper to commemorate him. In this paper, we recalled his life, his research progress and achievements of hybrid rice. He led and guided his research team to overcome difficulties in hybrid rice research. Hybrid rice has made important contributions to China and world's food security. He is a great researcher worthy of our memory forever in the world. [ABSTRACT FROM AUTHOR]

Leymus chinensis is a primary plant in the meadow steppe and typical steppe of China. With global warming and increasing grazing intensity, grassland degradation is being exacerbated. To better protect the L. chinensis grassland in this area and provide a theoretical basis for restoring it, this paper compared the germinable seed bank in the soil and the germination characteristics of L. chinensis (including initial germination time, duration of germination, germination termination time, germination dynamics, and germination index) in free-grazing and enclosed areas. At the same time, combining information about the density of L. chinensis on the ground and previous research results, a comprehensive analysis was conducted. The major results were: (1) there was no significant difference in the number of germinable seeds in the soil between the free-grazing area and the enclosed area, and these seeds were mainly concentrated in the 0–2 cm soil layer. (2) The free-grazing area resulted in a significant increase in the number of germinable L. chinensis seeds and advanced the initial germination time. (3) The number of soil germinated seeds and the number of L. chinensis germinated seeds decreased with the increase in soil depth. (4) Livestock grazing behaviours increased seed burial, thus improving the L. chinensis germination rate. At the same time, the L. chinensis seed bearing percentage and seed quality and the number of germinable L. chinensis seeds were significantly higher in the free-grazing area than in the enclosed area. However, this result still needs to be further explored. [ABSTRACT FROM AUTHOR]

The effective use of nutrient-rich crop straw is an important way to use resources efficiently and to sustain agricultural development. This meta-analysis study collected and analyzed the data of 6788 observations published in 238 peer-reviewed papers to investigate differences in soil C-N fractions and yields of paddy soils under different straw-return amounts. This large dataset was also used to quantify the degree of influence of factors such as climate characteristics, soil properties, N fertilizer application rates, straw-rotting agent addition, rice varieties, and straw return methods. The results showed that straw return amounts improved soil alkaline-hydrolysable N (7%), total N (10%), organic C (11%), the C:N ratio (8%), rice N accumulation (12%), and overall yield (18%). The most significant effect was in northeast China fields for total soil nitrogen (TN) content and yield with increases of 13% and 22%, respectively. We also found more effective N utilization and a greater rice yield when 220–260 kg ha−1 N fertilizer was applied with 20–30 kg ha−1 straw-rotting agent with the total amount of straw return. These findings have important implications for choosing appropriate conditions and field management practices and to improve rice yield in China. [ABSTRACT FROM AUTHOR]

As skilled talent is the core element in optimizing the industrial structure, clarifying the factors that influence the lack of skilled talent in the agricultural industry is essential. The lack of skilled talent in the agricultural industry is a complex problem. The author based this study on the push–pull theory and used the fuzzy-set qualitative comparative analysis (FSQCA) research method to explore the "joint effect" of the factors that affect the lack of skilled talents in the agricultural industry in 14 cities of Hunan Province. This paper found that indicators such as the rural industry status, rural medical level, agricultural and forestry financial investment, urbanization construction, regional economic development level, and local government officials were all presented under sufficient conditions. These influencing factors have a "combined effect" on the agricultural industry's lack of skilled talent. The rural industry status indicator was the core condition, appearing in all the configurations. This article's main marginal contribution is that it defined three conditional configurations for the lack of "present" agricultural skilled talents, and dependent configurations are consistent. Among the three conditional configurations, the "rural industry + economic construction and financial investment" configuration is the most powerful way to retain skilled agricultural talents. In addition, the causal relationship between the lack of "present" agricultural industry skilled talents and the lack of "absent" agricultural industry skilled talents are asymmetric. The primary task is to develop rural industries to solve the problem of the shortage of skilled talent in the agricultural industry. Additionally, then implement the 'industry+' combination policy. So the government must create related policies in the following areas: the supply of essential public services in rural areas, the economic construction of prefectures, the development of county urbanization, and the financial investment in agriculture, forestry, and water conservancy. [ABSTRACT FROM AUTHOR]

In order to ensure canopy area coverage with the most compact mechanical configuration possible, this paper proposes a configuration optimization design method of dual-manipulator to meet the research and development needs of an apple-efficient harvesting robot using the typical tree shape of a "high spindle" in China as the object. A Cartesian coordinate dual-manipulator with two groups of vertically synchronous operations and a three-degree range of motion based on the features of the spatial distribution of fruits under a typical canopy of dwarf and close planting was designed. Two-stage telescoping components that can be driven by both gas and electricity are employed to ensure the picking robotic arm's quick response and accessibility to the tree crown. Based on the quantitative description of the working space and configuration parameters of the dual-manipulator, a multi-objective optimization model of the major configuration parameters is constructed. A comprehensive evaluation method of the dual-manipulator configuration based on the CRITIC–TOPSIS combined method is proposed. The optimal solutions of the lengths and elevations of upper and lower telescopic parts of the dual-manipulator and the distance from the mounting base of the outer frame of the dual-manipulator to the center of the tree trunk are determined, which are 1119.3 mm and 39.4°, 898.7 mm and 26°, 755.3 mm, respectively. The interaction between the configuration parameters of the dual-manipulator and its working area is then simulated and examined in order to verify the rationality of the optimum configuration settings. The results show that the optimal configuration of the dual-manipulator can fully cover the target working space, and the redundancy rate is 16.62%. The results of this study can be utilized to advance robotic fruit-picking research and development. [ABSTRACT FROM AUTHOR]

Air-assisted sprayers are the mainstream orchard plant protection machinery in China. During the usage of sprayers, the pesticide droplets carried by strong air jets from sprayers enter into the target canopy. Therefore, the distribution of airflow field in the canopy has significant influence on the spatial movement of the droplets and the adhesion and penetration of the droplets inside the canopy. To enhance the working performance of sprayers, it is imperative to study their use in tree canopies. Based on computational fluid dynamics (CFD), the k-ε turbulence model, and the SIMPLE algorithm, a 3D simulated model of the spatial distribution of the airflow field in and around the tree canopy was established based on the porous model in this paper. The model was used to simulate and calculate the air field distribution of an air-assisted orchard sprayer under different operating parameters. The results showed that the optimal operation effect was achieved when the driving speed and the air speed of the fan outlet were 1 m/s and 20 m/s, respectively, while the air speed in the canopy was not less than 2 m/s. The 36 points measured in the canopy were compared with the simulated results through field experiments. It showed that average relative error between the measured and simulated values was 13.85%, and the overall goodness-of-fit was 0.97656. The model accurately simulated the airflow distribution in the canopy and provided a basis for optimizing the operating parameters of the air-assisted sprayers in orchards. [ABSTRACT FROM AUTHOR]

As a bridge and link between China and Central Asian countries, logistics infrastructure plays an important role in the process of forming a new development pattern of "dual circulation" and mutual promotion. Based on the panel data of Central Asian countries from 2010 to 2019, this paper uses random effect model to analyze the impact on the cross-border agricultural product supply chain between China and Central Asian countries. The results show that the logistics development level, railway transport connectivity, road transport connectivity, economic development level, market size, and common border of Central Asian countries positively affect the development level of the supply chain at different significance levels. Among them, the degree of railway transport connectivity has the greatest impact on the supply chain, followed by the common border, and then the level of logistics development. Moreover, the geographical advantage cannot be underestimated and it plays a positive role in promoting the high-quality development of cross-border agricultural product supply chain. China borders Kazakhstan, Kyrgyzstan, and Tajikistan, and the agricultural product import and export trade quota is significantly greater than that of other Central Asian countries. Based on the analysis, we propose to strengthen efforts to build a high-quality logistics system, build an efficient land transport network, and break through cross-border logistics congestion and difficulties. [ABSTRACT FROM AUTHOR]

As the largest inland river basin in China, the Tarim River Basin is an important energy base and a key cotton-producing area in China. Therefore, the harmonious development of water resources, energy resources and cotton resources in the Tarim River Basin is a lifeline for stable quality and efficient development in this region. In this paper, the water–energy–cotton system of the Tarim River Basin is constructed and the weight of each index in the system is determined by using the entropy method. On this basis, the comprehensive development index of each subsystem and the water–energy–cotton system is calculated to measure the comprehensive development level of each system. Then, the coupling relationship between the three resources is quantitatively analyzed by using the coupling coordination degree model, and the mutual influence and strength of interaction between each system are calculated. Finally, a fractional regression model is established to analyze the factors affecting the coordinated development of the water–energy–cotton system in the Tarim River Basin. The results show that the comprehensive development index of the Tarim River Basin fluctuates obviously and the comprehensive development level of water resource systems is higher than that of the cotton resource and energy resource systems, and there are regional differences between each subsystem and the comprehensive development index of water–energy–cotton system. The coupling coordination degree of the basin is the primary coordination on the whole; however, there are significant differences in the coupling coordination degree of each region. In terms of influencing factors, the area of soil erosion treatment and water consumption in ecological environments both play a positive role in promoting the coupling coordination degree, and population size and GDP will hinder the development of the coupling coordination degree to a certain extent. [ABSTRACT FROM AUTHOR]

Biofortification is a new agricultural intervention to alleviate hidden hunger in China and even the world. Exploring the impact of farmers′ adoption of biofortification on return on investment (ROI), which is calculated from farmers′ production net income and profitability, can provide empirical support for the development of sustainable agriculture. This paper examines the impact of the adoption of biofortification on ROI using cross-sectional data from a survey of farmers in China. An endogenous switching regression model that accounts for selection bias was used in the research. The empirical results revealed that the adoption of biofortification has a positive and statistically significant impact on ROI. A heterogeneity analysis also revealed that comparatively high annual income adopters and female adopters tend to benefit more from the adoption of biofortification than middle- and low-income adopters and male adopters. [ABSTRACT FROM AUTHOR]

Oil tea (Camellia oleifera Abel.) is a high-quality woody oil crop unique to China and has extremely high economic value and ecological benefits. One problem in oil tea production and research is the worldwide confusion regarding oil tea cultivar nomenclature. The purpose of this study was to automatic recognize some oil tea cultivars using bilinear attention network. For this purpose, we explored this possibility utilizing the bilinear attention network for five common China cultivars Ganshi 83-4, Changlin 53, Changlin 3, Ganshi 84-8, and Gan 447. We adopted the bilinear EfficientNet-B0 network and the convolutional block attention module (CBAM) to build BA-EfficientNet model being able to automatically and accurately recognize oil tea cultivars. In addition, the InceptionV3, VGG16, and ResNet50 algorithms were compared with the proposed BA-EfficientNet. The comparative test results show that BA-EfficientNet can accurately recognize oil tea cultivars in the test set, with overall accuracy and kappa coefficients reaching 91.59% and 0.89, respectively. Compared with algorithms such as InceptionV3, VGG16, and ResNet50, the BA-EfficientNet algorithm has obvious advantages in most evaluation indicators used in the experiment. In addition, the ablation experiments were designed to quantitatively evaluate the specific effects of bilinear networks and CBAM modules on oil tea cultivar recognition results. The results demonstrate that BA-EfficientNet is useful for solving the problem of recognizing oil tea cultivars under natural conditions. This paper attempts to explore new thinking for the application of deep learning methods in the field of oil tea cultivar recognition under natural conditions. [ABSTRACT FROM AUTHOR]

The development of introduced pulse varieties has made valuable contributions to the development of the global agricultural industry, and China is one of the largest pulse producers in the international market. A special type of pulse, the red kidney bean, has made a major contribution to improving the rural economy. Taking Kelan County, Shanxi Province, as an example, this paper expounds on the formation of the kidney bean industry and its impact on local development. The existing research used a qualitative case study (QCS) method to examine the driver and impact of kidney beans in the agricultural industry. This study found that (1) the development of the kidney bean industry has benefited from its adherence to a market demand-oriented strategy, focusing on breeding and retaining excellent varieties, and vigorously supporting the construction of technical systems and the cultivation of the main body of the industrial chain. Developing new varieties, creating brands, and industrial integration are the key driving forces for development. (2) The kidney bean industry promotes local development by increasing farmers' income, forming a more complete kidney bean supply chain, highlighting the brand effect, and promoting sustainable rural development. This study suggests that disease-resistant and mechanized-adapted varieties need to be developed in the future. Market and demand trends should be constantly monitored when determining reproductive paths. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI. [ABSTRACT FROM AUTHOR]

Water use efficiency ( W U E ) is one of the most widely used indicators in agricultural water management. Although this indicator has obvious advantages, it is limited to measuring the relationship between crop yield and corresponding water use. In recent years, many researchers have noted that understanding the economic efficiency of water use ( E E W U ) could have great water-saving potential, while it has been poorly investigated with respect to China's agricultural water management. This paper assesses E E W U through a household farmer-level survey in the piedmont region of North China. First, E E W U of crops are estimated based on agricultural water consumption (including irrigation water and effective precipitation) and profit (including gross profit and net profit); Second, the impact of monthly price changes in 2019 and annual price changes in 2014–2019 on E E W U is analysed. Main conclusions are as follows: (1) E E W U values of cash crops such as apple and cauliflower are much higher than those of grain crops such as wheat and maize; (2) For different crops, the median economic efficiency of irrigation water ( E E W i U ) and total water ( E E W t U ) range from 31.71 to 99.54 ¥/m3 and 11.31 to 44.05 ¥/m3, respectively; (3) The multi-year average E E W i U and E E W t U ranged from 4.75 to 63.99 ¥/m3 and from 2.67 to 31.71 ¥/m3, respectively. Economic efficiency of water use shows a slightly downward trend in the period of study, which would contradict the trend towards the use of more water-efficient technologies and shows an even larger margin of improvement in the domain of agricultural water efficiency. The results provide a powerful reference for the management of agricultural water use through economic leverage. [ABSTRACT FROM AUTHOR]

Agriculture is one of the three major industries in China, and its level of development occupies an important position in the development of the national economy. At present, China vigorously promotes the strategy of rural revitalization, which provides new opportunities for agricultural modernization. Since modern agriculture and regional economy are coupled in a mutually influencing and constraining relationship, this paper constructs a coupled and coordinated evaluation index system of agricultural modernization and regional economic development. Using the entropy value method and coupled coordination degree model, the comprehensive development level and coordinated development degree of agricultural modernization and regional economic development of 31 provinces, autonomous regions, and cities in China from 2011 to 2020 were evaluated, revealing the time evolution and spatial distribution characteristics of the two systems. The results show that: (1) In the past decade, China's agricultural modernization and regional economy have made some progress in terms of their respective development levels and the degree of coupling and coordination between them. (2) In terms of spatial evolution, there are obvious regional differences between the two systems, and the coupling coordination degree shows a "ladder" distribution pattern from the east to the central, northeastern, and western parts of the country. (3) Regions with a high level of regional economic development also have a relatively high degree of coupling and coordination between agricultural modernization and regional economic development. Our results provide insights into the economic effects of agricultural modernization in China and provide useful insights into the future direction of agricultural development programs in countries around the world. [ABSTRACT FROM AUTHOR]

Agriculture is the foundation of every country's survival and development. This paper analyzes the interaction between the business environment, agricultural opening-up and high-quality agricultural economy from the perspective of China's provinces. According to the panel data of 31 provinces and municipalities in China from 2009 to 2019, the empirical analysis was carried out based on the panel vector autoregression (PVAR) model. The results show that there is a quantitative interaction between China's business environment, agricultural opening-up and high-quality agricultural development, and the long-term time effect is very obvious. Specifically, (1) the impact of the business environment and the high-quality development of agriculture on itself is decreasing year by year. The agriculture opening-up itself has a strong impact and has always maintained strong stability. (2) The high-quality development of the agricultural economy has little impact on the business environment and the opening of agriculture to the outside world, but it has a certain stable and continuous effect. It is particularly important that the agricultural opening-up has an increasingly strong effect on the business environment and the high-quality development of the agricultural economy as time progresses. Therefore, we must always adhere to the agriculture opening-up. The regional business environment and the agriculture opening-up can jointly promote the high-quality development of the agricultural economy. The research results can provide a basis and reference for the central and local governments to formulate relevant agricultural development policies and provide a reference for relevant agricultural economic entities and enterprises. [ABSTRACT FROM AUTHOR]

Agricultural eco-efficiency can effectively reflect the coordinated green and balanced development of rural resources, and digital financial inclusion provides a strong financial power in the process of long-term balanced development of rural areas. There may be a complex relationship between the two. Therefore, based on the panel data of 30 provinces, autonomous regions, and municipalities in mainland China from 2011 to 2018, this paper explores the impact of digital financial inclusion on agricultural eco-efficiency through the differential GMM method. Further, the paper analyzes how digital financial inclusion influences agricultural eco-efficiency by influencing the agricultural scientific and technological investment. The following conclusions are drawn. First, there is a positive U-shaped nonlinear relationship between digital financial inclusion and agricultural eco-efficiency. Second, the impact of digital financial inclusion on agricultural eco-efficiency is of regional heterogeneity. Digital financial inclusion has a significant positive U-shaped impact on agricultural eco-efficiency in central China but has no significant impact on Eastern and Western China. Third, agricultural R&D investment will intensify the promotion effect of digital financial inclusion on agricultural eco-efficiency. [ABSTRACT FROM AUTHOR]