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Ice core provides a valuable vertical timeline of past climates and anthropogenic activities. Environmental proxies have been widely used in these studies, but there are few biological indicators available. To address this gap, we investigated the bacterial community from a 74 m ice core of Muztag ata glacier on the Tibetan Plateau to link biological indicators with past climate and anthropogenic activities. By analyzing the portion of the ice core with environmental proxies available (corresponding to 1907 to 1991), we observed an increase in bacterial richness throughout the ice core, which was associated with higher NH4+, an indicator of agricultural development. The bacterial community was jointly determined by human activity, natural input, and air temperature, with a strong human influence after the 1950s. Furthermore, the relative abundance of animal gut-associated bacteria, including Aerococcaceae, Nocardiaceae, Muribaculaceae, and Lachnospiraceae, was associated with livestock number changes in the Central Asian region. Together with other bacterial lineages, they jointly explained 59.8% of the livestock number changes. This study provides quantitative evidence of the associations between bacterial indicators and past climate and human activities, highlighting the potential of using bacterial proxies for ice core studies. [ABSTRACT FROM AUTHOR]

Emerging climate-development processes jeopardize water supply, especially in the Global South. In the Vietnamese Mekong Delta, disrupted water flows driven by climate change and hydropower development have caused water scarcity, threatening agricultural systems in both upstream and coastal areas. Based on insights from desk reviews, stakeholder workshops and interviews with local informants, this paper argues that while grey (engineered) solutions take precedence in addressing water scarcity, securing water sustainability (water retention) for agricultural resilience demands integrated 'grey–green' (engineered-nature-based) solutions. This paper suggests demands for translating this approach into the water governance framework for the delta's agricultural development. [ABSTRACT FROM AUTHOR]

In light of the environmental pollution resulting from the excessive use of chemical fertilizers, several methods, including the analysis of morphological and cultural characteristics as well as ITS rDNA sequencing, were utilized to identify the strain and evaluate its phosphate solubilizing capacity. A controlled greenhouse pot experiment was then conducted to assess the effects of the phosphate solubilizing bacteria strain Y-5 on crop growth and soil fertility. The identification results revealed that the phosphate solubilizing strain, Y-5, belongs to Pseudomonas donghuensis. Under liquid culture for 5 days, the ratio of the phosphate solubilizing ring's diameter to the colony's diameter (D/d) was 3.51. Notably, strain Y-5 exhibited exceptional phosphate solubilization efficiency, with a maximum phosphate solubilizing amount of calcium phosphate reaching 432.25 mg/L. Furthermore, the highest phosphate soluble content of lecithin was 116.37 mg/L, highlighting its robust phosphate solubilizing capacity. The pot experiment results revealed that the modified biochar of strain Y-5 effectively enhances the content of available phosphate and organic matter in soil. Additionally, the modified biochar of the Y-5 strain exerted a significant growth-promoting effect on corn seedlings. Based on the indices of maize seedling growth and soil property changes, it was determined that the optimal application ratio of Pseudomonas donghuensis modified biochar was approximately 10 g/kg. This research result significantly reduced the cost of popularizing and applying phosphate solubilizing bacteria modified biochar and served as a new agricultural material with good prospects for popularization and application, potentially contributing to the green and sustainable development of agriculture in the future. [ABSTRACT FROM AUTHOR]

Agricultural and industrial production activities generate GDP and greatly improve the quality of life of residents. At the same time, these activities also lead to increased energy consumption and emit large amounts of pollutants, which affect public health. However, there has been little past research focused on all the above associations. This study innovatively proposes a parallel two-stage EBM-DEA model to re-measure health production efficiency. The objective is to conduct a joint analysis of the economic development, pollutant emissions and human health of agriculture and industry in 30 provinces in China from 2016 to 2020, and calculate the health production efficiency, stage efficiency (agricultural production efficiency, industrial production efficiency and health efficiency) and environmental pollution efficiency. The research shows that the health production efficiency in other provinces has not reached the efficiency frontier except in Beijing, Fujian, Zhejiang, and Ningxia. In addition, there is room for improvement to varying degrees. In terms of geographical disparity, agricultural production efficiency and health efficiency are distributed in the trend of east > west > central, whereas industrial production efficiency is distributed in a ladder shape from east > central > west. Health efficiency contributes to overall efficiency, whereas agricultural and industrial production efficiencies drag down overall efficiency. The discharge efficiency of various pollutants shows that China's environmental control policies have achieved good results, but environmental problems in some provinces are still serious. Moreover, areas with higher pollutant discharge efficiency values have higher tuberculosis incidence efficiency values. Furthermore, provinces should pay attention to improving the efficiency of medical and health fund utilization. [ABSTRACT FROM AUTHOR]

Reducing and sequestering agricultural carbon is a crucial measure for achieving carbon neutrality and carbon peak, as well as an integral component of agricultural modernization. This study uses the super-efficiency Slack-based measure-Data envelopement analysis (SBM-DEA) and Exploratory spatial data analysis (ESDA) models to examine the spatiotemporal differentiation characteristics of agricultural ecological efficiency (AEE) in 38 districts and counties of Chongqing. The results indicate that the CRR-DEA model disregards the impact of undesirable output factors and overestimates the actual utilization of agricultural resources, whereas the super-efficiency SBM-DEA model is more consistent with the actual agricultural production process. Chongqing's AEE was at a high level of efficiency and the distribution of spatial pattern is uneven, displaying a development pattern of "The urban agglomeration in the Three Gorges Reservoir area of Northeast Chongqing (UTC)>Main urban metropolitan area (MUA) > Wuling Mountain urban agglomeration in southeast Chongqing (WUC)", and there is a significant agglomeration characteristic among various districts and counties. The High-High (H-H) agglomeration is primarily concentrated in the central urban area and the Low-Low (L-L) agglomeration area is primarily located in the eastern portion of UTC. Consequently, all regions in Chongqing should combine their own agricultural development characteristics, maximize their strengths, compensate for their weaknesses, and thereby enhance the AEE. [ABSTRACT FROM AUTHOR]

The adoption of green control technologies in agriculture is an important approach to ensure the quality and safety of agricultural products, address agricultural pollution from its source, and promote increased productivity and income in agriculture, thereby driving high-quality agricultural development. This paper is based on production and operation data from 2,155 tea farmers in 16 major tea-producing provinces in China. Using an endogenous transformation model from the perspective of the value chain, it empirically analyzes the impact of adopting green control technologies on tea farmers' income and draws three conclusions. First, the adoption of green control technologies significantly promotes income growth for tea farmers. Second, the awareness of tea farmers regarding pesticide residue limits and the exchange of pest control techniques among tea farmers significantly enhance the adoption of green control technologies. Third, the income effects of adopting green control technologies differ across different stages of the value chain, with the income growth in the cultivation stage being lower than that in the processing and marketing stages. Finally, based on the analysis and relevant research by scholars, four targeted suggestions are proposed: (1) Increase efforts to promote the adoption of green control technologies and enhance farmers' willingness to adopt them. (2) Promote education and training on green control technologies for tea farmers. (3) Ensure fair distribution of benefits along the agricultural value chain. (4) Strengthen the promotion of green control technologies at the market and consumer end. [ABSTRACT FROM AUTHOR]

BACKGROUND: A variety of environmental factors can disrupt biotic interactions between plants, insects and soil microorganisms with consequences for agricultural management and production. Many of these belowground interactions are mediated by volatile organic compounds (VOCs) which can be used for communication under appropriate environmental conditions. Behavioral responses to these compounds may likewise be dependent on varying soil conditions which are influenced by a changing climate. To determine how changing environmental conditions may affect VOC‐mediated biotic interactions, we used a belowground system where entomopathogenic nematodes (EPNs) – tiny roundworm parasitoids of soil‐borne insects – respond to VOCs by moving through the soil pore matrix. Specifically, we used two genera of EPNs – Heterorhabditis and Steinernema – that are known to respond to four specific terpenes – α‐pinene, linalool, d‐limonene and pregeijerene – released by the roots of plants in the presence of herbivores. We assessed the response of these nematodes to these terpenes under three moisture regimes to determine whether drier conditions or inundated conditions may influence the response behavior of these nematodes. RESULTS: Our results illustrate that the recovery rate of EPNs is positively associated with soil moisture concentration. As soil moisture concentration increases from 6% to 18%, substantially more nematodes are recovered from bioassays. In addition, we find that soil moisture influences EPN preference for VOCs, as illustrated in the variable response rates. Certain compounds shifted from acting as a repellent to acting as an attractant and vice versa depending on the soil moisture concentration. CONCLUSION: On a broad scale, we demonstrate that soil moisture has a significant effect on EPN host‐seeking behavior. EPN efficacy as biological control agents could be affected by climate change projections that predict varying soil moisture concentrations. We recommend that maintaining nematodes as biological control agents is essential for sustainable agriculture development, as they significantly contribute not only to soil health but also to efficient pest management. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Lynchets are ridges formed by erosion and sediment accumulation downstream of agricultural plots and offer valuable insights into past agricultural activity. These microtopographies cover vast areas and serve as indicators of historical changes in land use. As a result, their ubiquity across Europe makes them particularly interesting. In this study, we propose a geoarchaeological approach to analyze six lynchets, four in the Vosges Mountains and two on the Lorraine Plateau (France). The lynchets can be considered soil archives with no stratigraphic organization or chronological sequence from bottom to top, making it difficult to determine the age of the lynchets and identify changes in land use over time. To this end, we propose the analysis of historical and geo‐historical archives combined with the "pedosedimentary" archives of lynchets through charcoal identification and dating combined with near‐infrared spectroscopy to determine the age, vegetation, and past land use changes associated with lynchet landscapes. By combining these multiple data sources, we are better able to show the chronological development of these ancient agricultural systems and uncover valuable information on landscape history. Charcoal dating suggests a higher frequency of fires from the Middle Ages. The dating aligns with the regional dynamics of anthropogenic fires, indicating a potential use of fire for cultural purposes. We also demonstrate the difficulty of extrapolating the dating of a lynchet to the entire lynchet system. Our results highlight the difficulties of interpreting the formation and dating of lynchets and the lynchet system on the sole basis of charcoal analysis. However, we highlight the value of applying pedoanthracology to lynchets to determine the dynamics of land use change in former fields. [ABSTRACT FROM AUTHOR]

Agricultural nonpoint source pollution (ANPS) can cause systemic pollution of the ecological environment, directly threatening sustainable agricultural development and human health and safety. Accurate estimation of ANPS loads, clarification of the sources and spatial distribution patterns of ANPS, and effective control and management of ANPS in the watershed require identifying critical source areas. This case study focuses on the Danjiang River basin, a critical water source for China's South-to-North Water Division and demonstrates the efficiency of ArcGIS's spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS and extracting the causes associated with the spatial pattern, as well as the accuracy of identifying critical source areas of ANPS. In this study, two spatial statistical analysis methods, Anselin local Moran's I and hot-spot analysis (Getis–Ord Gi*), are applied to propose a method for accurately identifying critical source areas of ANPS based on the spatial distribution of ANPS loads. The results of the study are as follows: (1) based on the MIKE LOAD model, the annual ANPS loads of chemical oxygen demand (COD), ammonia nitrogen (NH4), total nitrogen (TN), and total phosphorus (TP) in the Danhan River basin are calculated as 182,530.15, 16,137.39, 58,285.92, and 2,962.84 t/yr , respectively. (2) Clusters are mainly distributed in the southwestern part of the watershed, and the spatial pattern is directly related to land use and rural population; the spatial pattern of outliers is related to agricultural modes and geographical characteristics. (3) Hot spot clusters are concentrated in the hinterland of Hanzhong Plain; the regional specialty of agriculture is the dominant factor in determining the spatial pattern of cold-spot and hot-spot clusters. (4) Based on these findings, seven critical subbasins and one critical source area of ANPS that need to be prioritized for control in the study area are identified. Agricultural nonpoint source pollution (ANPS) is a significant cause of ecosystem pollution, which directly threatens the sustainable development of agriculture, human health, and safety. The Dan–Han River Basin is a crucial water source in China for the South-to-North Water Diversion Project and provides water to 140 million people. It is essential to ensure that the water quality of the Dan-Han River remains unpolluted. The ANPS as a major source of water pollution in the watershed is difficult to identify and control. Accurately identifying the sources and spatial distribution patterns of ANPS pollutants is a prerequisite for maintaining water quality and controlling ANPS. Our study offers a new perspective on methods to reveal the hidden spatial pattern and identify critical source areas of ANPS in watersheds. This research demonstrates the effectiveness of ArcGIS's spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS, extracting the causes associated with the spatial pattern, and accurately identifying critical source areas of ANPS. These methods can also be applied to study nonpoint source pollution in other watersheds. [ABSTRACT FROM AUTHOR]

Agricultural endeavors, especially in developing nations, entail inherent risks. Amidst challenges related to capital and agricultural marketing, contract farming emerges as a highly effective strategy. It not only facilitates capital accumulation but also ensures consistent product sales, establishes fair pricing, and contributes to the overall balanced development of the agricultural sector. This concern has been a longstanding global consideration, with Iran now addressing it. Recognizing the paramount importance of implementing contemporary agricultural methodologies, including contract farming, this research systematically investigates factors influencing farmers' intentions in Iran. A survey methodology is employed for systematic information collection from a statistical population of 98,777 farmers in rural Markazi Province, Iran. Using the Karjesi and Morgan table for sample size determination, a representative subset of 383 farmers is selected through stratified random sampling, ensuring proportional assignment within strata. A researcher-made questionnaire, validated by expert panels and confirmed for reliability through Cronbach's alpha coefficient, serves as the research instrument. Data analysis was conducted using SPSS 27, and structural equation modeling was performed with SmartPLS4. The findings reveal that trust (0.528), awareness (0.332), and attitude (0.168), exert the most substantial causal influence on farmers' intention to embrace contract farming. Consequently, the research findings offer practical recommendations for the adoption of contract farming, providing valuable insights to policymakers and stakeholders for implementing targeted interventions aimed at boosting farmers' willingness to participate in contractual agreements. [ABSTRACT FROM AUTHOR]

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies. [ABSTRACT FROM AUTHOR]

Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration into the IoT. Herein, a ternary heterostructure was prepared by decorating WO3 nanoplates with Au and SnO2 nanoparticles through a facial photochemical deposition method. This was employed as a sensing material for 3-hydroxy-2-butanone (3H-2B), a biomarker of Listeria monocytogenes. These Au/SnO2–WO3 nanoplate-based sensors exhibited an excellent response (Ra/Rg = 662) to 25 ppm 3H-2B, which was 24 times higher than that of pure WO3 nanoplates at 140 °C. Moreover, the 3H-2B sensor showed an ultrafast response and recovery speed to 25 ppm 3H-2B as well as high selectivity. These excellent sensing performances could be attributed to the rich Au/SnO2–WO3 active interfaces and the excellent transport of carriers in nanoplates. Furthermore, a wireless portable gas sensor equipped with the Au/SnO2–WO3 nanoplates was assembled, which was tested using 3H-2B with known concentrations to study the possibilities of real-time gas monitoring in food quality and safety. [ABSTRACT FROM AUTHOR]

In the present study, headspace gas chromatography coupled with mass spectrometry (HS–GC/MS) was applied to profile volatile components of Bunium persicum samples (rhizomes, roots, stems, leaves, flowers and unripe & ripe seeds), which were collected from two locations of Indian alpine Himalaya. Thirty-six different volatile metabolites were characterized in all samples, representing (77.9-96.9%) of the total oils. Extracted volatile oils (VOs) were dominated by monoterpenes (37.9-94.8%), oxygenated monoterpenes (0.1-28.8%) and sesquiterpenes (0.2-24.3%) hydrocarbons. γ-Terpinene (6.0-64.1%), α-terpinolene (1.1-43.7%), p-cymene (0.4-8.9%), o-cymene (1.4-14.8%), sabinene (0.1-12.3%), β-pinene (0.7-14.2%), β-myrcene (1.2-11.0%), limonene (1.3-9.3%), sylvestrene (3.1-7.7%), and cymen-7-al (6.9-10.9%) were key volatile components identified in different parts of B. persicum using HS–GC/MS. These findings revealed a very high quantitative variation of VOs composition among the samples. The multivariate statistical analysis also classified these variations within the plants and their parts at two locations. HS-GC/MS technique can be utilized for the quality assessment and discrimination analysis of B. persicum, especially its essential oil (BEO). Further, essential oils of both the seed samples showed significant antioxidant activity in DPPH and ABTS assay. The current methodology will help to monitor the varietal development and smart agriculture practices as quality perspectives. [ABSTRACT FROM AUTHOR]

Determining a suitable "dry sowing and wet emergence" water control program for cotton fields in the arid regions of Northwest China is of great significance in saving water resources, improving economic efficiency, and promoting sustainable development of agriculture. The objective of this study was to analyze the effects of different "dry sowing and wet emergence" water control treatments on dry matter accumulation, chlorophyll fluorescence, yield quality, and water productivity of cotton, and to determine the optimal "dry sowing and wet emergence" water control program for cotton growth in arid areas. A two-year experiment was carried out in 2021 and 2022 in mulched drip-irrigated cotton fields, with a total of 13 treatments of different seedling water quantities (2021: 67.5 mm, 90 mm, 112.5 mm; 2022: 6 mm, 10.5 mm, 15 mm) and different drip frequencies (frequencies means number of drops at seedling stage) (2021: one, two, three times; 2022: two times, four times) in the "dry sowing and wet emergence". Results indicated a positive correlation between increased seedling water quantity and growth indexes. High seedling water quantity treatment demonstrated a 14.33% higher cotton yield than the low seedling water quantity treatment. In comparison with low-frequency treatment, the high-frequency treatment exhibited significantly larger cotton plant height, dry matter accumulation, and yield. Over two years, the average values increased by 8.69%, 16.4%, and 15.91%, respectively, with a 14.55% increase in the coefficient of photochemical quenching of the leaf blade (qP). The high frequency and larger amount of seedling water quantity treatments showed significantly higher irrigation water productivity, with increases of 39.2% and 70.2% compared to the winter irrigation control treatment. In summary, the appropriate "dry sowing wet emergence" water regulation mode (the first drip: 15 mm, the second drip: 4.5 mm, the third drip: 22.5 mm, the fourth drip: 15 mm) can ensure crop yield quality under the premise of significantly reducing the agricultural irrigation water, which can provide certain theoretical support for the green, efficient, and sustainable development of the local cotton industry. [ABSTRACT FROM AUTHOR]

Duckweed growing in paddy fields (DGP) has substantially increased because of the effects of climate warming and/or eutrophication in irrigated water. Previous studies have primarily focused on investigating the effects of DGP as a nonchemical agent for enhancing rice productivity on nitrogen utilization in rice paddy fields. However, how DGP impacts rice yield remains poorly understood. Therefore, a field experiment with three representative rice cultivars was conducted to determine the effects of DGP on rice yield, considering ecological factors, photosynthetic capacity, spectral changes, and plant growth. The results showed that DGP significantly reduced the pH value by 0.6 and the daily water temperature by 0.6 °C, accelerated rice heading by 1.6 days and increased the soil and plant analyzer development (SPAD) and photosynthetic rate of leaves by 10.8% and 14.4% on average, respectively. DGP also markedly enhanced the values of various vegetation indices such as RARSc, MTCI, GCI, NDVI705, CI, CIrededge, mND705, SR705, and GM, and the first derivative curve of the rice canopy reflectance spectrum exhibited a 'red shift' phenomenon upon DGP treatment. Changes in the aforementioned factors may lead to average increases of 4.7% in plant height, 15.0% in dry matter weight, 10.6% in panicles m−2, 2.3% in 1000-grain weight, and ultimately a 10.2% increase in grain yield. The correlation observed suggested that the DGP-induced enhancement in grain yield can be achieved by reducing the pH and temperature of the paddy water, thus enhancing the SPAD value and photosynthesis of leaves and stimulating rice plant growth. These results could offer valuable theoretical support for the future sustainable development of agriculture and the environment through the biological synergy between rice and duckweed. [ABSTRACT FROM AUTHOR]

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm's search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. [ABSTRACT FROM AUTHOR]

Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering–Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture. [ABSTRACT FROM AUTHOR]

Purpose: This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018. Design/methodology/approach: Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used. Findings: Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity. Practical implications: The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent. Originality/value: This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity. [ABSTRACT FROM AUTHOR]

Private land protection is an important and growing tool to address biodiversity loss and climate change. Thus, better empirical evidence on the effectiveness of private land protection and organizational practices, such as targeting of lands for protection and choice of protection mechanism (i.e., fee simple land acquisition and conservation easements), is needed. We addressed this gap by estimating the impacts of The Nature Conservancy's (TNC) (a large nongovernmental organization with relatively decentralized management) conservation land acquisitions and easements from 1988 to 2016 in three regions of the United States (Mid‐Atlantic, New England and New York, and California). We estimated impact in terms of avoided conversion by comparing natural land cover on 3179 protected parcels with matched unprotected parcels. Nineteen of 21 ecoregional plans used threats of agriculture and development to identify priorities for protection. When regions and protection mechanisms were pooled, on average there was no evidence of avoided conversion from 1988 to 2016. Accounting for mechanisms, TNC land acquisitions avoided conversion and easements did not. TNC's easements on parcels acquired by conservation partners did avoid conversion. Limitations of these results include focus on a single measure of impact, inability to capture future avoided conversion, and low land cover change accuracy in California. Our results suggest that private land protection managers who seek to avoid land conversion in the near to medium term should increase focus on areas with higher threats. Special attention should be paid to strengthening accountability and the role of partners, improving or clarifying how easements are used, and facilitating the flow of resources to work with the greatest potential impact. [ABSTRACT FROM AUTHOR]

The adverse impacts of climate change and the disparity between water availability and demand in agriculture necessitate the development of water-efficient irrigation schedules. The empirically derived crop water stress index (CWSI) is a popular tool for the detection of water stress in crops and the scheduling of water-efficient irrigation regimes. But the sensitivity of the empirical CWSI to the input parameters, i.e., air temperature (Ta), canopy temperature (Tc), and relative humidity (RH), is rarely studied. This study is conducted on wheat crops in the Uttar Pradesh province of India where four irrigation scheduling strategies/treatments are experimented with to study CWSI and its sensitivity to Tc and meteorological parameters (Ta and RH). Two irrigation scheduling strategies correspond to drip irrigation and the remaining two correspond to flood irrigation. The mean CWSI values, derived by empirical approach, are 0.11 and 0.03 for drip irrigated treatments and 0.29 and 0.31 for flood irrigated treatments. To observe the sensitivity of the empirical CWSI to the input parameters, initially, the accuracies of the research-grade equipment, used for taking Tc, Ta, and RH observations, are used as error values in the input parameters. This resulted in 3.5 to 7% errors in the empirically derived CWSI values. A maximum error of 2.7% was observed in CWSI values when an error of 10% was introduced in the RH data. The maximum error in CWSI (52%) was observed for the case when a combined error in Ta, Tc, and RH by −1 °C, 1 °C, and 10%, respectively, were introduced. A random error in the range −1 to 1 °C in Tc resulted in a maximum CWSI error of 21%. Out of the three input parameters, CWSI was found to be least sensitive to RH and with sensitivity to both Ta and Tc showing similar trends. A common pattern of decreasing error in CWSI values for increasing vapor pressure deficit (VPD) was observed for all the studied cases. The results show that the water stress, as reflected by the CWSI values, was significantly lower for the drip irrigation treatments as compared to the flood irrigation treatments. Also, small errors in the input parameters may combine to result in significant errors in empirically derived CWSI. Hence, data quality is critical for the studies that utilize CWSI for irrigation scheduling, especially in humid climatic conditions. [ABSTRACT FROM AUTHOR]

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies. However, crop yield is influenced by multiple factors within complex growth environments. Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat. Therefore, there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield, making precise yield prediction increasingly important. This study was based on four type of indicators including meteorological, crop growth status, environmental, and drought index, from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield. Using the sparrow search algorithm combined with random forest (SSA-RF) under different input indicators, accuracy of winter wheat yield estimation was calculated. The estimation accuracy of SSA-RF was compared with partial least squares regression (PLSR), extreme gradient boosting (XG-Boost), and random forest (RF) models. Finally, the determined optimal yield estimation method was used to predict winter wheat yield in three typical years. Following are the findings: 1) the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms. The best yield estimation method is achieved by four types indicators' composition with SSA-RF) (R2 = 0.805, RRMSE = 9.9%. 2) Crops growth status and environmental indicators play significant roles in wheat yield estimation, accounting for 46% and 22% of the yield importance among all indicators, respectively. 3) Selecting indicators from October to April of the following year yielded the highest accuracy in winter wheat yield estimation, with an R2 of 0.826 and an RAISE of 9.0%. Yield estimates can be completed two months before the winter wheat harvest in June. 4) The predicted performance will be slightly affected by severe drought. Compared with severe drought year (2011) (R2 = 0.680) and normal year (2017) (R2 = 0.790), the SSA-RF model has higher prediction accuracy for wet year (2018) (R2 = 0.820). This study could provide an innovative approach for remote sensing estimation of winter wheat yield, yield. [ABSTRACT FROM AUTHOR]

With the development of information and data era, the digital technology has seeped into the social. The digital transformation has became the key factors of the modern agriculture in China. Many internet technologies were applied in the agriculture, such as artificial intelligence, big data and internet of things, which promoted the development of agriculture. But with the change of management ways of agriculture, how to adjust the agriculture model and combine with digital technology became the key factors of digital agriculture. The development time of digital agriculture was short in China and had some problems. We analyzed the situation of digital agriculture in Hainan Province and put forward some optimizing suggestions in order to improve the speed of Hainan agriculture, which provided references for the development of digital agriculture in other areas. [ABSTRACT FROM AUTHOR]

China's agricultural economy has been hindered by insufficient accumulation of agricultural capital and credit constraints. It is worth investigating whether China's county financial marketization reform policy can alleviate these constraints and promote high-quality development of the agricultural economy (HQDAE). This paper presents an empirical analysis of the impact of county financial marketization reform on the HQDAE, based on county panel data. The focus is on the mechanism of county urbanization in the above relationship. The results show that county financial marketization has a significant non-linear impact on the HQDAE. Specifically, it has a 'U-shaped' impact on the overall growth of the agricultural economy and an inverted 'N-shaped' impact on the quality improvement of the agricultural economy. Secondly, the relationship between county financial marketization and the HQDAE is influenced by a threshold effect based on the level of county urbanization. As the level of county urbanization increases, the promoting effect of county financial marketization on HQDAE also increases significantly. Additionally, county financial marketization helps to promote county urbanization and accelerate urban-rural integration, which in turn leads to HQDAE. The research in this paper suggests that county-level local governments should promote a differentiated county financial system. In the early stages of financial market-oriented reform, the government should enhance the capacity of financial services in rural areas through tax breaks, policy incentives, and other measures to prevent financial leakage from agriculture. In the later stages of financial marketization reform, the government should strengthen financial supervision to prevent financial resources from being diverted from industry to capital. Moreover, to achieve the HQDAE, it is necessary to promote county financial market-oriented reform and accelerate the construction of county urbanization. This will help break the dual economic structure of urban and rural areas and promote the flow of financial capital, technology, and human capital from county cities to rural areas. [ABSTRACT FROM AUTHOR]

The digital economy, as a new economic form with high information density, provides a new driving force for the realization of high-quality agricultural development. Panel data of 31 provinces in China from 2011 to 2020 were selected for analysis. The static panel data interaction effect model and panel threshold model were used to verify the nonlinear influence mechanism and heterogeneity of financial development in the process of the digital economy affecting high-quality agricultural development. The findings are as follows. (1) During the study period, the high-quality development of China's agriculture showed a steady upward trend; however, the regional differences were significant, and the eastern part was larger than the central and western part. (2) The digital economy can promote high-quality agricultural development. (3) The digital economy has a double threshold effect in the process of affecting high-quality agricultural development, which depends on the level of financial development. When the threshold is exceeded, the digital economy has a more significant promoting effect on high-quality agricultural development. (4) The impact of the digital economy on high-quality agricultural development is heterogeneous. From the perspective of different regions, the impact effect is greatest in the eastern region, while the effect is smaller in the central and western regions. From different resource endowments, the positive impact effect is greatest in the major grain-selling areas, followed by the major grain producing areas, but the positive digital economy driving effect is not significant in the balance of production and sales areas. Finally, three policy suggestions are proposed. First, the Chinese government should increase investments in and support for digital technology to promote the integration of the digital economy and agriculture. Second, the Chinese government should promote the development of digital inclusive finance in areas with financial development below the threshold. Third, different regions should implement differentiated digital economies to promote high-quality agricultural development. [ABSTRACT FROM AUTHOR]

Drawing upon literature from both impact assessment of development projects and agricultural research, the aim of this article is to analyse the pilot testing of a new multi-dimensional assessment framework for defining and evaluating the societal impact of agricultural research and corresponding education. The research approach involves an action research effort of pilot testing in three case studies from three different countries. The framework assumes a systems view, understands impact as socially embedded, and adopts the concepts of contribution and productive interactions rather than cause-and-effect attribution. The emerging experiences include developing both project-specific and universal indicators; gauging impacts at different levels and sustainability dimensions; handling the issues of attribution and time frame; and the role of stakeholder involvement. The results have the potential to support the development of a new role for impact assessment, by enabling principal actors in research and higher education institutions to take responsibility for contributing to concrete and demonstrable sustainability changes in society. [ABSTRACT FROM AUTHOR]

In order to monitor the temperature of agricultural planting in real time and avoid losses in the yield and quality of crops due to excessive temperature fluctuation, a multinode temperature monitoring method for digital agricultural planting based on a wireless sensor network (WSN) was proposed herein. First, the WSN structure for the digital agricultural temperature monitoring system was designed, and then the overall structure of the digital agricultural planting multinode temperature monitoring system was designed by combining it with the upper computer to collect agricultural planting temperature data. Next, the characteristics of the agricultural planting temperature data were extracted, and the data were transmitted to the upper computer by establishing a temperature transmission route. Then, the efficiency of the temperature anomaly monitoring was optimized based on multiple input multiple output (MIMO) mode. Finally, the real-time collected temperature value was input into a Bayesian classifier to monitor the temperature anomaly, determine the type of temperature anomaly, and send a warning to the administrator, who, based on the type of warning, takes the appropriate measures to adjust the multinode temperatures at the agricultural planting points. The experiments showed that this method had strong data transmission ability, and the survival time of the data transmission nodes was longer, and the energy consumption of the data transmission was less, and the accuracy of the abnormal temperature monitoring was above 96%. The results showed that this method can accurately monitor abnormal temperatures and provide a warning in real time, accurately control the temperatures of the agricultural planting points, and promote the digital development of agriculture. [ABSTRACT FROM AUTHOR]

Although China has achieved great advancements toward national food security, the country is still confronted with a range of challenges, including natural resource stress, imbalanced diets and environmental pollution. Optimized management of crop-livestock systems is the key measure to realize agricultural green transformation. However, optimized management of crop-livestock systems that use multi-objective zoning is lacking. This study employed a multi-objective zoning management approach to comprehensively analyze four indicators: ammonia volatilization, nitrogen surplus, soil carrying capacity and ecological red line area. With its significant ecological integrity and a strong emphasis on sustainability, the Baiyangdian Basin serves as a unique and suitable test case for conducting analyses on multi-objective nutrient optimization management, with the aim to facilitate the agricultural green transformation. This study finds that less than 8% of the area in the Baiyangdian Basin meet the acceptable environmental indicator standard, whereas around 50% of the area that had both nitrogen surplus and ammonia volatilization exceeded the threshold. Implementation of unified management, that is, the same management technique across the study areas, could result in an increase of areas meeting environmental indicator thresholds to 21.1%. This project developed a novel multi-indicator partition optimization method, in which distinct measures are tailored for different areas to satisfy multiple environmental indicators. Implementation of this method, could potentially bring more than 50% area below the threshold, and areas with ammonia emissions and nitrogen surplus could be reduced to 15.8%. The multi-indicators partition optimization method represents a more advanced and efficiency-oriented management approach when compared to unified management. This approach could be regarded as the best available option to help China achieve agricultural transformation to improve efficient production and reduce environmental pollution. It is recommended that current policies aimed at nutrient management toward sustainable agricultural development should shift toward the application of multi-indicators partition optimization. [ABSTRACT FROM AUTHOR]

This review addresses the growing disparity between the current state of fertilizer production in China and the evolving demands of green agriculture in the 21st century. It explores major advances in fertilizers, proposes the concept of green intelligent fertilizers and develops new strategies aligned with the principles of green development in fertilizer industry and agriculture. Green intelligent fertilizers may be designed to maximize the synergistic effects among plants, soils, microorganisms, nutrient sources and the environment. This concept emphasizes the integration of industry and agriculture toward green development for entire industry chain, using an interdisciplinary approach to drive the green transformation of fertilizer industry, and promote green and sustainable development of agriculture. By bridging the gap between the current state of fertilizer industry and a growing need for environmentally responsible agricultural practices, this review highlights a path toward harmonizing fertilizer production with the imperatives of green agriculture. [ABSTRACT FROM AUTHOR]

Balancing ecological preservation with sustainable agricultural practices is a global issue. Erhai Lake has felt this challenge keenly. To address it, in 2022, a Science and Technology Backyards (STBs) project was launched in Gusheng Village. The goal of this is to care for the environment while ensuring that farms and farmers can thrive sustainably. The uniqueness of the Erhai STB arises from its interdisciplinary integration, encompassing fields such as ecology, agronomy and social science, resulting in specifically-designed solutions for the Erhai context. While this model aligns with broader STB paradigms, its distinctive edge lies in technological innovation and robust support mechanisms for local agricultural stakeholders. This paper describes the methodology and outcomes of the STB initiative, highlighting its pivotal role in spearheading sustainable transition in Erhai. Preliminary findings underscore the potential of the STB model as an efficacious tool for harmonizing environmental conservation and agricultural practices, that are both financially and environmentally sustainable, rendering it a potential model for comparable regions in China and other counties. [ABSTRACT FROM AUTHOR]

While agricultural green development (AGD) is highly recognized and has become a national strategy in China, it is imperative to bridge the knowledge gaps between AGD and the UN Sustainable Development Goals (SDGs), and to evaluate the contribution of AGD to meeting the SDGs. The first aim of this study was to compare the AGD goals and indicators with those of the SDGs so as to identify their relationship. The next aim was to examine the historical evolution of AGD indicators and analyze the gaps between the current status of various indicators and their benchmarks. Limiting factors were identified in China's transition toward AGD. These findings reveal that the indicators of AGD align with those of the SDGs, but have greater specificity to the context in China and are more quantifiable. There has been a significant increase per capita calorie and protein intakes in China, as well as a notable rise in agricultural output per unit of arable land and rural incomes from 1980 to the 2010s. However, these achievements have been accompanied by a high resource use and environmental pollution, highlighting the need for a more sustainable, environmentally responsible agriculture in China. [ABSTRACT FROM AUTHOR]

China has initiated a green transformation plan in 2015, which was soon applied to agriculture, known as the agriculture green development (AGD) initiative, with the goals of achieving food security, high resource use efficiency, and an ecofriendly environment. To assess the agricultural transformation from 1997 to 2020, this paper proposes a national-scale indicator system consisting three dimensions (socioeconomic, food production and eco-environmental) and ten sub-dimensions to quantify the AGD score. This study showed that AGD score in China was at a moderate level during 1997-2010, scoring 40 out of 100. During this stage, decreased scores in the sub-dimensions of resource consumption, environmental quality, and environmental cost have offset the improvement in the socioeconomic dimension, resulting in fluctuated scores around 40. In the second stage (2011-2020), China's AGD score improved but still at moderate level, scoring an average of 46.3, with each dimension increasing by 5.3%-25.0%. These results indicate that China has made progress in the agricultural transformation, transitioning from conceptualization to actions through the implementation of various policies and projects. However, the study emphasizes the need for more effort to address the insufficient and unbalanced development, along with the growing eco-environmental challenges, especially the trade-offs among dimensions. [ABSTRACT FROM AUTHOR]

Agriculture green development (AGD) has become an unavoidable choice to address the unique national circumstances of China. This study established a county-level AGD evaluation index system, comprised three dimensions, food production, ecological environment and socioeconomic development, using 20 indicators. The assessment delved into historical trend and current situation, utilizing Spearman rank correlation analysis to analyze trade-off and synergy relationships, using Quzhou County, Hebei Province as a case study. The main findings were in four areas. Firstly, the index for AGD in Quzhou County increased by 58.9% from 1978 to 2019. The major contribution were the social economy (65.8%) and food production (53.5%), whereas the ecological environment was found to have had a negative impact. Secondly, in 2019, the AGD index was only 56.4, indicating substantial potential for improvement relative to the target value. A notable difference in scores existed between the three dimensions, with the order being ecological environment (66.3) > food production (61.7) > socioeconomic (41.3). Also, 90% of the indicators did not reach the target value. Thirdly, relationship analysis of the indicators revealed that the synergistic effect exceeded the trade-off effect. Specifically, 46.3% of the indicators had no significant relationship, 35.3% had a synergistic relationship, and 18.4% had a trade-off relationship. Finally, interdimensional indicator relationships exhibited a trade-off effect between the ecological environment and both food production and socioeconomic dimensions. However, a positive trend of synergy between production and ecology has emerged since 2015. In conclusion, the quantitative evaluation index system exposed the unbalanced development and significant potential relative to the target value of AGD in Quzhou County, despite notable progress. [ABSTRACT FROM AUTHOR]

Reconciling the tasks of producing adequate amounts of nutritious food for the increasing global population while preserving the environment and natural ecosystems simultaneously is an enormous challenge. The concept of agriculture green development (AGD) and the necessary governmental policies were developed to address the aforementioned challenge in China and to help achieve the related global sustainable development goals. Agriculture green development emphasizes the synergy between green and development; current agriculture has to transform from the intensive farming with high inputs, high environmental impacts and low resource-use efficiency to a more sustainable agriculture, in order to ensure an adequate supply of nutritious food while delivering environmental integrity, improved economic profitability, and social equity. A research program on AGD was established by China Agricultural University with four research themes, namely: green crop production, green integrated crop-animal production, green food and industry, and green ecological environment and ecosystem services, to provide a scientific basis for future developments and to facilitate the implementation of AGD in practice. AGD requires a multistakeholder approach, fueled by innovative and interdisciplinary research. Joint actions have to be taken by governments, farmers, supply industries, consumers, educators, extension services and researchers to support AGD. This requires strong coordination and public awareness campaigns. This review presents the progress that has been made over the past 5 years and makes recommendations for more research and development, in order to better deliver agricultural green and sustainable development on national and international scales. [ABSTRACT FROM AUTHOR]

Extension, and extension workers in particular, drive the agricultural modernization process and the rest of those in associated professions and positions are 'support staff.' Whether you are a lecturer, a professor, a dean, a researcher, an extension director or a head of a non-government organization, if your mission is to develop smallholder agriculture at farmer level, you are supporting the field extension worker to achieve your/farmers' goals. To this extent, it is no surprise that, when no perceptible improvement takes place at the farmer level, the blame lies squarely on the shoulders of extension. Despite its importance, agricultural extension is the most misunderstood of all agricultural disciplines, and the lack of understanding reveals itself in several ways. This paper discusses some of the misconceptions and debatable issues that affect the way extension is supported, the way it is structured, the way it is staffed and the way extension workers are trained and capacitated. The misconceptions also affect the kinds and levels of expectations people have of extension. The misconceptions include: unending definitions of extension, expanding extension concepts, blaming extension for perceived failures in agriculture, undermining the extension discipline, inadequacies in extension teaching and training, markets and the role of extension and structural changes in extension. The origins of some of these debatable issues can be traced to development partners who come with their conceptions of what is needed while others come from development theorists who have not done any extension work in the field. Unfortunately, there are no strong extension professional associations in many African countries that could: raise the profile of extension as a discipline, interrogate some of the agricultural development interventions before implementation and help clear some of the misconceptions. In the absence of extension platforms, extensionists operate as individuals, each struggling the best way they know how to make a difference at the farmer level. There is no way of harnessing the experiences the individuals are going through for purposes of learning, sharing and developing common positions. The purpose of this paper is to promote debate on, and scrutiny of these extension issues which are often presented as facts and absolute truths. [ABSTRACT FROM AUTHOR]

Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a + 9.35 × 10 5 km 2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (- 2.19 × 10 5 km 2), bare soil (- 5.14 × 10 5 km 2) and dark surfaces (- 2.27 × 10 5 km 2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (10.57760/sciencedb.13287, Sun and Sun, 2023). [ABSTRACT FROM AUTHOR]

Climate change is among the pressing global challenges affecting sustainable development, especially in the rain-fed-based agriculture of developing countries. Climate-smart agriculture (CSA) approach answers this question by ensuring high productivity, creating an improved adaptive capacity and reducing GHG emissions. This systematic literature review aims to provide an overview of the indicators, theory of change and impact pathways of CSA that lead to sustainable development. Using a systematic research method, four stages of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) namely, identification, eligibility, screening and included were used for selecting the articles reviewed. A total of 37 articles were then used for this study. CSA interventions measured by the behavioral changes resulting from different stakeholders (producers, consumers, extension workers, policymakers and institutions, civil society and the private sector) can play a significant role in sustainable development. [ABSTRACT FROM AUTHOR]

The article explores the use of remote sensing techniques to study the Boynton Mounds Complex in Palm Beach County, Florida. The site, which contains earthworks and mounds, has been investigated since the 1970s and is believed to have a long history of habitation. The research aims to analyze the site's design and its relationship with other sites in the region. The article emphasizes the significance of remote sensing in archaeology, as it provides valuable information about ancient earthworks that may have been lost due to development or agriculture. The findings from the analysis of the Boynton Mounds Complex contribute to a better understanding of the site's layout and reveal similarities to other earthwork sites in the area. The article also highlights the importance of using new survey technologies to re-examine previously investigated sites. [Extracted from the article]

The complexity of NP and NPK fertilizers in stationary and long-term system yields is proven to determine substantial changes in soil fertility, revealing interaction possibilities related to the chemistry and requirements of other nutrients (S, Ca, Mg, and microelements), while sustainable fertilization can exert control over them through rational fertilization practices and complex nutritive management. Revealing the extent of the modifying effects in the application of S, Ca, and Mg correlated to the soil-plant system conditions relates to the hypothesis of the present research in the context of long-term experiments in Romania at the Office of Pedological and Agrochemical Studies Alba (OSPA Alba) and the Turda Agricultural Research and Development Station (SCDA Turda) with 55 years of a fertilizing effect over the 1967–2022 period, but not solely. This study shows that for sustainable fertilization systems, the complementary application of S, Ca, and Mg with NP and NPK has proven to be effective and unitarily constitutes a measure for the sustainable protection and enhancement of soil fertility. [ABSTRACT FROM AUTHOR]

This study was carried out in 2017-2018 to determine the in vitro and in vivo activities of some bacterial bioagents against Sclerotinia sclerotiorum, which causes root and root-collar rot in sunflower cultivation areas of Konya and Aksaray provinces. Against the two most virulent S. sclerotiorum (Hırkatol and Eskil) isolates selected as a result of pathogenicity tests after being isolated and diagnosed from diseased plants which were collected from sunflower cultivation areas in Konya and Aksaray provinces, the antifungal effects of 16 bacterial isolates from the soil in the rhizosphere region of the healthy sunflower plants from the same areas were evaluated. Primarily, the most effective bacterial bioagents were determined by dual culture tests. As a result of the in vitro tests, a total of 5 bacterial isolates constituting the largest zone diameter were molecularly identified according to 16S rRNA and were used in pot experiments. The bacteria were identified as Bacillus cereus, Bacillus simplex, Brevibacterium frigoritolerans, Bacillus toyonensis (2 isolates) and were coded using the BLAST program of the GenBank database (NCBI). As per in vitro, the highest effect in both isolates of S. sclerotiorum was observed in Bacillus cereus and Bacillus simplex with an inhibition rate of 49.19-57.95%. Except for Bacillus toyonensis (B1), one of the bacterial species which were tested in vivo, all the bacteria reduced or stopped lesion development compared to the control. As a result of the application, the biological control agent completely prevented the growth of both the isolates of Bacillus cereus and Bacillus simplex S. sclerotiorum in in vivo conditions (100%). Efficacy studies have shown that bacterial isolates both cause healthy growth of sunflower plants and significantly prevent disease formation in treated plants when compared to control plants. These results emphasize the importance of such studies as a tool for the development of sustainable agricultural practices that can be easily applied in our region, and also show that B. cereus and B. simplex in sunflowers can be potential bacterial bioagents that can be used in biological control against S.sclerotiorum. In addition, it will be useful to carry out studies on the development of commercial preparations of the bacterial isolates found in the study. [ABSTRACT FROM AUTHOR]

Nitrogen and phosphorus play essential roles in ecosystems and organisms. However, with the development of industry and agriculture in recent years, excessive N and P have flowed into water bodies, leading to eutrophication, algal proliferation, and red tides, which are harmful to aquatic organisms. Biochar has a high specific surface area, abundant functional groups, and porous structure, which can effectively adsorb nitrogen and phosphorus in water, thus reducing environmental pollution, achieving the reusability of elements. This article provides an overview of the preparation of biochar, modification methods of biochar, advancements in the adsorption of nitrogen and phosphorus by biochar, factors influencing the adsorption of nitrogen and phosphorus in water by biochar, as well as reusability and adsorption mechanisms. Furthermore, the difficulties encountered and future research directions regarding the adsorption of nitrogen and phosphorus by biochar were proposed, providing references for the future application of biochar in nitrogen and phosphorus adsorption. [ABSTRACT FROM AUTHOR]

Farmers' use of agricultural technology is necessary for reducing poverty. However, women make up most poor smallholder farmers in India and are generally unable to access relevant opportunities. Tandem to supporting women farmers in technology adoption is increasing their empowerment. While women's empowerment has been on policy agendas in India for several years, progress has been slow and results are mixed. Using primary data from West Bengal, this study finds that female farmers with access to agricultural technologies will likely have social advantages over those without, but institutional and social support is necessary to facilitate real empowerment and overall development. [ABSTRACT FROM AUTHOR]

Because the integrated development of rural industry promotes the application of the agricultural green production mode, it inevitably has an impact on the green development of agriculture. Analysing the influence of rural industry integration on agricultural green development is of great significance for realizing agricultural modernization and sustainable development. Based on panel data of 30 provinces in China during 2010-2021, the entropy weight TOPSIS method was used to estimate the agricultural green development level (AGD) and the rural industrial integration level (RII). The influence of agricultural industry integration (AII) on the level of agricultural green development (AGD) and its nonlinear characteristics were analysed by constructing a fixed model, intermediary effect model and threshold effect model. The results show the following. (1) A significant positive relationship exists between the RII and the AGD; that is, the development of rural industry integration can effectively promote the green development of agriculture. Regarding the three regions in China, the integration of rural industries in eastern China plays the most prominent role in the green development of agriculture. (2) Rural industrial integration has significant scale, capital and technology effects; that is, rural industrial integration mainly promotes AGD indirectly by promoting agricultural scale management and agricultural technological progress and improving rural human capital. (3) Rural industrial integration has a significant nonlinear marginal increasing effect on AGD. In the dynamic process of AGD changing from low to high, the effect of the marginal utility of rural industrial integration on AGD is gradually enhanced. Therefore, China should strengthen the deeply integrated development of rural industries, ensure the appropriate scale operation of agriculture and adjustments to the planting structure, accelerate agricultural technology innovation and rural human capital accumulation, and realize a win--win situation for rural economic and ecological conservation. [ABSTRACT FROM AUTHOR]

To obtain a scientific understanding of the spatial and temporal patterns of agricultural eco-efficiency (AEE) as well as its influencing factors in Fujian Province, effective recommendations were provided to promote agricultural green development. The Super-SBM model with undesired output was employed to assess AEE, and the GTWR model was used to analyze the main factors influencing AEE based on data from nine prefecture-level cities in Fujian Province spanning from 2000 to 2020. There are three conclusions to be drawn: Firstly, the AEE of Fujian Province has increased from 0.340 in 2000 to 0.971 in 2020, but it has not yet reached the efficiency frontier due to excessive input of agricultural factors and carbon emissions. The development of AEE across regions is unbalanced with a wide gap between them. Secondly, from a spatial correlation perspective, global correlation of AEE in Fujian Province showed random distribution while local agglomeration existed to varying degrees. Thirdly, AEE in Fujian Province was influenced by various driving factors that changed significantly over different periods and exhibited obvious regional differences. In summary, the AEE of Fujian Province has shown overall improvement; however, its effectiveness remains limited. The disparities among cities are evident, necessitating the formulation of differentiated strategies for agricultural ecological development to enhance regional synergistic driving forces and improve regional AEE. [ABSTRACT FROM AUTHOR]

• Withania somnifera is a revered herb of Indian medicinal system. • The roots and leaves of Ashwagandha contain various phytochemicals like phenols, flavnoids, quinones, steroids etc. • These phytochemicals are distributed among various tissues of this plant in varying concentrations. • Since the location and environmental factors-biotic and abiotic play a key role in the biosynthesis of the bioactive metabolites so the agro-climatic conditions are conductive for introducing and domesticating new exotic plant varieties with enhanced phytochemicals and withanolide contents. • The present study focusses on the assessment of these bioactive compounds in different commercially available varieties for the cultivation of elite germplasm in perspective of hill agriculture and sustainable development. Withania somnifera is a revered herb in the Indian medicinal system due to the presence of pharmacologically bioactive compounds. The roots and leaves of Ashwagandha contain various phytochemicals like phenols, flavonoids, quinones, steroids, etc. These phytochemicals are distributed among various tissues of this plant in varying concentrations. Since location and environmental factors (biotic and abiotic) play a key role in the biosynthesis of bioactive metabolites, the agro-climatic conditions are conducive to introducing and domesticating new exotic plant varieties with enhanced phytochemicals and withanolide contents. The present study focuses on the assessment of these bioactive compounds in different commercially available varieties for the cultivation of elite germplasm from the perspective of hill agriculture and sustainable development. In the study, total phenols were found to be highest in Jawahar-20 root, i.e., 82.714 mg/g (DW), followed by Poshita root, i.e., 64.02 mg/g (DW), while the least was found in Pratap leaf, i.e., 2.33 mg/g (DW). The concentration of total flavonoids varied significantly (p ≤ 0.05) from 1.98 ± 0.0.01 to 80.735 ± 4.33 mg QE/g (DW). Total flavonoids were found to be highest in Jawahar-20 root, i.e., 80.735 mg/g (DW), followed by Poshita leaf, i.e., 60.02 mg/g (DW), and least in Nimithli stem, i.e., 1.98 mg/g (DW). The total tannin content varied significantly (p ≤ 0.05) among the five varieties of Withania somnifera under the present study. The concentration of total tannin varied significantly (p ≤ 0.05) from 0.691 ± 0.51 to 36.09 ± 0.61 mg TAE/g (DW). Tannins were found to be highest in Jawahar-20 root, i.e., 36.09 mg/g (DW), followed by Poshita root, i.e., 29.287 mg/g (DW), while the least was found to be in Pratap seed, i.e., 0.682 mg/g (DW). Ortho-dihydroxy phenol was found to be highest in Jawahar-20 leaf, i.e., 40.081 mg/g (DW), followed by Jawahar-20 root, 19.31 mg/g (DW), and Poshita leaf, i.e., 19.02 mg/g (DW). Both recorded the same, and the least was found in Nimithli leaf, i.e., 0.95 mg/g (DW). Total antioxidant capacity was found to be highest in Jawahar-20 leaf, i.e., 16.70 mg/g (DW), followed by Jawahar-20 leaf, i.e., 11.51 mg/g (DW), and Poshita leaf, i.e., 11.31 mg/g (DW), both of which recorded the same almost least in Pratap leaf, i.e., 0.257 mg/g (DW). The DPPH activity was found to be highest in Poshita root, i.e., 92.39 at 1000 μg concentration, which was very near the ascorbic acid, i.e., 98.9 (a natural antioxidant), followed by Pratap leaf, i.e., 88.04, and least in Nimithli root, i.e., 58.36. The IC50 value of Pratap leaf was the least, i.e., 369.42, followed by Jawahar-20 stem, i.e., 370.32, and the highest IC50 value was found in Nimithli root, i.e., 877.22. The reducing power of five different varieties increased with increasing concentration. The varieties differed w.r.t. their total reducing power activity, i.e., the reducing power activity was highest in Poshita stem with an R2 value of 0.992, followed by Poshita root with an R2 value of 0.982, and least in Pratap seed with an R2 value of 0.565. Thus, Jawahar-20 and Poshita not only recorded higher phytochemicals but also withanolide A and withaferin A. [Display omitted] [ABSTRACT FROM AUTHOR]