Agricultural Economic Benefits Research Articles

Change in the sustainability of regional agricultural systems: based on an emergy decomposition analysis

Abstract In the context of rapid population growth and limited arable land resources, the agricultural system has to provide enough food in a sustainable way. Regional agricultural systems have good consistency in agricultural practices, management decisions, social economy, and climate, which is of great significance in ensuring food security. In this study, emergy analysis and the logarithmic mean divisia index method were integrated to construct an evaluation framework from the dimensions of socio-economic environment, resource environment, climate environment, and ecological environment. Then we evaluated and analyzed the changes in agricultural system sustainability from 1990 to 2019 in the mainstream of Liaohe River Basin, a typical agricultural basin in China. The results showed that the Emergy sustainability index (ESI) decreased from 0.17 to 0.14, and factors Δ Pt/Gt (social and economic development level), Δ G/I (agricultural economic benefits), and Δ Gt/G (economic structure) from the socio-economic environment dimension had the greatest impact on changes in ESI. Moreover, society and economy affected the factors in the resource environmental dimensions through the allocation of policies and resources, which in turn directly affected ESI. The influence of factors from the climatic environment and ecological environment weakens as the ability to manage agricultural systems increases. The research provided a reference for the planning and management of sustaining agricultural systems at a regional scale.

Selection of Suitable Organic Amendments to Balance Agricultural Economic Benefits and Carbon Sequestration.

Long-term excessive use of fertilizers and intensive cultivation not only decreases soil organic carbon (SOC) and productivity, but also increases greenhouse gas emissions, which is detrimental to sustainable agricultural development. The purpose of this paper is to identify organic amendments suitable for winter wheat growth in the North China Plain by studying the effects of organic amendments on the economic benefits, carbon emissions, and carbon sequestration for winter wheat fields and to provide a theoretical basis for the wide application of organic amendments in agricultural fields. The two nitrogen rates were N0 (0 kg ha-1) and N240 (240 kg ha-1), and the four organic amendments were straw, manure, mushroom residue (M R), and biochar. The results showed that, compared to N0, N240 significantly increased the yield by 244.1-318.4% and the organic carbon storage by 16.7-30.5%, respectively, but increased the carbon emissions by 29.3-45.5%. In addition, soil carbon stocks increased with all three types of organic amendments compared to the straw amendment, with the biochar treatment being the largest, increasing carbon storage by 13.3-33.6%. In terms of yield and economic benefits, compared to the straw amendment, the manure and biochar amendments increased winter wheat yields by 0.0-1.5% and 4.0-13.3%, respectively, and M R slightly decreased wheat yield; only the economic benefit of the M R amendment was greater than that of the straw amendment, with an increase in economic benefit of 1.3% and 8.2% in the 2021-2022 and 2022-2023 seasons, respectively. Furthermore, according to the net ecosystem productivity (NEP), N0 was the source of CO2, while N240 was a sink of CO2. The TOPSIS results showed that N240 with a mushroom residue amendment could be recommended for increasing soil carbon stocks and economic benefits for winter wheat in the NCP and similar regions. Low-cost M R can increase farmer motivation and improve soil organic carbon, making a big step forward in the spread of organic materials on farmland.

The recognition and optimization study on the dominant agricultural space in Cangzhou based on the suitability evaluation of agricultural production

Agricultural spatial division and suggestions for the optimization of the partition space were obtained by constructing a recognition system of the dominant agricultural space. The prerequisite was to master natural economic development in agriculture. It was vital to maintain national food security and promote healthy and sustainable agriculture. The suitability evaluation of agricultural production and the dominance evaluation of agricultural development were incorporated to recognize the dominant agricultural space in Cangzhou, Hebei, China in 2020. Besides, priority scenarios were set, e.g., economic development, low-carbon protection, and coordinated development of a low-carbon economy. The NSGA-II genetic algorithm model was used to optimize the quantitative structure of cultivated lands in the agricultural space of Cangzhou in three scenarios in 2030. The research results are as follows: (1) Cangzhou had the largest number of general suitable areas for agricultural production in 2020, accounting for 27.04%; suitable areas were the least, accounting for 10.99%. The proportion of current cultivated lands in unsuitable agricultural production areas still stood at 11.26%; (2) The dominance of agricultural development in 2020 in Cangzhou was mainly at Tier III, accounting for 33.60% with the general dominance of agricultural development; (3) The total area of the dominant agricultural space in Cangzhou was 238208.75 hm2, accounting for 16.72% of the national territorial area of Cangzhou. It included 35 villages and towns beyond ecological red lines, mainly distributed in the western part of Cangzhou; (4) The agricultural space of Cangzhou in 2030, optimized by the multi-objective NSGA-II genetic algorithm model, exhibited decreased cultivated lands across three scenarios. The total amount of cultivated lands was the largest under the priority scenario of economic development, and that was the smallest under the priority scenario of coordinated development of a low-carbon economy. Meanwhile, agricultural economic benefits and carbon emission density were reduced under three scenarios. The benefits and density were moderate under the coordinated development of low carbon and economy. The work provides a reference for further formulating and improving the policies of the agricultural space in various regions.

Managing the water-agriculture-environment-energy nexus: Trade-offs and synergies in an arid area of Northwest China

Water is a stabilizer for production and life in the arid areas. According to research shared by the Pacific Institute and The Guardian, there were 228 incidents of water conflict globally in 2022. Therefore, comprehensive measures are necessary to achieve regional water security. This objective can be achieved by the application of interdisciplinary and professional approaches through establishing a dynamic equilibrium of the different water consumers, and trade-offs and synergies for water allocation between them. A multi-objective optimization model is proposed for managing the water-agriculture-energy-environment (WAEE) nexus in water-scarce areas. Real and integer-encoded multi-objective evolutionary algorithms (EMCMO) were applied to solving management models. Multiple objectives, such as the maximization of economic benefits from agriculture and hydropower generation, the minimization of ecological water shortages for local plants, and the minimization of environmental risks influenced by land use and food supply, were defined in the model. The model was applied to the Yanqi Basin, an important agriculture and energy producer in Xinjiang province, Northwest China. The results indicated that (1) the four-dimensional Pareto solution set has the advantage of presenting the WAEE nexus, highlighting the trade-offs and synergies between high-value crops, surface water, and groundwater, ecological water demand, hydropower generation and total nitrogen and phosphorus loading (TNP); (2) An excessive increase in the agricultural economy will increase TNP loading and agricultural non-point source pollution; (3) Optimization of WAFE for local ecosystem restoration. Optimization in a wet year, reduced agricultural economic benefits by 0.20%, and agricultural TNP loading by 13.19% while increasing ecological water by 58.33%, and hydropower generation by 3.98%; in a normal year, reduced agricultural economic benefits by 6.62%, agricultural TNP loading by 13.19% while increasing ecological water by 46.43%, and hydropower generation by 0.79%; in a dry year, reduced agricultural economic benefits by 17.19%, agricultural TNP loading by 9.72%, and hydropower generation by 16.18% while increasing ecological water by 42.86%. The trade-offs and synergies of the WAEE nexus also apply to other similar water-scarce areas.

Study on the optimization strategy of agricultural industry economic management innovation in the context of rural revitalization in the multi-Internet era

Abstract With the implementation of a rural revitalization strategy, the management of the rural industrial economy urgently needs further innovation and optimization. In this paper, principal component analysis is used to identify the key factors of the problems of agricultural and industrial economic management in the context of rural revitalization, and potential indicator factors are uncovered through principal component analysis, and these potential factors are used instead of all indicators for more accurate analysis. Meanwhile, the PCA method was combined with a support vector machine to construct a PCA-SVM model, based on which the influence factors of agricultural economic efficiency were measured. The higher the degree of informationization of agricultural management, the greater the influence factor on agricultural economic benefits, and the correlation rate reached 26.85%. The higher the degree of the system construction of agricultural management, the greater the influence factor on agricultural economic efficiency, and the correlation rate reached 32.14%. The higher the degree of improvement of infrastructure construction, the higher the influence factor on agricultural economic efficiency, with a correlation rate of 35.68%. This study can precisely analyze the current problems in the economic management of the agricultural industry and provide effective references for promoting the economic development of rural areas in the context of rural revitalization.

An improved bi-level programming model for water resources allocation under multiple uncertainties

Nowadays, with the scarcity of water resources, competition for water resources among different levels and water sectors is growing increasingly fierce. Furthermore, uncertainties are unavoidable in the water resources system. To address the aforementioned issues, a fuzzy max-min decision bi-level multi-objective interval programming model was proposed, which can not only focus on water conflicts at the same level or between different levels, but also pay attention to optimal allocation of water resources under uncertainty. The developed model was then applied to a case study in Wuwei City, Gansu Province, China, which selected fairness of water distribution and agricultural economic benefits as planning objectives. Based on the developed model, different water resources optimal allocation schemes under different representative hydrological years were provided. From the result, as representative hydrological years changed from wet (P = 25%) to dry (P = 75%), agricultural economic benefit and Gini coefficient of agriculture would vary from [35.19, 37.78] × 108 yuan to [31.12, 31.99] × 108 yuan and from [0.468, 0.429] to [0.505, 0.503], which indicates that as available water resources decrease, agricultural economic benefit would decrease and fairness of water distribution would also decrease. And the water distribution fairness of the upper bound water allocation scheme is higher than that of the lower bound water allocation scheme when in the same representative hydrological year. In addition, no matter what representative hydrological year, the results of the established bi-level programming model are always in the middle of the results of the upper and lower level individual objective, which means that the developed bi-level programming model has great advantage to deal with water competing conflict among different levels. Furthermore, based on the results of developed model, the reasonable water resources optimization schemes can be determined by the decision-makers when faced with multi-objective, bi-level and multiple uncertainties problems.

High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.

With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.

Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization

In the context of global climate change, changes in precipitation and temperature conditions, especially the increasing frequency of extreme climate events, have a severe impact on agricultural production. Predicting climate change and adjusting planting structures over time are critical to sustainable agricultural development. In this study, the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Soil and Water Assessment Tool (SWAT) models were used to simulate the future climate and runoff in the middle reaches of the Heihe River Basin (MHR). Then, the MOPSO and NSGA-II were used to plan future planting structures. The results showed that the temperature and precipitation in the MHR showed an increasing trend. By the 2070 s, the temperature was projected to increase by more than 2 °C under SSP245, while that under SSP585 was more than 4 °C. The annual mean precipitation was projected to increase, but the distribution was uneven, and the increased precipitation was mainly concentrated in spring and autumn. Under the interaction of temperature and precipitation, the runoff of the Heihe River will increase in the future, especially under SSP585, and the runoff of the Heihe River is projected to reach 2.48 billion m³/a in the 2070 s. Crop water demand was projected to increase with temperature, and crop irrigation water demand would increase by 14.4 % in the 2070 s under SSP245. Planting vegetables helps to improve agricultural economic benefits and carbon sinks, but to control soil erosion, the area of vegetables is best kept at approximately 16 %. Fertilizers and agricultural films are the primary carbon sources in agricultural production activities in MHR, accounting for more than 60 % of the total carbon. This study suggests promoting sustainable agricultural development by improving irrigation efficiency, using low-carbon fertilizers, controlling soil erosion, and gradually expanding the vegetable planting area with a considered ecological environment.

A socio-hydrological model for assessing water resource allocation and water environmental regulations in the Maipo River basin

Overuse of water resources has led to serious ecological degradation in most river basins. However, there is little processed-based understanding of the dynamic feedback between management decisions from different sectors in river basins and their resultant socio-economic and ecological outcomes. This study developed a socio-hydrological model to track the evolution of water used for socio-economic and ecological uses and to simulate the response of water resource allocation and water environmental regulations to the resulting economic and ecological outcomes. It was applied in the Maipo River basin, Chile, from 1990 to 2019. The results show that this model successfully simulated water resources allocation and water environmental regulations over time. Water environmental regulations showed a strongly linear responsive behaviour to observed accumulated ecological degradation. Results also show that the observed decrease in water resource allocation for economic uses was influenced by increasing agricultural economic benefits and accumulated ecological degradation. It is suggested that environmental regulations should be stronger, synchronized and coordinated with water resources allocation decisions to revert the increasing ecological degradation. This socio-hydrological model, with revision into different contexts, could be used to assess integrated water resources and water environmental management in other river basins.

Optimal cropping pattern based on short-term streamflow forecasts to improve agricultural economic benefits and crop productivity under uncertainty conditions

ABSTRACT Accelerated urbanization has led to diminished land for agricultural activities. Riverine ecosystems play an important role in allocating fertile lands to support agricultural activities. A substantial component of the uncertainty in agricultural productivity comes from seasonal variations linked to inter-annual climate fluctuations. Therefore, understanding the complicated phenomena of streamflow in a riverine environment is important for agricultural and water resources decision making. The present work focuses on forecasting monthly to seasonal streamflow using persistence flow, historical analogues, and artificial neural network approaches. Based on these forecasts, decisions on cropping patterns were made by developing an optimization framework using the constrained linear programming and inexact multiobjective fuzzy linear programming approaches. The proposed fuzzy programming approach was found to be beneficial in producing fair and stable solutions under uncertainty. The findings reveal that integrating forecasting and optimization knowledge could aid in precisely evaluating ecosystem services and meeting rising food demand.

A system dynamics model analysis for policy impacts on green agriculture development: A case of the Sichuan Tibetan Area

Tibetan area of Sichuan Province is the green shelter for national ecological security, so the local agriculture should follow the path of green development rather than the traditional way. To develop green agriculture, proper policies need to be put forward. Therefore, it is necessary to simulate and test the policies proposed and see the long-term outcomes of implementing each policy before making the final decisions. The system dynamics model is a suitable way to simulate the impact of various policies on the development of green agriculture in the Sichuan Tibetan area from a dynamic and systematic perspective. Unlike existing studies that only consider unilateral policy impact, this paper conducts policy simulations from multiple aspects of population, investment and ecological environment. The simulation results showed that population policy and investment policy had a significant effect on improving the agricultural economic benefits. The agricultural output value will increase maximum of 2.8% if the population grows by 1.2% and more rural people are engaged in agricultural activities. The grain output will increase maximumly 9.8% if the sown area of crops increases by 1.2% each year and the investment in agricultural fixed assets increases by 2%. Green policy played a significant role in improving the ecological benefits of agriculture, and the maximum relative ratio of the green index of agricultural development was 12.2%. Finally, some suggestions are put forward, including government investment, protection of the agricultural ecological environment, and promotion of the agricultural market. It is anticipated that this system dynamic model and suggestions can contribute to the green development of agriculture in the Sichuan Tibetan area and other areas.

Integrating optimization and damage estimation to increase economic benefit and ensure food security under seasonal land variability

Rapidly increasing population and rising food demand necessitates expansion of agricultural mainland. However, with accelerated urbanization, agricultural land resources are difficult to expand. The riverine sandbars offer a vast fertile terrain that is conducive to agricultural food production and could support the global food demands despite urbanization. Nevertheless, hydroclimatic factors like streamflow and precipitation have an impact on the availability of agricultural land and potential crop damage in riverine ecosystems. The present study determines the agricultural economic benefits from cultivation in riverine sandbars through optimization framework under stochastic streamflow. A damage estimation model was developed to evaluate the economic losses an optimally planned riverine agricultural area would suffer, if the flow variation exceeds a certain threshold. The findings showed that economic benefit of ∼130 million rupees could be achieved with ∼22 million rupees of additional benefit from the proposed optimization approach. This additional benefit was in reference to the selective cropping approaches, which the farmers did not account. Furthermore, the damage estimation model could comprehend the losses under fluctuating streamflow in subsequent years that was found to vary between 5 and 34 percent. Therefore, this framework of integrating optimization and damage estimation approaches contribute to a better understanding of optimally utilizing the riverine sandbars, thereby improving the socio-economic status of marginalized communities and providing potential additional land resources to sustain food security and production. The study also highlighted the need of crop insurance facilities to assess and manage risks that could provide financial support to farmers, cover crop loss and damage arising from hydroclimatic variabilities.

Driving Mechanisms of Cropland Abandonment from the Perspectives of Household and Topography in the Poyang Lake Region, China

Cropland abandonment is driven by various mechanisms and is best viewed from multiple perspectives to suggest targeted policy changes which may change the status quo of abandonment. Here, we systematically analyze the characteristics of abandonment and its driving mechanisms by different farming households (pure, part-time, and non-farm) in three topographic regions of the Poyang Lake region using a binary logistic regression model. Results show that: (1) The overall abandonment probability in the Poyang Lake region is largest for non-farm households, followed by part-time households and pure households. In the mountainous region, abandonment is largest for non-farm households, followed by pure households and part-time households. Both the hilly and plain regions show the largest abandonment probability for pure households, followed by part-time households, and non-farm households. (2) The low agricultural economic benefits and the uneconomical investments of time in plots are the main abandonment determinants for pure households. Economic efficiency, both the time invested in plots and economic efficiency, are key abandonment determinants for pure households in the mountainous and plain regions, respectively. (3) Labor shortage and plots which are time-consuming and unfavorable to cultivation are the main abandonment determinants for part-time households, with different factors in different topographic regions. (4) For non-farm households, many factors can influence the occurrence of abandonment. Non-farm households in the mountainous and hilly regions are more influenced by non-farm work and the number of farming workers, respectively; in addition, the inconvenience of using agricultural machinery has a significant influence.

Risk regulation of water allocation in irrigation areas under changing water supply and demand conditions

The uncertainty of the hydrological environment and unbalanced water resource allocation result in a high risk of irrigation water shortages in regional agriculture, which seriously affects the sustainable development of agricultural systems. In this paper, we propose a risk regulation based modeling approach for the optimal allocation of agricultural water resources in a complex stochastic environment. The approach includes a conditional value-at-risk (CVaR) model, two-stage stochastic programming (TSP) model, two-dimensional joint distribution probability (JP) model, fractal criteria, and a multiple forms of chance-constrained programming (CCP) model. The model can weigh the contradiction between the intended target and associated penalties attributed to unknown hydrological events, measure the risk between system benefits and expected losses in agricultural water allocation at different confidence levels, and address the randomness in the objective function and constraints (including the left end term, right end term, and left and right end terms). To verify the applicability of the method, it is applied to the Jinxi Irrigation District in China to optimize the allocation and risk regulation of limited water resources under the variable runoff conditions of the Songhua River and crop water demands in the irrigation area. By adjusting parameters such as risk preference and probability of violation, the risk of water shortages in the irrigation area can be regulated, and the multidimensional impacts of different water allocation schemes on agricultural economic benefits, social benefits, ecology and environment can be determined. The case study reveals that the CTSP-CCJP method is sensitive, applicable to complex and uncertain environments and important for the efficient use of agricultural water resources and risk reduction.

Can ecological landscape pattern influence dry-wet dynamics? A national scale assessment in China from 1980 to 2018

Land cover has been demonstrated to have substantial impacts on climate and dry-wet environment, but potential influence of landscape pattern dynamics accompanying land cover change on drought remains unclear. In this study, response of dry-wet dynamics to landscape pattern in China was examined. Results suggest that landscape pattern in China's nine agricultural districts had transformed to varying extents and showed spatiotemporal heterogeneity from 1980 to 2018. For forest landscape, the highest annual average Percentage of Landscape (PLAND) was recorded in SC, reaching 62.26%; and the highest Largest Patch Index (LPI) was presented in YGP, followed by SC, with annual values of 53.79% and 46.26% respectively. The QTP has the most prominent forest connectivity in spite of its lower abundance. For grass landscape, the highest abundance and dominance were recorded in QTP, with annual PLAND fluctuation range of 49.66%—63.52% and annual LPI variation range of 34.10%—58.46%, which is associated with its climate and altitude. The most prominent crop landscape abundance and dominance were recorded in HHHP, with annual PLAND fluctuating interval of 56.53%—60.64%, indicating the highest agricultural development level in this district. At landscape level, dry-wet circumstance could be improved with enhancements in the largest patch percentage, patch density and spatial connectivity, while worsen with increases of landscape fragmentation and separated degree. At class level, increases in abundance and dominance of forest and crop landscapes would reduce drought risk, while it was opposite for grass landscape. Improved forest connectedness would optimize dry-wet environment and reduce drought risk. The PLAND of forest and crop landscapes contributed the most prominent effect to alleviate drought intensity. Compared with forestland and grassland, determining suitable crop landscape configuration to reduce drought risk is more complex because the balance between agricultural economic benefits and ecological landscape effects should be taken into account.

Optimum plastic mulching application to reduce greenhouse gas emissions without compromising on crop yield and farmers' income

With the rapid socio-economic development in China, poverty alleviation and the reduction of the environmental footprint in the plastic film mulching (PM) planting system have become key to sustainable agricultural production. Although many studies have evaluated the maize yield, agricultural economic benefits, and greenhouse gas (GHG) emissions associated with PM through small-scale field experiments, identifying suitable PM regions in combination with their demographic characteristics and the future development of such systems has received little attention. This study combines a Denitrification-Decomposition (DNDC) model and demographic characteristics to determine the optimum PM region in rainfed areas of the Loess Plateau in northwest China. The results demonstrated that PM produced a higher maize yield, agricultural net profit (ANP), and cost-benefit ratio compared to a control treatment (CK) without PM. An agricultural income far above the poverty level would assist in meeting the goals of alleviating poverty and building a prosperous society. In addition, the PM system produced more GHG emissions, but had a lower greenhouse gas intensity (GHGI) than CK under both low (200 kg N ha−1) and high (300 kg N ha−1) nitrogen (N) fertilizer rates. This study developed a framework to evaluate maize yield alongside economic and environmental indicators. We concluded that PM should be adopted in areas with precipitation less than 500 mm, and concentrated in the region with rainfall of 200–400 mm. The results provide a theoretical basis for the sustainable development of the PM maize planting system, and will contribute to the desired goal of environmentally sustainable agricultural production.

Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

Agricultural and rural ecological management system based on big data in complex system

Abstract The rural agricultural ecosystem has an important influence on the development of our country’s economy, society and ecological environment at any time. In recent years, our country has paid more and more attention to rural agriculture, and scientific and reasonable management of the agricultural ecosystem is a problem that everyone is more concerned about. In order to solve the management problem of rural agro-ecosystem, this article uses big data as the research background and constructs an ecological management system index system for rural agro-ecosystem based on complex system theory. The experiment uses fertilizer consumption, water pollution degree, pest degree, carbon and nitrogen absorption and agricultural economic benefits in a rural agricultural ecosystem in a certain area as the system indicators of the ecological management system. Use data mining technology in big data to collect and process relevant data in the network, analyze and understand the agricultural ecosystem through complex systems, and finally calculate and analyze the data of various indicators. The final result showed that the consumption of fertilizer in the rural area with the introduction of the agricultural ecological management system was 70 kg/m 2 less than that in another rural area, and the water pollution and insect pests were two degrees lower than the other rural area. From 2019 to 2020, due to the introduction of the agricultural ecological management system, the carbon absorption of the agricultural ecosystem increased to 166 mt and 241 mt, and the nitrogen absorption increased to 1134 mt and 1430 mt. And under the influence of the agricultural ecological management system in this area, the total agricultural economic income of rural D and rural E that introduced the system was 12.51 million, which was 1.98 million more than the other three rural areas. It can be seen that the agricultural ecological management system has a positive effect on the rural agricultural ecological system.

Input-output Analysis of Agricultural Economic Benefits Based on Big Data and Artificial Intelligence

Agricultural modernization is not only reflected in the agricultural technology, production equipment and development model, the more important embodiment is the development of the concept of modernization. To make scientific, rational and practical analysis and guidance on the whole process of agricultural production economy by means of information is helpful to the vigorous development of agriculture and related industries. The purpose of this paper is to provide better Suggestions and technical means for the rational planning of agricultural industry economy by analyzing the input and output of agricultural industry economic benefits based on big data and artificial intelligence. In this paper, the integrated use of big data and the method of artificial intelligence technology related theory, proposed from the perspective of scientific data organization architecture framework model for the evaluation of agricultural economic data, build data acquisition process model, put forward the multi-source, scattered repetition, flooded agricultural economic data resources for centralized framework for assessing analysis and solution. Finally, try in big data environment, respectively, from the dimension of the influence factors of agricultural economic data lifecycle dimension, service dimension three different dimensions explore implementation method, the results showed that the industrial economy in the process of running efficiency of the indicators were higher than 0.76, for the big data in the application of the agricultural economy provides a good example.

An improved interval-based fuzzy credibility-constrained programming approach for supporting optimal irrigation water management under uncertainty

In response to uncertainties and complexities in the irrigation water management systems, this study presents an improved interval-based fuzzy credibility-constrained programming (IIFCCP) model for optimizing the allocation of irrigation water resources under uncertainty. Mathematical techniques of interval parameter programming (IPP), improved fuzzy credibility-constrained programming (IFCCP) and interval crop water production functions (ICWPFs) are incorporated into a modeling framework for irrigation planning. Thus, this model has the following several advantages in irrigation water management. First, it can not only address interval and fuzzy uncertainties, but also analyze tradeoffs between irrigation water amount and agricultural production returns, to maximize the agricultural economic benefits. Second, it further improves upon existing studies on deterministic CWPFs by using the ICWPFs to reflect uncertain information in the practical applications. Third, it makes a novel attempt to explore weight coefficients (i.e. the proportion of possibility in estimating credibility) to represent various preferences of decision makers. Fourth, it provides more flexible strategies by considering various weight coefficients and credibility levels. Then, the proposed model is applied to a case study of irrigation water management in the middle reaches of the Heihe River Basin, northwest China. Therefore, optimal decision solutions for the allocation of irrigation water under different scenarios, i.e., the combinations of three water levels, four credibility levels and nine weight coefficients, will be obtained and analyzed. A greater system benefits will be achieved at a high weight coefficient and a low credibility level, especially weight coefficients are assigned as 0.8 and 0.9. Additionally, these solutions can provide detailed tradeoffs among system benefits, credibility levels and weight coefficients. Comparisons with the results from deterministic CWPFs and conventional FCCP method indicate that the IIFCCP method has an enhanced practical applicability in actual irrigation water management under uncertainty conditions in arid areas.