Allocation Of Agricultural Resources Research Articles

A Study on Enhancing Crop Yield Prediction with Accuracy through Machine and Deep Learning Models

Accurate crop yield prediction is essential for optimizing agricultural resource allocation and supporting farmers in making informed crop management decisions. This study investigates the effectiveness of various machine learning and deep learning models for predicting crop yields based on key agricultural parameters, including Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. We apply multiple algorithms—Logistic Regression, Multilayer Perceptron, Sequential Minimal Optimization (SMO), J48, Random Forest, REP Tree, and a proposed deep learning model—to evaluate their predictive accuracy in classifying agricultural items. Each model’s performance is assessed using metrics such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and the time taken to build the model. Findings reveal that while traditional machine learning models like Random Forest and REP Tree show robust performance, the proposed deep learning model demonstrates enhanced accuracy and efficiency, making it highly promising for crop yield prediction applications. This study underscores the potential of advanced learning algorithms to improve agricultural productivity by providing actionable insights for crop planning and resource management.

Off-farm employment, farmland transfer and agricultural investment behavior: A study of joint decision-making among North China Plain farmers

Improving farmers’ ability to make joint decisions on multi-factors is crucial for efficient resource allocation in agriculture, promoting sustainable land use, and fostering rural revitalization. Using survey data from 1780 households in the North China Plain, this study used the Conditional Mixed Process (CMP) joint estimation to address potential endogeneity issues and investigate the interaction of off-farm employment, farmland transfer, and agricultural capital investment. First, the results reveal a significant relationship between the three factors in farmers’ decision-making. Particular, there is a positive relationship between off-farm employment and farmland outflow (ρ = 0.163, P < 0.05), while a negative correlation is observed between farmland outflow and capital investment (ρ = − 0.476, P < 0.01). Secondly, there is heterogeneity in the correlation of joint decision-making regarding off-farm employment types and agricultural capital input types. For risk-averse farmers and farmers with low capital liquidity, off-farm employment is not significantly correlated with farmland outflow, while farmers with land-poor farmers have the strongest positive correlation between farmland inflow and capital input. Third, even if increasing farmland outflow can facilitate the expansion of large-scale land operations, the investment effect of off-farm employment remains unrealized. Finally, the lag of the farmland transfer market relative to the labor transfer market and the rural capital market hinders the growth of optimal-scale agricultural management.

Analysis of Factors Affecting the Spatial Association Network of Food Security Level in China

Food security serves as the cornerstone of national security, intricately linked to social stability and economic progress. Currently, with the swift evolutions in social economy, logistics and transport, information dissemination, and technological advancements, there has been a marked increase in the cross-regional flow of food production, distribution, and consumption. Consequently, the spatial interdependence of food security across different regions has grown increasingly salient. This paper investigates the spatial interrelationship of food security levels in China through a network analysis framework, examining its determinants and network dynamics. The findings offer valuable insights for decision-makers aiming to optimize agricultural resource allocation and enhance national food security levels. This research establishes a comprehensive evaluation index system for assessing food security levels in China across four dimensions: production security, distribution security, supply security, and consumption security. Employing data from 30 provinces between 2008 and 2022, the entropy method quantifies food security levels, while a modified gravity model underpins the construction of a spatial association network. This framework subsequently examines the network’s structural characteristics and the factors influencing its formation. The results reveal that: (1) China’s food security levels demonstrate a consistent upward trajectory over the study period, though significant regional disparities persist. The central region surpasses the national average, while the eastern and western regions lag. Recently, the western region has shown accelerated improvements in food security, followed by the central area, with the eastern region maintaining steady growth. (2) A structurally robust spatial correlation network of food security has emerged, characterized by variations in the number of network relationships, fluctuations in network density, and a decline in network efficiency while still exhibiting pronounced small-world characteristics. (3) The network displays a clear core-periphery structure, with Shanghai, Beijing, and Jiangsu positioned centrally, playing pivotal intermediary roles, whereas remote provinces such as Gansu, Ningxia, and Liaoning occupy the periphery. (4) The four major regions demonstrate sparse internal connectivity yet robust inter-regional ties, resulting in pronounced spillover effects. (5) Various factors, including geographic distance, provincial proximity, disparities in economic development levels, variations in marketization, differences in agricultural human capital, and disparities in land productivity, significantly impact the establishment of spatial correlations in food security. The affirmative influences of geographic distance and neighboring relations, along with the beneficial shifts in economic development disparities, suggest that the flow of technology and resources plays a crucial role in reinforcing spatial connections.

A Semantic Segmentation Method for Winter Wheat in North China Based on Improved HRNet

Winter wheat is one of the major crops for global food security. Accurate statistics of its planting area play a crucial role in agricultural policy formulation and resource management. However, the existing semantic segmentation methods for remote sensing images are subjected to limitations in dealing with noise, ambiguity, and intra-class heterogeneity, posing a negative impact on the segmentation performance of the spatial distribution and area of winter wheat fields in practical applications. In response to the above challenges, we proposed an improved HRNet-based semantic segmentation model in this paper. First, this model incorporates a semantic domain module (SDM), which improves the model’s precision of pixel-level semantic parsing and reduces the interference from noise through multi-confidence scale class representation. Second, a nested attention module (NAM) is embedded, which enhances the model’s capability of recognizing correct correlations in pixel classes. The experimental results show that the proposed model achieved a mean intersection over union (mIoU) of 80.51%, a precision of 88.64%, a recall of 89.14%, an overall accuracy (OA) of 90.12%, and an F1-score of 88.89% on the testing set. Compared to traditional methods, our model demonstrated better segmentation performance in winter wheat semantic segmentation tasks. The achievements of this study not only provide an effective tool and technical support for accurately measuring the area of winter wheat fields, but also have important practical value and profound strategic significance for optimizing agricultural resource allocation and achieving precision agriculture.

UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain, as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning. The conventional method of watermelon yield estimation relies heavily on manual labor, which is both time-consuming and labor-intensive. To address this, this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle (UAV) videos for detection and counting of watermelons. This pipeline uses You Only Look Once version 8 s (YOLOv8s) with panorama stitching and overlap partitioning, which facilitates the overall number estimation of watermelons in field. The watermelon detection model, based on YOLOv8s and obtained using transfer learning, achieved a detection accuracy of 99.20 %, demonstrating its potential for application in yield estimation. The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications compared with the video tracking based detection and counting method. The counting accuracy reached over 96.61 %, proving a promising application for yield estimation. The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.

The impact of labor force aging on agricultural total factor productivity of farmers in China: implications for food sustainability

IntroductionThe rapid aging of the labor force has serious implications for socioeconomic development and poses challenges to food sustainability. Existing studies have focused on the impact of labor force aging on land productivity, but little attention has been paid to the comprehensive capacity of agricultural production, namely the total factor productivity of agriculture.MethodsWe estimate the impact of labor force aging on total factor productivity in agriculture using data from 170,506 sample farm households from the National Fixed Point Survey data from 2003 to 2020. We estimate the results using a panel fixed effects approach and mitigate the endogeneity problem using an instrumental variables approach to ensure the robustness of the estimates.Results and discussionThe results show that labor force aging has an adverse effect on the agricultural total factor productivity of farm households and remains robust after addressing endogeneity and substituting explanatory variables. The aging effect shows significant heterogeneity across different food functional areas with different types of farm households. The negative impact of labor force aging on total factor productivity in agriculture is mainly concentrated in the main grain-production and grain-producing and marketing-balanced areas. We also find that the negative impact of labor force aging is more significant among large-scale farmers. The mechanism analysis finds that the aging of the labor force adversely affects the agricultural total factor productivity by inhibiting technological progress and reducing the efficiency of agricultural resource allocation. These insights, when considered in the light of global trends towards agricultural labor aging, suggest that policy interventions aimed at promoting technological adoption, enhancing resource allocation efficiency, and supporting the transitioning of older farmers may hold promise for maintaining food sustainability and addressing the challenges posed by an aging agricultural workforce in numerous developing nations.

Nonlinear Effects of Agricultural Technology on Sustainable Grain Production in China

Grain production is an important element of the United Nations Sustainable Development Goals, regarding livelihoods and social stability. This article uses data on agricultural technology, social factor and grain production in China from 2011 to 2022, and uses the Generalized Additive Model (GAM) to deeply explore the nonlinear impact of agricultural technology and social factor on grain production. The results of the study show that (1) China’s grain output is generally on a growing trend, but the growth rate is declining and fluctuating significantly. There is a significant difference in grain production before and after the COVID-19 epidemic. Moreover, the output in the northern region is significantly higher than that in the south. (2) Except for Consumption expenditure per capita, all other agricultural technology and social factor variables are positively correlated with grain out. (3) The impact of agricultural technology and social factor on grain output shows significant non-linear characteristics, and its impact effect varies in different intervals. Specifically, When the value of the agricultural meteorological observation service station is 20-25, the effective irrigation area is greater than 1800, consumption expenditure per capita greater than 17000 and the total sowing area of crops is 7500, it can significantly increase grain yield. On the contrary, if the emission value of chemical oxygen demand exceeds 130, it has a significant inhibitory effect on grain yield. Furthermore, the effect on grain yield peaks when the total power of agricultural machinery, GDP, and the number of unemployed people in cities approach 3000, 10000, and 20, respectively. The results of the study provide an important basis for optimizing the allocation of agricultural resources and enhancing the efficiency of grain production. Finally, some practical policy recommendations are put forward.

Research on the impact of climate change on the income gap between urban and rural areas-empirical analysis based on provincial panel data in China.

Narrowing the income gap between urban and rural areas is the key to achieving common prosperity in China. On the basis of analyzing the mechanism of climate change's impact on urban-rural income gap, this article empirically analyzes the impact of climate change on urban-rural income gap using provincial-level panel data of 30 provinces in China from 2011 to 2020. Research indicates that climate change significantly impacts the urban-rural income gap at the 1% significance level, implying that climate change exacerbates the urban-rural income gap. This widening effect varies significantly across different regions, particularly in the western regions and areas with lower fiscal support for agriculture. Further analysis reveals that there is a mediating role between the total agricultural output value and resource mismatch in the impact of climate change on urban-rural income inequality; the digital rural construction plays a regulatory role in the impact of climate change on the urban-rural income gap. On this basis, policy recommendations are proposed to promote the development of climate-resilient agriculture, improve the meteorological forecast and early warning system, increase financial support, and optimize the allocation of agricultural resources.

The Application of a Smart Nexus for Agriculture in Korea for Assessing the Holistic Impacts of Climate Change

Sustainable development involves maximizing the benefits of development while minimizing its consequent effects on the environment. This study uses a water–energy–food nexus framework, the Smart Nexus for Agriculture in Korea (SNAK), to assess the impact of climate change on sustainable resource management in agriculture. The nexus database applied in this study comprises three individual databases related to resources, interlinkages, and resource management scenarios, which include all variables and scenarios of the framework. Different resource management scenarios were evaluated via investigating the interlinkages between resources and quantifying resource consumption and sustainability. The variable selection and application module uses the interlinkage database to quantitatively model how the production and supply of one resource affects the consumption of other resources. The scenario analysis module involves the identification and application of resource management scenarios based on policies for individual resources and climate change. The sustainability evaluation module links the previous two modules to quantify food production, the consumption of food and energy resources, carbon (CO2) emissions, and land use in each scenario. Finally, resource security and economic benefits were considered when estimating the sustainability index of each scenario. The SNAK platform is anticipated to possess the ability to analyze environmental, social, and economic systems grounded in water, energy, and food. It is believed that the platform can optimize the timing and allocation of agricultural resources, leading to the derivation of optimal management scenarios. Furthermore, the platform will utilize water–energy–food linkage assessments to formulate scenario-based policies addressing food demand, water resource utilization, and energy consumption.

Enhancing agricultural environmental performance: Exploring the interplay of agricultural productive services, resource allocation, and marketization factors

Exploring the factors that affect agricultural environmental performance enriches our understanding of the environmental implications of agricultural practices. Using panel data for 79 prefecture-level cities in the middle and lower reaches of the Yangtze River in China from 2003 to 2020, this study theoretically determines and empirically tests the relationship between agricultural productive services, resource allocation, and agricultural environmental performance. The results show that agricultural productive services can significantly improve agricultural environmental performance. Marketization process has a positive moderating function on the direct effect of agricultural productive services on agricultural environmental performance, which increases with the improvement of marketization process. While marketization process has a significant moderating function on the mediating effect of labor misallocation and capital misallocation in both the first and second halves of the path, marketization has a significant moderating function on the mediating effect of land misallocation in the first half of the path. Based on results, we provide reference for agricultural policymakers, especially in countries dominated by smallholder farmers, to design policies on agricultural productive services, agricultural resource allocation, and market-oriented reforms, as well as to formulate differentiated agricultural environmental performance policies.

Modeling the rational use of natural resources and innovative quantum technologies in agribusiness

The influence of quantum computing techniques on the optimization of resource allocation in the agricultural sector is analyzed. The use of variational quantum eigensolver (VQE) algorithm to achieve optimal resource allocation is explored. It has been found that production functions, such as the Cobb-Douglas function, effectively describe the transformation of input parameters into output. Parameterization of a quantum circuit and minimization of the expected value of the Hamiltonian to optimize resource allocation are studied. It is determined that the quantum optimization approach demonstrates improved computational efficiency compared to classical methods. It has been found that the integration of quantum technologies can significantly improve resource allocation in agriculture. An assumption has been made about increasing productivity and sustainability in the agricultural sector thanks to quantum methods. Further research on quantum algorithms is proposed to improve the efficiency of natural resource management. New software has been developed for modeling and optimizing production functions in agriculture. The use of quantum computing to solve complex problems in the agricultural sector is justified. A detailed description of the quantum algorithm and its benefits in the context of agriculture is presented.

Assessing cropland disagreement in Tanzania using machine learning methods with Sentinel-2 and Planet Scope imagery

ABSTRACT East Africa faces major land use pressures arising from the harsh effects of climate change and fast-increasing human populations. Recent efforts to increase food production in this region have focused on cropland mapping and approaches to improve yield. Generating accurate cropland maps provides critical support to inform policy, investment, and logistical decisions that address food security, but cropland disagreement assessment studies are lacking mostly due to the unavailability of fine-scale data. Understanding the inconsistent nature of pre-existing global cropland mapping products necessitates disagreement assessment as the most valuable urgent tool to align agricultural resource allocation and policy realignment to tackle food security challenges. This study evaluates cropland disagreement in Kilombero and Ulanga districts in Morogoro, Tanzania based on land use land cover (LULC) maps generated using machine learning methods and high-resolution PlanetScope and Sentinel-2 data sets acquired from 1 January 2017 to 31 December 2018. We created time-variant median composites and performed a three-step assessment; (i) generating multi-class LULC maps in Google Earth Engine, (ii) generating croplands and non-cropland LULC classes from the multi-class images, and (iii) assessing cropland disagreement based on GIS conditional statements. Results show minimal disagreement in mapping cropland based on higher-resolution PlanetScope and Sentinel-2. Results based on the Random Forests and Support Vector Machines relatively outperform those from Classification and Regression Trees achieving 93%, 89%, and 83% with PlanetScope and 91%, 86%, and 76% with Sentinel-2, respectively. On average, 73% of the pixels were consistently classified while 27% were misclassified between cropland and non-cropland classes. Prior unsupervised land cover cluster analysis may substantiate the quality of sampling for accurate land cover classification. Our results indicate that in data-sparse regions where confirmatory ground truth data are lacking, remotely sensed cropland identification may be more successful using Support Vector Machines on newer high-resolution imagery, thereby improving crop forecasts.

Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test.

The impact of digital financial inclusion (If) and agricultural technology innovation (Gi) on agricultural carbon emissions has attracted wide attention from the academic community, but the inconsistent conclusions of existing studies and the reality that few studies have gathered them into a framework require more evidence to fill this gap, which can contribute more insights to promoting economic development and controlling carbon emissions. Taking the provincial-level relevant data of China's agriculture from 2011 to 2020 as a sample, the GMM method is used to integrally test the relationship between the three factors. The results show that (1) from 2011 to 2020, China's overall agricultural carbon emissions experienced two stages of fluctuating rise (2011-2015) and continuous decline (2015-2020). In 2015, China's agricultural carbon emissions peaked at 1,040 million tons; Overall, Hunan, Hubei, and Henan were the provinces with the largest agricultural carbon emissions; Beijing, Tianjin, and Shanghai are provinces with relatively low agricultural carbon emissions. (2) Although the impact of digital financial inclusion on agricultural carbon emissions is negative, it is not significant. (3) Agricultural technology innovation promoted the reduction of agricultural carbon emissions. If the level of agricultural technology innovation increased by 1 percentage point, agricultural carbon emissions would decrease by 0.09 percentage points. (4) Mechanism analysis showed that agricultural technology innovation could reduce carbon emissions through the efficiency of agricultural resource allocation, and its effect reached 56%. The results can provide a scientific basis for the government to formulate targeted policies, and the methods can be extended to other places.

Optimization of Agricultural Resource Allocation among Crops: A Portfolio Model Analysis

The portfolio model provides a new framework that enables farmers to make tradeoffs between the returns and risks of agricultural production. However, it has only been used in cost allocation among resources and in farmland allocation among crops at present. Thus, we conducted a portfolio model analysis based on an independent, mixed cross-section of data accounting for 1650 rural households in Minqin county, China, over a timescale of three years; the goal was to explore the feasibility and actuating extent of the portfolio model in optimizing the allocation of agricultural resources among crops other than farmland. The results showed that, by optimizing resource allocation among crops using a portfolio model, the return per unit resource of farmland, water, and labor increased by CNY 4.03 thousand per hectare, CNY 0.38 per cubic meter, and CNY 39.75 per day, without increasing risk, and with corresponding increases in use efficiency of 14.64%, 9.25%, and 13.55%, respectively, in the study area. Therefore, the portfolio model is a good tool for agricultural resource allocation optimization. Policy makers can manage agricultural resources according to their risk preference using this model. This study provides new insights into agricultural resource allocation and critical empirical evidence for return and risk management, as well as resource use efficiency improvement in agricultural production.

Social Security, Intergenerational Care, and Cultivated Land Renting Out Behavior of Elderly Farmers: Findings from the China Health and Retirement Longitudinal Survey

In China, the transfer of land management rights has always been a topic of much discussion, as it plays an important role in improving land use efficiency, achieving the optimal allocation of agricultural resources, and protecting farmers’ rights and interests. With the advent of an aging society, elderly farmers are becoming the main force of agricultural production, and their land transfer behavior influences the land transfer situation in China. Based on three-period panel data from the China Health and Retirement Longitudinal Survey (CHARLS), this study uses a binary linear regression model to explore the effects of social security, intergenerational care, and their interactions with elderly farmers’ cultivated land renting out behavior. The results show variability among elderly farmers across different regions of China, as well as a variability in other characteristics that influence cultivated land renting out behavior. It was found that: (1) pension insurance had a significant positive effect on elderly farmers’ cultivated land renting out behavior, while the effect of medical insurance was not significant. Elderly farmers who participated/received pension insurance were 4.3% more likely to choose to rent out farmland, compared to those who did not do so. (2) The frequency of intergenerational care had a significant negative effect on elderly farmers’ cultivated land renting out behavior, while the intensity of intergenerational care had no significant effect. (3) There was an interaction between the frequency of intergenerational care and social security, whereby a high frequency of intergenerational care was found to increase the probability of renting out farmland among elderly farmers with pension insurance.

Integration of Agricultural Commodity Markets in Punjab

Efficiency of resource allocation in agriculture depends on the functioning of commodity markets. Although the larger markets that are better connected with the transport and communication network are expected to be well-integrated, the same cannot be said about the smaller, more remote markets. This paper tests integration of agricultural commodity markets in Southeastern Punjab. The region is located off the main trading axis of Pakistan, the Peshawar-Karachi highway, and is mostly served by relatively small markets known as mandis. This study focuses on markets for cotton, wheat, and rice in five towns in the region. Cotton and wheat are the main crops in the area while rice is mostly grown as part of crop rotation aimed at controlling salinity. The analytical framework developed by Ravallion was used to conduct tests of market integration for the three selected commodities. Within this framework, it is possible to test for short-run integration, long-run integration or complete market segmentation. The results indicate that, generally, markets are integrated only in the long run, with short-run integration limited to some special cases. Moreover, the smaller markets are more likely to be isolated as compared to the larger markets. The small markets also take longer to fully adjust to the price shock originating from a more dominant central market. Finally, in the case of rice, it is more likely that a market would be isolated if it were small. This implies that farmers’ incentives to grow rice as a means of combating salinity may be constrained by local demand conditions.

Theoretical basis and technical path for the regional all-for-one customization model of black soil granary

The black soil area in Northeast China serves as a “ballast” to ensure China’s food security. Unreasonable development and utilization lead to serious black soil degradation in some areas and affect regional food production and economic and social development. In the context of the intensification of the contradiction between food supply and demand worldwide, we should pay more attention to the overall situation of regional sustainable development and seek for systematic, scientific, and economic solutions. This study establishes a regional all-for-one customization model of black soil granary on the basis of the regional system of human—land relationship, customized and accurate management, agricultural system theory, and agricultural informatization with the guidance of integrated geography concept. The aim of this regional all-for-one customization model is to systematically diagnose the key problems and leading factors of black soil degradation and determine a solution that combines the commonness and individuality of black soil protection from the perspective of multiscale linkage, multifactor coupling, and multitechnology cooperation. The regional all-for-one customization model of black soil granary integrates the two perspectives of “regional” and “customization” into the protection and comprehensive utilization of black soil for the first time. It adopts zoning, grading, and classification as the main strategy and big data and artificial intelligence as the main technical approaches. This model constructs three strategies of different scales by combining the “satellite—air—ground—network” 3D monitoring system and the all-for-one customization platform driven by big data and artificial intelligence. First, the “implementing strategies by regions” are implemented at the regional scale to formulate the regional agricultural resource allocation scheme and agricultural zoning, which can provide strategies to protect and utilize black soil effectively. Second, the “determining strategies in accordance with villages” are implemented at the village scale to formulate a black soil protection and utilization model for different categories of villages, which can promote the organic integration of black soil protection and rural revitalization. Third, a “one strategy for one field” concept is applied at the field scale to provide accurate strategies for soil restoration and yield improvement in a fixed, quantitative, and regular manner. Multiscale integrated demonstration and scheme verification of the regional all-for-one customization model of black soil granary are conducted in Qiqihar City at three scales, namely, region, village, and field, to solve the key issues in black soil protection and utilization and form a replicable and popularized system solution, thereby providing a model for the sustainable development of Chinese and global black soil agriculture. The proposed regional all-for-one customization model of black soil granary has important theoretical and practical value in promoting the high-quality development of regional agriculture and rural revitalization, and provides a demonstration model of land protection and utilization for the black soil area in China and the whole world.

Evaluation and advancement of the integrated circular economy model of farming and stock raising

The integrated circular economy model of farming and stock raising (ICEMFSR) has attracted increased attention as an effective model for solving the current irrational allocation of agricultural resources and realizing the agricultural value-added industrial chain. This study uses emergy analysis to comprehensively examine and evaluate the economic benefits, environmental pressures, and sustainable development levels of ICEMFSR in Shucheng County, China. The results show that the ICEMFSR possesses the value of popularization with optimally allocated resources in the studied region, in which the emergy yield ratio (EYR), emergy loading ratio (ELR), and emergy sustainable index (ESI) in this model accounted for 3.59, 1.25, and 2.89, respectively. This result indicates a leading position in the national agricultural system. Hence, this study constructs a new model based on the coupling of emergy evaluation and multi-objective linear programming to study ICEMFSR. Consequently, the EYR, ELR, and ESI respectively varied by +24.23%, −10.40%, and +38.06% after replanning of ICEMFSR. This variation implies a significant improvement in the sustainable development level of the model. In addition, the optimized scenario design for key substances is proposed based on traceability and the reduce-reuse-recycle principle, including biogasification of crop straw and enhancement of crop scientific planting capacity.

Optimization of sustainable bioenergy production considering energy-food-water-land nexus and livestock manure under uncertainty

Residual resources in agriculture provide prime raw material for bioenergy production whose optimization has potential to promote agricultural economy while mitigating environmental side-effects. Food, energy, water, and land resources are intertwined in agricultural systems. Effective management of bioenergy production, considering the nexus of these resources, is needed for the sustainable development of agriculture, which is challenging because of the uncertainties involved therein. This study proposes an optimization-assessment approach (input/output relationship) for sustainable bioenergy production in agricultural systems. The approach is capable of (1) providing decision makers with the ability to determine optimal policy options among water, land, energy, and livestock, considering the tradeoff between economic and environmental impacts for bioenergy production; (2) helping decision makers identify the level of sustainability of agricultural systems and where the effort should be made for various regions; and (3) dealing with the uncertainties to provide decision alternatives. The proposed approach is applied to a case study in the particular context of northeast China, which is predominantly an agricultural region with large bioenergy potential. The changing range of bioenergy production potential, system costs, and environmental impacts were obtained, based on different schemes for the allocation of agricultural resources among different regions. Economic-environmental impact and sensitivity analyses were conducted, and agricultural system sustainability was assessed in a changing environment. Considering the complexity due to uncertainty, the proposed approach can help manage bioenergy production in agricultural systems in a sustainable way, and will be applicable for similar agriculture-centered regions.

Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India

In agricultural sector, farm efficiency evaluation is an important means of farm management. To evaluate the farm efficiency for effective allocation of agricultural resources, data envelopment analysis (DEA) is used. Commonly, a two-stage regression analysis is used to treat the obtained efficiency values on a set of explanatory variables. However, majority of the past studies explained the variables influencing efficiency rather than efficiency prediction. This paper aims to use DEA in combination with Machine learning approach to examine and predict the impact of environmental variables on farms’ performance. Random forest (RF) algorithm has been employed, which is one of the most useful machine learning algorithms of recent times. First, DEA was used to evaluate efficiency of all the farms. Then the RF method was employed to examine the variables crucial in predicting farm performance. This multi-stage model was applied to 450 paddy producers in rural Eastern India. The results of the RF algorithm revealed that land ownership, Kisan Credit Card (KCC), and educational status were the most crucial variables which affected the performance of the paddy producers. With the identification of the major factors influencing agricultural production, new policy actions may be developed to assist the small farmers. Furthermore, this joint DEA-Machine-learning approach may help future researchers not only to investigate but also to predict the impact of the important environmental variables on farms’ performance.