Agricultural Planting Structure Research Articles

The Impact of Rural Population Aging on Agricultural Cropping Structure: Evidence from China’s Provinces

Agricultural cropping structure is related to the quality of the agricultural supply system and is a key element of the structural reform of the agricultural supply side. Based on China’s provincial panel data from 2000 to 2021, this paper empirically examines the impact and mechanism of rural population aging on the planting structure of food and cash crops by using a two-way fixed-effects model, which fills the gap in the research on the impact mechanism of the rural population aging on agricultural planting structure. The conclusions of the study show that: as the aging of the rural population deepens, the proportion of food crops planted will further increase, while the proportion of cash crops planted will decrease; agricultural mechanization will promote the further increase of the proportion of food crops planted while the proportion of cash crops planted will decrease in the deepening of the aging of rural population; the aging of the rural population has a more significant impact on the structure of agricultural planting in the eastern region and does not have a significant impact on the central and western provinces. The aging of the rural population has a significant impact on the agricultural planting structure in the eastern region, but not in the central and western provinces. This paper argues that we should fully respect the willingness of agricultural management subjects to choose planting varieties, increase the research, development, and promotion of agricultural machinery, continuously improve the level of farmers’ human capital, and further enhance the degree of organization, scale, and specialization of agricultural production.

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures.

Sap flow characteristics and water demand prediction of cash crop in hyper-arid areas

Crop sap flow is a critical indicator of crop water demand, and predicting its variation trend is essential for adjusting agricultural planting structure and managing water resource in hyper-arid areas. Presently, research on crop sap flow in hyper-arid areas faces limitations due to inadequate meteorological data and restricted monitoring duration, leading to imprecise assessments of environmental impacts on future crop sap flow. Consequently, this hinders effective irrigation scheduling and oasis sustainable development policy. This study analyzed the sap flow changes characteristics of two economic crops (red jujube and walnut) at different time scales in the oasis in the south of Tarim Basin from 2017 to 2021 and the relationship between four environmental factors (solar radiation, temperature, relative humidity and vapor pressure deficit) and the sap flow of red jujube and walnut. By using the Random Forest (RF) model and the future climate data, the sap flow of the two economic crops from 2020 to 2100 was predicted. The results showed that: (1) walnut had a longer daily peak sap flow duration compared to red jujube, whereas red jujube showed a longer monthly peak sap flow duration; (2) in hyper-arid areas, the moisture and temperature directly influenced crop sap flow, while other environmental factor affected these two factors; (3) with the temperature increases, the water demand of red jujube and walnut in Cele Oasis will exceed local water resources carrying capacity. To achieve sustainable development of oasis, it is vital to consider future climatic changes in planning the expansion and crop structure.

The Impacts and Spatial Characteristics of High-Standard Farmland Construction on Agricultural Carbon Productivity

Agricultural carbon productivity combines the dual attributes of reducing carbon emissions and stabilizing economic growth, and is a core aspect of the new era of low-carbon agricultural development. The construction of high-standard farmland is an important initiative to promote high-yield and high-efficiency agriculture, as well as environmentally sustainable development through land improvement in China. However, the impact of high-standard farmland construction on agricultural carbon productivity and its mechanisms is still in the process of being determined. In order to fill this gap, this study aims to construct a theoretical, analytical framework for the impact of high-standard farmland construction on agricultural carbon productivity. Based on the current situation of high-standard farmland construction and the characteristics of agricultural carbon productivity in China, this study used the panel data of 31 provinces, from 2003 to 2017 in China, to empirically examine the effects, mediating paths, and spatial characteristics of the construction of high-standard farmland on agricultural carbon productivity using a double fixed-effects regression model, a mediating-effects model, and a spatial econometric model. The results show that: (1) High-standard farmland construction has a positive effect on agricultural carbon productivity, with a direct effect coefficient of 0.139 after adding a series of control variables. (2) Furthermore, heterogeneity analysis shows that the impact of high-standard farmland construction on agricultural carbon productivity will vary greatly depending on the topographic characteristics of the studied area, the level of economic development, and whether it is a main grain-producing area. (3) Mechanism analysis shows that agricultural scale operation, agricultural planting structure, and agricultural technology progress all have partial mediating roles in the impact of high-standard farmland construction on agricultural carbon productivity, with mediating effect coefficients of 0.025, 0.024, and 0.013, respectively. (4) Agricultural carbon productivity has a spatial correlation, and for every 1% increase in the level of high-standard farmland construction, agricultural carbon productivity increases by 0.117%, with a direct effect of 0.074% and a spatial spillover effect of 0.043%. Our study explains the impact effects, mechanisms, and spatial spillover effects of high-standard farmland construction on agricultural carbon productivity from theoretical and empirical perspectives, thus deepening the literature on the relationship between high-standard farmland construction and agricultural carbon productivity, and providing a theoretical basis and practical references for improving agricultural carbon productivity from the perspective of high-standard farmland construction policy.

Optimization of agricultural planting structure in irrigation areas of Heilongjiang province considering the constraints of water resource system resilience

From the perspective of sustainable development in irrigation areas, imperfect constraints, which have negatively impacted previous agricultural planting structure optimization models, tend to produce unreasonable optimization results. Thus, in this paper, attempts are made to incorporate the resilience of water resource systems into the constraint set and explore the optimal layout of agricultural planting structures in irrigation areas. We selected 12 sample irrigation areas in Heilongjiang Province, China, including Chahayang, Fuyu, and Tailai, as the research areas and considered their actual situations. Then, we established a multiobjective optimization model for an agricultural planting structure with rice, soybean, and corn planting areas as decision variables; socioeconomic benefits, agricultural production benefits, ecological benefits, and water resource benefits as objective functions; and water resource system resilience and net crop output value and other factors as constraints. Fifteen scenarios were assessed through the combination of objective functions, and the nondominated sorting genetic algorithm III (NSGA-III) was used to optimize and analyze the agricultural planting structure of typical irrigation areas in Heilongjiang Province and to determine the optimal planting plan. The research results found that the function values adjusted by planting area have significantly improved compared to those before optimization. The adjustment trend of agricultural planting area is to increase the planting area of rice and soybean and reduce the planting area of corn. The introduction of constraints on water resource system resilience ensures that other benefits remain basically unchanged; thus, the water resource objective function is significantly better with resilience constraints than without them. Therefore, water resource system resilience can act as an effective constraint; this finding enriches and expands the theoretical and methodological system for optimizing the management of agricultural planting structures in irrigation areas and thus provides important technological support for ensuring regional food security, ecological security, and water security, which has substantial practical significance.

The impact of non-grain conversion of cultivated land on the relationship between agricultural carbon supply and demand

Increasing carbon sinks and reducing emissions in agricultural system is important for global sustainable development. Affected by the increasing cost and the declining profit of grain planting in China, the overall agricultural planting structure in the Northwest region showed a trend of conversion to non-grain crops. However, the impact of the non-grain conversion of cultivated land on agricultural carbon supply and demand is unclear. Therefore, taking Gansu Province as an example, this paper constructed a theoretical framework for the impact of non-grain conversion on agricultural carbon supply and demand, and empirically tests the relationship between non-grain conversion and carbon supply and demand index using a two-way fixed effects model based on statistical panel data from 2000 to 2020. The study found that the proportion of non-grain crop planting areas in the Gansu Province increased from 24.88% to 32.90% from 2000 to 2020. The proportion of non-grained medium and high value areas increased significantly and spread from a localized to a province-wide influence. In the past 21 years, the total agricultural carbon absorption showed an inverted “N” type downward development trend and was greatly affected by the carbon absorption of grain crops (especially wheat, corn), while the total agricultural carbon emission showed an “N” type upward trend and was greatly affected by the carbon emissions of non-grain crops (especially vegetables). The increase in the area of non-grain crops weakened the carbon sink effect of farmland and intensified its carbon source properties, resulting in a decrease in the carbon supply-demand index (SDIC) from 0.187 to 0.023. The rate of non-grained level (NGL) is significantly negatively correlated with the SDIC, and that for every 1% increase in the NGL, the SDIC will decrease by 0.728%. Based on this, policy recommendations for optimizing planting structures and agricultural practices are proposed with a view to providing theoretical support for achieving a win-win situation for food security and ecological safety in Gansu Province.

Development and application of a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources

The optimal allocation of agricultural water and land resources is of great significance in ensuring sustainable food production and economic benefits of farmers. However, agriculture, as an important carbon cycle ecosystem, has paid limited attention to carbon sequestration in the optimal allocation of water and land resources. Therefore, this study developed a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources. In this model, the minimum water scarcity, maximum carbon sequestration and maximum economic benefits are taken as the optimization objectives. In addition, surface water volume and groundwater volume and planting area etc. were defined as constraints, respectively. Then, the model was solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and the Entropy-weighted-TOPSIS evaluation method. The developed model was demonstrated in the largest Farm, Youyi Farm, which is one of commercial grain production base in China to analyze the optimization of water and land resources from 2010 to 2019. We found that the new WCECM, based on the simulation of a complex coupled water-carbon-economy system, can realize the optimal allocation of agricultural water and land resources to protect regional water resources, increase carbon sequestration and adjust the agricultural planting structure. In detail, through the multi-objective optimization model, the planting structure and the allocation ratio of surface water and groundwater irrigation water consumption are more suitable for this study area. After the optimization, the area planted with Rice was significantly reduced, the area planted with Maize was increased, and the area planted with Soybean did not change significantly compared with the first two crops. The planting structure has changed from focusing on paddy cultivation to dryland cultivation, with the ratio of Rice area, Maize area and Soybean area being 3:6:1. The water consumption is constrained within manageable limits, with an average annual irrigation water consumption of 2.01 × 108 m3. The amount of carbon sequestered has increased significantly, with an average annual increase of 7.8 × 108 kg. Meanwhile, the optimized economic benefits increased slightly, with a value of ¥2.35 billion. In short, optimization of water and land resources is beneficial for improving farmers' incomes, increasing carbon sequestration in agriculture, and conserving water resources.

Mapping and assessing supply and demand of crop pollination services in Shandong Province, China

Urban expansion and large-scale expansion of intensive agriculture have resulted in loss and fragmentation of pollinator habitats, posing a significant threat to the pollination services they provide. Owing to rapid changes in land use and agricultural planting structure, it is critical to understand the spatio-temporal dynamics of supply, demand, and supply-demand balances of pollination services to ensure that pollination capacity meets the requirements of crop production. This study was conducted in an agroecosystem situated in the Shandong province, eastern China, the largest agriculture output value province in China, and the spatio-temporal dynamics of the pollination services that can support the agricultural demand from 1990 to 2018 were assessed. We quantified and mapped pollination demand, pollination capacity, and supply-demand (mis)matches of pollination during 1990–2018. The pollination results indicated that supply, demand, and supply-demand matches of pollination services decreased during the last three decades, and that the entire province of Shandong was on the verge of pollination imbalances in 2018. Moreover, in 28.99% of the province, there is a severe imbalance between pollination supply and demand, resulting in unmet needs and posing a threat to crop production. Regarding spatial patterns, supply, demand, and supply-demand matches of pollination services were heterogeneous in the province, and individually targeted conservation in different regions should be devised by the local government. We provided a spatially-explicit framework to identify spatial patterns linking pollination capacity and crop demand and optimize pollination-related management to ensure agricultural sustainability and crop production.

Prediction model and demonstration of regional agricultural carbon emissions based on Isomap–ACO–ET: a case study of Guangdong Province, China

Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap–ACO–ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap–ACO–ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction.

Parametric approach for multi-objective enhanced interval linear fractional programming problem

The design (decision) variables in the presented article of a multi-objective interval fractional optimization problem based on a linear function are assumed to take the form of a closed interval using the concept of the parametric form of an interval. The original problem is initially changed into equivalent multi-objective interval linear programming with the design variables as closed intervals. Further, it is made free from interval uncertainty by changing into a classical single-objective problem using the weighted-sum method. The solutions of the model are theoretically justified by its existence. Finally, a numerical example and a case study on the agricultural planting structure optimization problem with hypothetical data are presented to support the recommended technique for the model.

New reflections on food security and land use strategies based on the evolution of Chinese dietary patterns

Food is the fundamental guarantee for the sustainable development of human society. Countries around the world put food security on their high-priority political agenda. As the largest developing country in the world, China uses only 9% of the world’s land, contributing to 25% of the world’s food and feeding one-fifth of the population. However, with the transformation and upgrading of the dietary structure, food security in China is latent with a structural crisis. This study explored the demand for cultivated land resources under the evolution of Chinese dietary patterns, examined the transition of agricultural planting structure in China, and put forward land use strategies to ensure food security. Results show that from 1987 to 2020, the Chinese gradually shifted to land-intensive food consumption. Specifically, plant-based food, such as cereals and vegetables, gradually decreased, whereas animal-based food (land-intensive), such as meat, eggs, and milk, continued to increase. The cultivated land requirements for food (LRF) progressively approached the upper limit of domestic cultivated land resources. Unfortunately, the LRF for meat consumption was equal to that for cereals consumption, and meat consumption alone contributed 70.6% to the increase in the total LRF. More importantly, the coexistence of supply shortage and excess in China’s agricultural planting structure exhibited an imbalance. Grain was trapped in a vicious cycle of more production, more import, and more inventory, but the planting area for soybean and succulence was only one-fourth of the feed grain required for animal-based food such as meat, eggs, and milk. Furthermore, this study predicted that China would be difficult to feed its population by 2050 if it relied on domestic cultivated land alone, including implementing the Chinese Food Guide Pagoda (2022). Therefore, the work suggests that it is necessary to promote the transformation of food production in China from a single supply pattern from cultivated land resources to a multi-supply pattern that relies on different types of land resources (e.g., woodlands, grasslands, and lakes). Meanwhile, it is essential to adjust the current dietary guidelines and encourages residents to adopt resource-saving diet patterns.

How does digital village construction influences carbon emission? The case of China.

Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an "inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.

Effects of Climate and Land Use Change on Agricultural Water Consumption in Baicheng County

Changes in climate and land type directly affect the transformation and utilization of regional water resources. To analyze the evolutionary characteristics and drivers of agricultural water consumption (AWC) in arid regions, the Baicheng County is selected as an example. Based on the meteorological and land use/cover data from 1990 to 2020, the Penman–Monteith model and sensitivity method were used for analysis. The results show that: (1) The water consumption of major crops during the growth period was increasing, which was caused by climate change and changes in agricultural planting structure. (2) The sensitivity of AWC to meteorological factors was as follows: mean temperature (1.56) > mean wind speed (0.6) > precipitation (−0.12) > sunshine duration (−0.06). Temperature and wind speed were the dominant factors contributing to increased water consumption in oasis agriculture. (3) The change in land type was more obvious, mainly in cultivated land and urban and rural residential land with obvious growth, while the area of water area, forestland, and grassland showed a decreasing trend. In the past 30 years, the increase in cultivated land has reached 24.32%. The increase in cultivated land area was an important reason for the increase in AWC.

Identification of soybean based on Sentinel-1/2 SAR and MSI imagery under a complex planting structure

Soybeans, as the major global oil seed crops, are among the large-scale agricultural products imported into China. In this way, the accurate identification of their growing areas is the basis for agricultural decision-making and planting structure adjustment, which are of great significance to the national food security. Currently, there is insufficient research on the identification of soybean, using remote sensing, under complex agricultural planting structures. In this paper, Guoyang County, a typical soybean-producing region in the north of Anhui Province, China, was selected as the study area, and then the multi-temporal Sentinel-1/2 (S-1/2) microwave and optical multispectral data were integrated to obtain the spatial distribution of the planting areas, via a stepwise hierarchical extraction strategy. Considering the Jeffries-Matusita (JM) distance, August 18 (i.e., the early pod setting stage) was correspondingly determined as more appropriate for soybean extraction, compared with other time phases. For this purpose, a set of rules were established to eliminate the non-cropland pixels, and the total distribution of the field vegetation was derived accordingly. In total, 30 candidate features (viz. 10 spectral bands, 11 vegetation indices, 4 texture features, and 5 microwave polarization features) were selected, and their importance was evaluated based on the ReliefF algorithm. With the support of the typical cover type samples, the weight assessment method for the ReliefF feature was further bundled with three machine-learning (ML) algorithms, namely, the random forest (RF), the back-propagation neural network (BPNN), and the support vector machine (SVM), to single out the optimum subset of the features for soybean identification. The results finally revealed that the ReliefF-RF model outperformed the other two. With nine optimum features, Kappa coefficient also reached up to 0.77–0.85. Furthermore, the extraction accuracy produced by the optimum subset of the features was significantly greater than the original 10 spectral bands (Kappa coefficient of 0.75–0.84), albeit slightly lower than the effect of the total 30 features (Kappa coefficient of 0.78–0.85).

Agricultural outputs and structure adjustments: Empirical evidence based on high-impact weather

Changes in meteorological conditions affect crop output and planting structure. Based on county-level data of agricultural statistics and weather information in Guangdong province from 1992 to 2018, this paper uses a panel regression model to examine the effects of high impact weather on agricultural outputs and identify the responses in planting structure. The findings show that the rainstorm significantly reduces the per-unit yield (PUY) of rice, and an additional day with daily excessive precipitation (≥50 mm) in the growth period leads to an average 0.25% reduction in PUY. However, the vegetable yield is not susceptible to high-impact weather. With a two-period panel regression model and sub-regional climate response analysis, we find that the agricultural planting structure is formed by the regional farming adapting to the high-impact weather (low temperature, high temperature, rainstorms, and high wind). The original rice farmland in the more climate-sensitive areas is transformed into vegetable farming, while the scale of rice is increasing in those less sensitive areas. Finally, we argue that the agricultural sector should assess the impacts of climate change on staple crops and make national climate sensitivity zoning maps. It will provide a scientific justification for making the national agriculture structural adjustment plan.

Spatiotemporal characteristics and influencing factors of agricultural low-carbon economic efficiency in china

Developing low-carbon agriculture can effectively avoid the waste of natural resources, thus contributing to the long-term sustainability of agriculture. This study uses the Super-SBM model to measure agricultural low-carbon economic efficiency (ALEE) in China from 2000 to 2018, then analyzes the spatial-temporal evolution characteristics. Simultaneously, the influencing factors of ALEE are investigated using spatial econometric model. The results show that: (1) In terms of temporal evolution, the ALEE in most provinces is declined over time, with only a few provinces improving. The ALEE in China and the three regions all show an obvious “L” trend of decline first and then stability. (2) From the perspective of spatial differentiation, provinces in eastern region have higher ALEE, while those in central and western regions have lower ALEE. Hainan’s ALEE has an absolute advantage, while Shanxi is the worst. (3) China’s ALEE shows obvious spatial agglomeration characteristics of H-H and L-L agglomeration, which are further enhanced over time. The number of L-L agglomeration provinces gradually increases, indicating that China’s ALEE has not been improved significantly. (4) Economic growth level and Agricultural scientific and Technological progress have effectively improved the ALEE. However, Capital deepening, Government fiscal expenditure, Agricultural planting structure, and Agricultural disaster all have negative impacts. Rural electricity consumption also has a negative impact, but the impact is not significant. To accelerate the development of low-carbon agriculture, all regions must not only pursue a differentiated low-carbon agriculture development path, but also accelerate agricultural transformation, strengthen research and development, and popularize low-carbon agricultural technologies, reduce the input of traditional agricultural means of production, optimize the agricultural industrial structure, and adjust agricultural subsidy policies.

Spatial-temporal characteristics and driving factors of the chemical fertilizer supply/demand correlation network in China.

The rational allocation of chemical fertilizer resources is of strategic importance in mitigating agricultural source pollution and achieving agricultural green development. The spatiotemporal correlation of chemical fertilizer supply/demand and its determinants remains unclear. In this study, based on the inter-provincial chemical fertilizer supply/demand panel data of China from 1994 to 2018, an improved gravity model was employed to determine provincial chemical fertilizer supply/demand correlations. Finally, the chemical fertilizer supply/demand evolution and its driving factors were analyzed using social network analysis and a quadratic assignment procedure. The results indicated that (1) the intensity of the spatial relationship of inter-provincial chemical fertilizer supply/demand increased in a fluctuating fashion, but there was still room for improvement. The network structure presented good stability, and the spillover effect exhibited multiple superposition characteristics; (2) the spatial correlation network of inter-provincial chemical fertilizer supply/demand presented a "core-periphery" distribution pattern of the supply, demand, and balance areas. The division of blocks in the network changed in time and space, and some provinces changed their roles and positions in the network during development; (3) chemical fertilizer-related policies (e.g., Exemption Agricultural Tax, Notice on the resumption of value added tax policy on fertilizers, and Rural Revitalization Strategy) have played a positive role in the formation and development of the interprovincial spatial correlation network of chemical fertilizer supply/demand in China; (4) natural conditions and socioeconomic factors interact to promote the formation of the spatial correlation network of chemical fertilizer supply/demand. The differences in the scale of the rural labor force, the scale of agricultural mechanization, the agricultural planting structure, the populations, and urbanization levels all had a significant impact on it. The identification of the spatial characteristics of chemical fertilizer supply/demand correlation networks offers a new perspective on taking various measures to realize the cross-regional coordination of chemical fertilizer resources, strengthen the protection and utilization of agricultural resources, and promote green agricultural development.

New method for diagnosing resilience of agricultural soil-water resource composite system: Projection pursuit model modified by sparrow search algorithm

Considering the driving mechanism of agricultural soil–water resource composite system (ASWRS) resilience, the Jiansanjiang Branch of China Beidahuang Agricultural Reclamation Group Co., Ltd., is selected as the research object. A projection pursuit (PP) model improved by the sparrow search algorithm (SSA) was proposed to measure the key drivers of resilience, and the driving mechanism of resilience was analysed in combination with the logarithmic mean Divisia index (LMDI). The performance of the built model and the rationality of the results were verified, thus providing a new horizon for the healthy and sustainable development of agricultural water and soil resources and the environment. The results indicated that the SSA-PP model constructed in this paper was superior to PP models based on genetic algorithm (GA) optimization (GA-PP) and particle swarm optimization (PSO-PP) in regard to the solution speed and optimization ability, and these three models yielded similar conclusions. Compared with those of the entropy weight (EW) method, the driving results of the SSA-PP model are more significant, and the screened key driving factors are more reasonable. It was finally determined that the impact on the resilience of the ASWRS was the result of the joint action of economic and agricultural systems. The key driving factors included the per capita gross domestic product (GDP), paddy field proportion, per capita net income and grain output per unit area. According to the LMDI results, the economic level, grain output and paddy field proportion increased, which played a positive role in driving resilience. With the use of the autoregressive integrated moving average (ARIMA) model to simulate the future evolution of the key driving factors, it was predicted that the resilience level will increase year by year in the future. It was found that accelerating the economic development process, increasing the grain output, implementing rational planning and adjusting the agricultural planting structure are effective ways to enhance resilience.

Detection and attribution of changes in cultivated land use ecological efficiency: A case study on Yangtze River Economic Belt, China

Improving the cultivated land use ecological efficiency (CLUE) to obtain greater agricultural output at a lower environmental cost, is an important part of effective management of land resources and sustainable intensification (SI). However, when evaluating CLUE, the existing research ignores the heterogeneity of the production environment of different agricultural production units, which leads to a “distortion effect”. To address this, this study focuses on the Yangtze River Economic Belt (YREB) in China as the study area and uses a three-stage super-efficiency slacks-based measure-undesirable (three-stage super-efficiency SBM-U) model to evaluate CLUE after excluding the interference of a heterogeneous agricultural production environment. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) models to analyze the driving mechanism of CLUE. The results indicate that environmental factors have a significant impact on the evaluation results of CLUE. Ignoring environmental factors leads to an overestimation of CLUE in areas with better environmental conditions, and vice versa. Second, due to the difference in input–output structure and the varied difficulty in production factor substitution, the driving mechanism of CLUE is spatiotemporal non-stationarity. During the study period, gross domestic product per capita, and farmland management scale had a positive effect on CLUE, while industrialization level had a negative effect. The impact of fiscal support for agriculture changed from insignificant to significant, in terms of improving CLUE in the western YREB. The influence direction of the multiple cropping index and agricultural planting structure changes from positive to negative and is concentrated in the east of the YREB. As a result, a series of measures are proposed to promote the improvement of CLUE and realize SI.

Reconstructing cropland change since 1650 AD in Shaanxi province, central China

Regional reconstructions of historical cropland change are essential to improve the accuracy of global datasets. In Shaanxi province, central China, frequently affected by rebellions and natural disasters over the past 300 years, the cropland area fluctuated. In this study, based on local gazetteers, statistics, and survey data, systematic steps are followed to reconstruct the cropland area change since 1650 in Shaanxi province. The result shows four stages of changes in the total cropland area: steady increase during 1650–1830 stimulated by policies and population growth, decrease during 1830–1937 affected by wars and natural disasters, rapid growth during 1937–the 1980s, and rapid decrease after the 1980s influenced by urbanization and the agricultural planting structure adjustment. The total cropland area increased from 2.2 × 104 km2 in 1650 to 3.99 × 104 km2 in 2016. The majority of the cropland concentrated on the Guanzhong region from 1650 to 2016. The cropland expanded from the center to the surroundings in the Guanzhong region, from northeastern to southwestern in northern Shaanxi, and from western to eastern in southern Shaanxi before the 1980s. Cropland area declined in all three regions after the 1980s. The comparisons show most cropland area data in the Qing dynasty from the existing regional studies are still highly dependent on the registered tax-paying cropland area. Without considering agricultural history, changes in the SAGE cropland in Shaanxi province over the past 300 years are irrational. Spatially, HYDE 3.2 dataset cannot show the “real” spatial distribution of the cropland before 1930, because too much cropland is allocated to northern and southern Shaanxi.