Arable Area Research Articles

Challenges and Adaptations for Resilient Rice Production under Changing Environments in Bangladesh

Rice-based food production is crucial for food security, socio-political stability, and economic development in Bangladesh. However, climate and environmental changes pose serious challenges to sustainable rice production in the country. This review paper critically reviews the status, challenges, and adaptation opportunities of Bangladeshi rice systems in changing environmental, demographic, and socio-economic settings. A mixed-methods approach (quantitative summary of secondary data on rice production, environmental changes, impact on rice productivity; and qualitative thematic synthesis) was used to review adaptation for resilient rice production. Significant agronomic and environmental challenges to rice-based cropping in Bangladesh are posed by rising temperatures, anomalous rainfall patterns, extreme weather, and increasing salinisation. Rice production, availability, and access have been further compromised by decreasing arable areas, labour shortages, crop diversification, and low profitability. Farmers are adapting through autonomous and centrally planned strategies such as efficient irrigation and input use, stress-tolerant cultivars, mechanisation, and income diversification. However, profitable and sustainable adaptation requires broader facilitation by the government (e.g., infrastructure, financial incentives) and agribusiness (e.g., extension services, contracting). This review paper recommends research and development support for efficient irrigation management and stress-tolerant cultivars, enabling policy initiatives, and equitable value chain participation. The insights of the review can be applied to policymakers to target policy design and decision-making for a sustainable rice system in years to come.

Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features

Mapping arable field areas is crucial for assessing agricultural productivity but poses challenges in sub-Saharan agroecosystems because of diverse crop calendars, small and irregularly shaped fields, persistent cloud cover, and lack of high-quality model training data. This study proposes several methodological improvements to overcome these challenges. Specifically, it utilizes long-term MODIS data to stratify finer Sentinel-2 reflectance and Sentinel-1 backscatter image features on a per-pixel basis. It also incorporates texture features and employs a machine learning approach with over 300,000 samples. The eastern region of Ghana was stratified into seven seasonal strata exhibiting distinct vegetation seasonality, capturing diversity in crop calendars, using long-term MODIS (2001–2009) normalized difference vegetation index phenology. Three years (2017–2019) of Sentinel-1 and Sentinel-2 original bands at 20 m were composited into dry and wet seasonal features according to the strata, from which spectral, polarimetric, and texture features were extracted. The field boundaries were digitized using PlanetScope images (2018–2019). Random Forest classifier with 10-fold cross-validation and recursive feature elimination was used for feature selection and model building. Including topographic variables, out of 137 image features, only 11 features were found important. Sentinel-2 SWIR-based spectral features were most important, followed by Sentinel-1 polarimetric (VV) and elevation features. Half of the 11 features were variance texture features, followed by spectral features. The Random Forest classifier produced a 0.78 AUC score with overall precision, recall, and F1-score of 0.96, 0.78, and 0.85, respectively. While the precision for both classes was >0.90, the recall rate for arable areas was half that of non-arable areas. Future studies could improve the technical workflow with reliable balanced sampling, narrowband hyperspectral images, and fully polarized SAR images.

Thinking about the strategy and practice path of modern agricultural industry development in the context of big data

Abstract China is in the critical period of “four synchronous development” of industrialization, informatization, urbanization, and agricultural modernization, so it is urgent to find a correct, scientific, and reasonable development strategy for modern agricultural products and promote the development of big data agriculture. In this paper, we use big data technology to determine the algorithm model of agricultural big data technology and the application system of the agricultural Internet of Things and argue for big data for agricultural planting technology, agricultural economic management, and agricultural industry upgrading in order to find the optimal strategy for the development of modern agriculture in China. As of the statistics at the end of 2019, China’s arable land transfer area has reached 440 million mu, accounting for 30.8% of the total contracted arable land area. With the subsequent land transfer entering a standardized and normalized stage, the scale operation of agricultural production is bound to speed up. In recent years, due to the application of big data technology in agricultural production, China’s modern agricultural industry has developed rapidly, with the mechanization rate of farming at around 86% and the contribution rate of science and technology at over 67%, and agricultural production has gained breakthroughs nationwide. Thus, it can be seen that the modern agricultural industry in the context of big data will usher in new development opportunities.

Spatiotemporal Analysis and Multi-Scenario Prediction of Ecosystem Services Based on Land Use/Cover Change in a Mountain-Watershed Region, China

Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. The impact of LUCC on ESs in the Qingjiang Watershed (QJW), an ecologically sensitive area, and LUCC’s role in future ESs under different land use scenarios are crucial to promoting ecological conservation and land use management. This paper assessed water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) using the InVEST model, and their responses to LUCC in the QJW from 1990 to 2018 using the geodetector and multiscale geographically weighted regression. We predicted land use patterns using the Logistic–CA–Markov model and their effects on ESs in 2034 under business as usual (BAU), ecological land protection (ELP), arable land protection (ALP) and ecological economic construction (EEC) scenarios. From 1990 to 2018, the area of cropland and woodland decreased by 28.3 and 138.17 km2, respectively, while the built-up land increased by 96.65 km2. The WY increased by 18.92%, while the SC, CS and HQ decreased by 26.94%, 1.05% and 0.4%, respectively. The increase in the arable land area led to a increase in WY, and the decrease in forest land and the increase in construction land led to a decrease in SC, CS and HQ. In addition to being influenced by land use patterns, WY and SC were influenced mainly by meteorological and topographical factors, respectively. In 2034, there was an obvious spatial growth conflict between cropland and construction land, especially in the area centered on Lichuan, Enshi and Yidu counties. Under four scenarios, WY and SC were ranked ALP > BAU > EEC > ELP, while CS and HQ were ranked ELP > EEC > BAU > ALP. Considering the sustainable eco-socio-economic development of the QJW, the EEC scenario can be chosen as a future development plan. These results can indicate how to rationally improve the supply of watershed ESs through land resource allocation, promoting sustainable regional development in mountainous watershed areas.

Land Use Transition and Its Driving Mechanism of “Human–Elephant” Conflicts Zone in Yunnan, China

In recent years, the issue of “human–elephant” conflict in the south of the Yunnan Province, China has been escalating and poses a severe threat to the livelihoods of local residents. To address this problem, this study utilized survey data from farmers in Pu’er City and villages in Xishuangbanna Prefecture, Yunnan Province. By employing land input–output analysis and spatial analysis methods, this study aims to uncover the land use transition in the research area over the past three decades and identify the driving mechanism behind this transition. The findings of this research can provide valuable guidance for reducing regional conflicts between humans and wild animals, as well as improving the livelihoods of farmers. Research indicates that farmers in the study area have significantly transformed their land use practices. The per capita arable land area has increased, and traditional grain crops are being replaced with economically profitable crops such as rubber. Rubber is the predominant crop in the conflict-prone “human–elephant” core region, while other economic crops dominate the peripheral region. The overall land use index has risen, with a greater diversity and stability in land use structure. However, the input–output efficiency of cultivated land in the “human–elephant” core region remains low, leading to a lower comprehensive land use index than that of the peripheral region. The land use transition is influenced by several factors, including socio-economic development, changes in crop comparative benefits, and the activities of wild Asian elephants. Frequent crop destruction by elephants, which results in damage to farmers’ livelihoods, is the primary cause of land use changes in “human–elephant” conflict areas. Ultimately, this conflict stems from the competition for regional land resources between humans and elephants, as humans dominate production space while elephants dominate ecological space. Local governments should optimize the layout of regional production and ecological spaces to alleviate these conflicts while also regulating circulation markets and improving farmers’ land output levels.

Financial Analysis and Recording Activities of Farmers on Corn Farming

Corn farming is a corn cultivation system that utilizes biological materials and processes to obtain a decent profit for farmers. This study aims to identify the financial analysis and recording activities of farmers in corn farming, and analyze what factors influence the financial analysis and recording activities of these farmers. The research method used is survey method. A sample of 70 corn farmers was selected using a proportional sampling technique of 25% of the total number of corn farmers. Logit regression analysis was used to analyze recording activities and financial analysis of farmers in corn farming. The results showed that the recording and financial analysis activities of corn farming farmers were influenced by the income and experience of receiving training in financial analysis and recording activities on corn farming. Factors that influence the financial analysis and recording activities of farmers in corn farming, namely: age, educational background, income, farming experience, arable land area, and training experience.

The Coupling Relationship and Driving Factors of Fertilizer Consumption, Economic Development and Crop Yield in China

China has become the largest consumer of chemical fertilizers. The excessive application of chemical fertilizers has resulted in a series of problems including environmental pollution, seriously threatening China’s sustainable development. Therefore, it is highly important to study the factors driving chemical fertilizer consumption. In this study, we used the panel data of 31 provinces in China and the Tapio decoupling model to explore the coupling relationship between fertilizer consumption, economic growth and crop yield increase, build the IPAT-LMDI model, and identify and analyze the factors driving the observed changes. The results show the following: (1) Since 2015, the consumption of chemical fertilizers in most provinces of China has decreased significantly, and the implementation of the zero-fertilizer policy in various regions has generally achieved remarkable results. (2) Since 1980, China’s crop production and economic development have undergone coordinated growth, but the decoupling relationship between chemical fertilizer consumption and economic growth has changed from weak to strong, and the dependence of China’s crop production on chemical fertilizers has gradually been reduced. (3) Fertilizer consumption in China is promoted by factors related to economic level (Pg), crop value (Cval), fertilizer efficiency (Feff), fertilization intensity (Fein), per capita arable land area (Clap) and population size (P), while it is restrained by factors related to science and technology level (Ffag), agricultural population (P1) and industrial structure (Inst). (4) Fertilizer consumption has arrived at its peak in East China, South China and Central China, while there is still room for growth in the western areas; gaps in economic and technological development between different provinces are the main factors affecting changes in fertilizer consumption. Finally, we offer specific suggestions for improving the efficiency of chemical fertilizers from the perspectives of farming modes and science and technology.

Updating of the Köppen and Thornthwaite and Mather (1955) climate classification system for the Southern Amazonas

The Climate Classification Systems (CCS) play an important role to define the geographic boundaries and agroclimatic zone. Thus, the main aim of this paper was to determine and evaluate the Köppen and Thornthwaite and Matter (1955) climatic classification for the Southern mesoregion of Amazonas. The data have been collected throughout 10 years (April 2008 through April 2018) by the National Meteorological Institute (INMET) automatic weather stations installed in the municipalities of Humaitá, Apuí, Manicoré, Boca do Acre, and Lábrea, Amazonas State, Brazil. The results have shown that Köppen’s CCS has presented the same type and subtype of climate, to the entire analyzed region. Thornthwaite and Mather's (1955) classification showed greater sensitivity to thermal and water climate variation, totaling 4 (four) types and 5 (five) climate subtypes considering the information generated by the respective water balances such as water deficit and surplus, real and potential, which can serve as a subsidy for studies of land or arable areas. On the other hand, the Koppen classification indicated more general results that are better suited to the macroscale. In this way, the climate classification of Thornthwaite and Mather (1955) showed better accuracy than the Köppen method for the study region.

Analysis of Red Algae Seaweed (Eucheuma cottonii) Cultivation in Jerowaru District, Lombok Regency

Red algae seaweed (Eucheuma cottonii) is a very important commodity to be developed because it has a high selling price and production. The purpose of this study is to determine the cultivation of red algae seaweed (Eucheuma cottonii) in Jerowaru District, East Lombok Regency, analyze the feasibility of red algae seaweed cultivation business (Eucheuma cottonii) in Jerowaru District, East Lombok Regency, find out the obstacles faced in conducting red algae seaweed (Eucheuma cottonii) cultivation business in Jerowaru District, East Lombok Regency. The method used is the descriptive method. Data collection was carried out by survey techniques. Determination of the research area is carried out by purposive sampling or deliberately. The determination of the number of respondents was carried out by the Slovin method, which was 42 respondents. The collection of respondents was carried out using the Proportional Random Sampling method. The results showed that (1) the average profit obtained is IDR 19,532,149.98 per arable land area or IDR 23,902,980.75 per hectare during one harvest season; (2) the cultivation of red algae seaweed (Eucheuma cottonii) in Jerowaru District is feasible; (3) obstacles faced by farmers in red algae seaweed cultivation business (Eucheuma cottonii) in Jerowaru District, namely lack of capital, lack of labor, pest attacks, diseases, weeds, and erratic weather. It is recommended for farmers to cultivate red algae seaweed (Eucheuma cottonii) in Jerowaru District to increase productivity and be able to overcome the obstacles faced by participating in seaweed cultivation training activities held by related agencies.

Using a variety of machine learning approaches to predict and map topsoil pH of arable land on a regional scale

AbstractIn order to accurately predict soil properties, various machine learning (ML) approaches and hybrid models constructed by integrating ML into regression kriging framework were used to predict and map arable land topsoil pH in Henan province, central China. Random forest (RF), cubist (Cu), support vector machine, artificial neural network, multiple linear regression, classification and regression trees (CART) and their hybrid models were compared for pH accuracy prediction. Among all standalone ML models, RF had the best predictive performance, in terms of the metrics employed in this study, followed by Cu, and CART was the worst. Compared with their ML counterparts, hybrid models could improve the accuracy of topsoil pH prediction to various extents. The accuracy improvement of the hybrid models constructed based on the simple ML was much greater than that based on the complex ensemble ML. Except for artificial neural network kriging , there was no significant difference between different hybrid models in the predicted results of topsoil pH. The outputs from the best predictive models showed that weak acidic soils and weak alkaline soils were the dominant arable soils in the study region, accounting for more than 30% and more than 50% of the total arable land area respectively, the topsoil pH of arable land in the north of the study area is generally higher than that in the south. Boruta variable selection revealed that altitude, climatic covariates closely related to soil moisture availability and some soil properties were the most critical factors affecting and controlling the topsoil pH of arable land.

Investigating the effectiveness of livelihood capital in reducing re-poverty risk: an empirical analysis of policy withdrawal and income structures in rural China

In the context of China’s comprehensive poverty alleviation efforts, this study explores the differences in the re-poverty risk between households that have been lifted out of poverty before and after policy withdrawal, as well as the sensitivity of different family types to their livelihood capital. The study used data from 45,141 out-of-poverty households in Yucheng County, Henan Province, from 2016 to 2020, and combined the poverty vulnerability theory and short-fall risk method to evaluate the re-poverty risk. The Tobit model was used to explore the influence of livelihood capital on the re-poverty risk. The study found that the overall re-poverty risk is 1.13%, which increases to 18.09% after direct poverty alleviation policy is withdrawn. The risk of working families is significantly lower than farming families. All kinds of livelihood capital significantly reduce the re-poverty risk, with natural capital playing the most significant role. For different family types, the marginal contribution of financial capital to reducing the re-poverty risk is relatively larger in working households, while that of natural capital is larger in farming households. Specifically, labor capacity, arable land area, local leaders, and loans have a more significant inhibitory effect on the re-poverty risk. These findings provide valuable insights for formulating policies related to increasing household income and preventing the occurrence of re-poverty.

Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas

Resource depletion, land-use change, and population growth triggered by the industrial revolution represent serious issues shared globally that have altered the structure, processes, and functions of ecosystems and had significant impacts on human well-being and survival security. This paper assesses changes in ecosystem service values (ESVs) in karst areas based on the perspective of land-use change. Guizhou province, which is typical of the South China Karst (SCK) ecologically fragile areas, was taken as a research subject. The past, current, and future spatial-temporal evolution of karst ESVs were assessed, using equivalence factors and CA-Markov modelling methods. The results show that: (1) from 1980 to 2040, arable land, woodland, and grassland occupy the main land types in the study area; at the same time, the water and built-up areas show a steady growth trend, with dramatic land use shifts occurring in the western, middle, and northern parts. (2) The overall ESVs increased by US$ 31.12 million during the study period, showing a temporal change trend of first decreasing and then increasing; forest land, grassland, and arable land area shift are the main factors of ESVs change; soil conservation, biodiversity conservation, and gas regulation functions are enhanced and play a vital role in the single ESVs increase; ESVs show a basin-type spatial distribution pattern. (3) The sensitivity index is <1, the ESV is inelastic to the VC factor adjustment, and the results are true and valid. This paper found that using quantitative methods to predict land use change of the South China Karst can provide accurate assessment of the differences in supply and demand for natural and social ecosystem services (ES) in a region, clarifying the trade-offs and synergies of ecosystem service functions, contributing to the achievement of sustainable development, and providing a practical reference for global land landscape optimization and land resource planners.

POLA PENGOMBINASIAN TANAMAN DI KELOMPOK TANI HUTAN SEJAHTERA 4 DESA PESAWARAN TAMAN HUTAN RAYA WAN ABDUL RACHMAN

The pattern of plant combinations is the arrangement of the species of plants that are cultivated in a field. This research aims to analyze the pattern of plant combinations in the arable land of KTH Sejahtera 4 Tahura Wan Abdul Rachman. The research method used is the multiple plot method, direct observation, and interviews. Plots are arranged systematically on the farmers' arable areas. The total area cultivated by KTH Sejahtera 4 Tahura Wan Abdul Rahcman farmers is 36.05 ha. The area of the sample plots made is 1,442 ha with a sampling intensity (IS) of 4%, therefore the number of plots made is 36 plots. The results showed that all farmers applied agroforestry techniques with mixed crop combinations at random. The combination combines MPTS plants, both from the forestry plant group and the agricultural plant group. From the results of the vegetation analysis, it is known that in KTH Sejahtera's arable land 4 plants that have a high level of dominance are cloves (Eugenia aromaticum L.) and nutmeg (Myristica fragrans). According to farmers' opinion, there are three types of plants that are superior, namely cocoa (Theobroma cacao), rubber (Hevea brasiliensis), and coffee (Coffea robusta L.). The plants favored by the farmers are the mainstay of their income.

Post-farming land restoration schemes exhibit higher soil aggregate stability and organic carbon: Evidence in the Three Gorges Reservoir Area, China

The Three Gorges Reservoir Area was dominated by a farming landscape but has been the target of restorative land management schemes over recent decades, wherein croplands have been converted to forests or citrus woodlands run by commercial enterprises or, alternatively, abandoned for natural vegetation succession. How these ongoing restorative land management schemes imprint soil systems remains poorly understood. Accordingly, in this study, a space-for-time substitution approach was applied to compare soil aggregate stability and organic carbon stocks between lands used for ongoing arable production and areas subjected to restorative practices. Composite soil samples from reforestation woodlands, abandoned croplands, citrus woodlands, and present-day croplands were collected, and a variety of soil metrics (i.e., water-stable aggregate percentage, mean weight diameter, geometric mean diameter, erosion resistance of the aggregates, bulk soil organic carbon and aggregate-specific soil organic carbon) were used as a basis for comparison. Reforestation woodlands and abandoned croplands exhibited a higher mass percentage of macro-aggregates, higher values of mean weight diameter and geometric mean diameter, and higher organic carbon content within the sampled 0–40 cm depth soil profiles. Reforestation and cropland abandonment significantly increased the stability of soil aggregates and organic carbon stocks. Moreover, abandoned cropland samples exhibited higher soil aggregate stability and organic carbon stocks than reforestation woodlands. In contrast, soil aggregate stability for all sampled soil depths and organic carbon content except for the topsoil (0–10 cm) of citrus woodlands exhibited lower values than those for present day croplands. Exclusion of crop farming and reestablishment of restorative vegetation have therefore contributed to changes in soil physical structure and organic carbon stocks, with reforestation and cropland abandonment being more beneficial for soil aggregate stability and soil organic carbon stocks in the Three Gorges Reservoir Area.

Analysis of the Balance between Supply and Demand of Arable Land in China Based on Food Security

Arable land is the natural resource of food production, plays a key role in safe guarding people’s livelihood, maintaining social stability, and ecological balance. In the context of the great challenge of rapid loss of arable land and rapid growth of food demand due to rapid socio-economic development, scientific forecasting of arable land demand and supply, can clarify the conservation pressure of regional arable land, provide a reference for formulating effective arable land policies, and ensuring regional food security and sustainable development. Therefore, with the help of system dynamics model and Gray-Markov model, this study predicts and analyzes the balance of arable land supply and demand in China, and reached the following conclusions. (1) During the projection period, with the continuous development of living standards and agricultural technology, both per capita food demand and food production show an upward trend. However, the combined effects of changes in diet structure and grain yields on changes in arable land demand are smaller than those brought about by population changes. Therefore, the trend of arable land demand is similar to that of population change, and the arable land demand in China reaches a peak of 112.78 million hm2 in 2026. (2) The amount of arable land possession is always greater than the amount of arable land replenishment, and the arable land supply area shows a continuous decreasing trend, and the arable land supply in China is 127.16 million hm2 in 2035. (3) China’s supply holdings are all able to meet the demand of China’s arable land, but combined with existing studies, it is insufficient to meet the demand of crop rotation fallow between 2023 and 2030, which is not conducive to sustainable development. In the future, arable land protection policies should be comprehensively improved and strictly implemented to ensure national food security and achieve sustainable use of arable land.

Changes in Land Use Pattern and Structure under the Rapid Urbanization of the Tarim River Basin

Urbanization not only affects a region’s economic development but also impacts its land use structure. As the largest inland river basin in China, the Tarim River Basin has experienced rapid economic growth and urbanization in recent years, posing a serious threat to its soil and water resources and ecological sustainability. In this study, four remote sensing data products from 1990–2020 are selected to explore the distribution of land use types and their land structure changes in the basin in the context of rapid urbanization. The results show that the built-up land area increased by 2855.74 km2 during 1990–2020, mainly from the transfer of grassland and barren land. Furthermore, the migration of the center of gravity of built-up land moved from the desert to oasis areas, indicating that the urbanization process intensified during the study period. The overall trend is toward a continuous increase in arable and built-up land area and a continuous decrease in barren land. Future trends in the Tarim River Basin predict that arable land will decrease and that built-up land will continue to increase. However, the increase in built-up land will level off, mainly due to the transfer of arable land and grassland, which accounts for 37.94% and 20.40%, respectively. The migration characteristics of the center of gravity of each land type in the basin varied widely during 1990–2020, but the land structure will tend toward a gradual balance in the future. Therefore, in the context of increasing urbanization, focusing on the sustainable development of regional soil and water resources and ecology is crucial for the coordinated development of regional resources and economy.

HABITAT STRUCTURE AND HETEROGENEITY DETERMINE A SUCCESSFUL BREEDING PROBABILITY OF THE LONG-EARED OWL (<i>ASIO OTUS</i>, STRIGIDAE, STRIGIFORMES)

In 2001 to 2020, for each Long-eared owl nest in the Crane Homeland Nature Reserve territory with an area of 48 km2, the following microhabitat parameters were determined using standard field methods: the nest type built by different bird species, the presence of a nest roof, the nesting tree species, and the nest height above the ground. In most cases, for nesting the Long-eared owl reused magpie nests (Pica pica) (77%), and half of the nests were found on willow trees (family Salicaceae) (50%). The nest height above the ground varied from 1.5 to 22 m. Vegetation on the nesting area with a radius of 500 m was determined using the “Semi-Automatic Classification Plugin” automatic vegetation classifier of QGIS software. Five habitat types were so classified: forests, shrubs, meadows, arable land, and satellite image mistakes. Village areas were allocated separately. The average shares of the largest habitat types for over 20 years on the nesting sites included meadows (51.1%) and shrubs (16%). From 2001 to 2020, a habitat change in the breeding area was observed: forest and shrub areas decreased, while the meadow, village and arable land areas increased. An analysis of the effect of habitat types on the nesting success was evaluated by means of the “gbm” and “dismo” modules in the R program using the Boosted Tree classifier algorithm. The presence of such habitat types in the nesting area as villages (24.2%), shrubs (19.3%), meadows (11.8%), and habitat heterogeneity (11.6%) most strongly affected the probability of owl successful nesting. That owls tend to use anthropogenic and agricultural territories is associated with the diversity, easy foraging and more effective protection from predators during the breeding season.

Vertical farming for crop production

Vertical farming for crop production

Findings on agricultural cleaner production in the three Gorges Reservoir Area

The ‘rural revitalization strategy’ is important to achieve sustainable development in the Three Gorges Reservoir Area (TGRA), the largest reservoir in China, located in the poverty alleviation region of the Qinling-Daba mountains, and characterized as mountainous and hilly dominated by backwards resettlements. Pig farming around the reservoir area is a key industry, accounting for 0.90% of the arable land area in the country, while the annual pig market represents a 1.37% share of the national figure. Here, 12 study sites were investigated on-site for understanding agricultural green development in the TGRA. We found two main prevailing models: one of ecological circulation (EC), based on animal husbandry and recycling. Of the 12 site, six cases of the ecological circulation model relied on pig husbandry, combined with cropping (grains/fruits/vegetables) by eco-industrial chains, such as pig-biogas-fruit (grain/vegetables), to prevent environmental pollution, while promoting agricultural economic growth by recycling fecal residues and wastewater (FSW) from pig-husbandry facilities to the fields. Our analysis predicted that a farm holding 10,000 pigs may save inorganic nitrogen and phosphorus fertilizers by as much as 74.36 and 11.15 ton·a−1, respectively. On the other hand, five cases of ecological models oriented towards agritourism provided tourists with high-quality ecological products while coordinating environment protection with economic development. In addition, 11 research points applied water and fertilizer integration system for the purpose of saving water. However, lack of sufficient supporting arable land made intensive pig farming a risk of ecological degradation. Green control technologies are rarely used leading to an increase in the type and amount of pesticides. Our study has theoretical and practical significance for decision-makers to promote agricultural cleaner production (ACP).

Analysis of Virtual Water Flow Patterns and Their Drivers in the Yellow River Basin

Virtual water flows have a profound impact on the natural water system of a country or region, and they may help conserve local water resources or exacerbate water scarcity in some areas. However, current research has only focused on the measurement of virtual water flows, without analysis of the causes of virtual water flow patterns. This study first obtained virtual water flow patterns across provinces by constructing a multi-regional input–-output (MRIO) model of the Yellow River basin in 2012 and 2017, and then analyzed its driving factors by applying the extended STIRPAT model to provide directions for using virtual water trade to alleviate water shortages in water-scarce areas of the basin. We found the following: (1) The Yellow River basin as a whole had a net virtual water inflow in 2012 and 2017, and the net inflow has increased from 2.14 billion m3 to 33.67 billion m3. (2) Different provinces or regions assume different roles in the virtual water trade within the basin. (3) There is an obvious regional heterogeneity in the virtual water flows in different subsectors. (4) Per capita GDP, tertiary industry contribution rate, consumer price index, and water scarcity are the main positive drivers of virtual water inflow in the Yellow River Basin provinces, while primary industry contribution rate, per capita water resources, and water use per unit arable area promote virtual water outflow. The results of this paper present useful information for understanding the driving factors of virtual water flow, which could promote the optimal allocation of water resources in the Yellow River basin and achieve ecological protection and high-quality development in this area.