Crop Planting Structure Research Articles

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.

Reducing agricultural nitrogen use: A price endogenous partial equilibrium analysis in the Yangtze River Basin, China

The overuse of nitrogen fertilizers in agricultural production in China, resulting in negative impacts on the environment, has become a serious issue. Thus, reducing agricultural nitrogen use has become one of the top priorities for achieving the sustainable development goals of the Chinese agricultural sector. Searching for effective approaches to reduce nitrogen use is essential to agricultural and environmental sustainability. In this study, we selected the Yangtze River Basin as the research area, owing to its critical role in Chinese agricultural production, and established a price endogenous partial equilibrium model to simulate the effect of nitrogen use reduction from nitrogen use optimization (NUO) and nitrogen use efficiency improvement (NUE+). Based on agricultural datasets in 2019, simulation results revealed that 1) NUO helped reduce nitrogen use and nitrogen loss by 6.99% and 7.50%, respectively; if changes in the acreage are considered, then the reduction effect will be less significant; 2) nitrogen use decreased continuously with NUE+, and the reduction rate was 7.85%, 15.38%, 22.65%, and 28.02% under the NUE+10%, NUE+20%, NUE+30%, and NUE+40% scenarios, respectively, and nitrogen loss was highly sensitive; and 3) the crop heterogeneity indicated that cereals are regarded as nitrogen-overuse crops and more sensitive to nitrogen use reduction under the NUE+ scenarios than oil crops. Accordingly, in this study, we suggested that practical NUO and NUE+ policies and incentives are necessary, and flexible adjustment strategies for crop-planting structures, such as enlarging the acreage for cereals, may be useful in reducing nitrogen use in the Yangtze River Basin.

Response of dryland crops to climate change and drought-resistant and water-suitable planting technology: A case of spring maize

Climate change has a significant impact on agriculture. However, the impact investigation is currently limited to the analysis of meteorological data, and there is a dearth of long-term monitoring of crop phenology and soil moisture associated with climate change. In this study, temperature and precipitation (1957–2020) were recorded, crop growth (1981–2019) data were collected, and field experiments were conducted at central and eastern Gansu and southern Ningxia, China. The mean temperature increased by 0.36°C, and precipitation decreased by 11.17 mm per decade. The average evapotranspiration (ET) of winter wheat in 39 years from 1981 to 2019 was 362.1 mm, demonstrating a 22.1-mm decrease every 10 years. However, the ET of spring maize was 405.5 mm over 35 years (1985–2019), which did not show a downward trend. Every 10 years, growth periods were shortened by 5.19 and 6.47 d, sowing dates were delayed by 3.56 and 1.68 d, and maturity dates advanced by 1.76 and 5.51 d, respectively, for wheat and maize. A film fully-mulched ridge–furrow (FMRF) system with a rain-harvesting efficiency of 65.7-92.7% promotes deep rainwater infiltration into the soil. This leads to double the soil moisture in-furrow, increasing the water satisfaction rate by 110-160%. A 15-year grain yield of maize increased by 19.87% with the FMRF compared with that of half-mulched flat planting. Grain yield and water use efficiency of maize increased by 20.6 and 17.4% when the density grew from 4.5×104 to 6.75×104 plants ha–1 and improved by 12.0 and 12.7% when the density increased from 6.75×104 to 9.0×104 plants ha–1, respectively. Moreover, responses of maize yield to density and the corresponding density of the maximum yield varied highly in different rainfall areas. The density parameter suitable for water planting was 174 maize plants ha–1 with 10 mm rainfall. Therefore, management strategies should focus on adjusting crop planting structure, FMRF water harvesting system, and water-suitable planting to mitigate the adverse effects of climate change and enhance sustainable production of maize in the drylands.

Changes in the Water-Energy Coupling Relationship in Grain Production: A Case Study of the North China Plain.

Water consumption and energy consumption are inevitable in grain production, but few studies have focused on the integrated assessment of these two indicators and their relationships. To address the research deficiency, taking the North China Plain (NCP) as a case study, this paper quantifies the changes in grain crop planting structure and the accompanying changes in irrigation water consumption (IWC) and energy consumption (EC) in the NCP. On this basis, the water-energy coupling index (CI) is constructed to analyze the water-energy coupling relationship in the context of grain crop planting structure change. The results revealed that the sown area of three of the four main grain crops in the NCP, namely winter wheat, summer maize, and rice, roughly increased in the south and decreased in the north, while the sown area of spring maize increased in most counties where it was planted in the NCP from 2000 to 2015. With the change of grain crop planting structure, IWC and EC of winter wheat in the NCP decreased by 19.87 × 106 m3 and 16.78 × 108 MJ, respectively, mainly distributed in the Beijing-Tianjin-Hebei region, while IWC and EC of other crops all increased. In terms of CI values, although that of spring maize increased, those of winter wheat, summer maize, and rice all decreased, and the overall CI values of grain production in the NCP decreased from 0.442 in 2000 to 0.438 in 2015, indicating that grain crop distribution has been optimized toward a less water- and energy-intensive and more sustainable layout in the NCP. This paper can add case and methodological support to the food-water-energy (FEW) nexus research and can also provide policy suggestions for regional crop optimization layout and conservation of both water and energy resources.

Sustainability and assessment of factors driving the water-energy-food nexus in pumped irrigation systems

Understanding the status of water-energy-food (WEF) Nexus, oriented to the whole process of water extraction and deployment, water consumption for crop growth, and food production output, in irrigation systems is essential for food security and resources sustainability. Based on WEF Nexus quantification, combining traditional agricultural water-saving and water footprint theory, the sustainable development level (SDL) of water acquisition-transfer-consumption process for pumped irrigation systems was analyzed, using the principal component analysis (PCA) and taking the Lianshui Irrigation District (LID) in eastern China as a case study, in this paper. The driving mechanism of SDL for WEF Nexus was revealed by virtue of the partial least-squares regression (PLSR). Results showed that, annual SDL of the WEF Nexus in the pumped irrigation systems was 0.3 during the study period 2005–2019, mainly due to the low SDLe and SDLf, indicating that sustainability had been concerned. PRE and RRI were significant-positive factors, while CDI functioned as significant-negative driving factors on SDL of WEF Nexus. The measures adjusting crop planting structure, transforming irrigation and drainage mode, updating water pump equipment, and increasing agricultural investment contribute to the sustainability of WEF Nexus in the observed irrigation systems. Therefore, the sustainable management can be implemented according to the unique driving factors identification of WEF Nexus in pumped irrigation systems. The research is conducive to the management and program for irrigated agricultural systems under the changing circumstances.

Satellite data-driven multi-objective simulation-optimization modeling for water-environment-agriculture nexus in an arid endorheic lake basin

In the arid endorheic lake basin with highly developed agriculture, water scarcity drives the nexus of water-environment-agriculture (WEA) while potential evapotranspiration (ETp) is a critical element linking basin-scale irrigation water demand and agro-economic development. The study proposes a satellite remote sensing data-driven multi-objective simulation-optimization (SO) framework to implement the basin-scale WEA nexus management. The hydrological model (MODFLOW-NWT) combined with the MODIS (Moderate Resolution Imaging Spectroradiometer) based ETp estimation with a 500-m spatial resolution simulates the monthly surface water (SW) and groundwater (GW) irrigation practices. Then, the multi-objective evolutionary algorithm (ε-MOMA) is applied to solve management model which includes maximization of agro-economic benefit and environmental flow and minimization of environmental risk of total nitrogen loading subject to irrigation water demand, land availability and food supply. The SO framework is validated in an arid endorheic lake basin with intensive agricultural irrigation in northwestern China. The four-dimensional Pareto-optimal solutions are achieved to optimize crop planting structure and irrigation water allocation in order to avoid cognitive myopia triggered by low-dimensional or single objective optimization. The results highlight the trade-off among the high value crop planting, SW-GW irrigation and environmental flows, and indicating that lowering nitrogen loading is more sensitive to agro-economic benefit. Historical maximum and minimum ETp scenarios are tailored to quantify the impact of ETp change on WEA nexus management. Results show the increasing ETp has a great negative effect on the environmental flow objectives. Therefore, how multi-sectoral interests change depending on the basin-wide development goals may be difficult to regulate due to complex WEA nexus and associated deep uncertainty in the future.

Multi-Objective Crop Planting Structure Optimisation Based on Game Theory

To realise the ecological protection and high-quality development of the Yellow River Basin and transition from extensive utilisation to intensive conservation of agricultural water, a multi-objective crop planting structure optimisation model was established. The model enabled highly efficient crop planting in terms of net income with high yield and low consumption of water. Thereafter, the game algorithm was used to balance different requirements of each objective function under each constraint, both competitively and cooperatively, to obtain an optimal crop planting structure. Finally, the proposed model and analysis method were demonstrated and verified using the Xiaolangdi south bank irrigation area as an example. The results indicated that using the competitive game algorithm produced a superior crop planting structure in terms of high net income, high yield, and low water usage, suggesting that the relationships between game players and objective functions should be considered in designing the optimisation model. Thus, the proposed approach provides a theoretical basis for the sustainable development of the agricultural industry by realising the intensive utilisation of water resources in a particular irrigation area.

Effects of climate change, crop planting structure, and agricultural management on runoff, sediment, nitrogen and phosphorus losses in the Hai-River Basin since the 1980s

Investigating the spatiotemporal changes in the eco-hydrological processes and environment, as well as their drivers, is essential to manage the trade-offs among water supply, agricultural production, and environment at a basin scale. In this study, we developed a framework based on the Soil and Water Assessment Tool (SWAT), together with the historical data, to evaluate the effects of changes in climate, crop planting structure, and agricultural managements on the eco-hydrological processes and environment in the Hai-River basin during 1985–2015. The results showed that an increase in temperature not only reduced runoff and sediment but also increased nitrogen and phosphorous losses in the river basin. By contrast, an increase in precipitation increased runoff directly and increased sediment and nitrogen and phosphorous losses indirectly. Runoff caused directly the difference between sediment and losses of nitrogen and phosphorous. Changes in crop planting area and fertilizer application rate affected positively the losses of nitrogen and phosphorous. Changes in irrigation area affected negatively runoff. Different crop species had different impacts on runoff and sediment. Increase in crop planting areas of maize and vegetable generally reduced runoff and sediment, while increase in crop planting areas of wheat and beans generally increased both runoff and sediment. The findings gain insights into the impacts and driving mechanisms of the changes in climate, land use, crop planting structure and agricultural management on runoff, sediment, and losses of nitrogen and phosphorus in the Hai-River basin. • Impact of crop planting structure and managements on runoff, sediment, N and P loss were assessed. • Long-term hydrological observation data, statistics data and SWAT model were used. • Increase in crop planting area could increase N and P loss. • Different increases in crop planting area have different effects on runoff and sediment. • Temperature increase reduced runoff and sediment, but increased N and P loss.

Research on the Optimization of Agricultural Industry Structure Based on Genetic Algorithm

Due to the complexity and importance of optimizing and adjusting the crop planting structure in the Jianghuai hilly Tangba irrigation area between the upper reaches of the Huaihe River from Xinyang to the lower reaches of the Huaihe River in China, and based on experimental results from the Feidong Badou Irrigation Experiment Station, the farmland was modeled using rainfall and runoff data from the Tangba irrigation area. By examining the water balance of submerged irrigation, an optimization model of the agricultural industry structure was developed using genetic algorithms, and the model was solved using an accelerated genetic algorithm. Developing the research findings may provide scientific and technological support for adjusting the planting structure and formulation of irrigation systems in the Jianghuai Hills and Tangba irrigation area between the upper reaches of the Huaihe River and Xinyang, as well as significant practical guiding significance and application value.

Extraction of soybean planting area based on feature fusion technology of multi-source low altitude unmanned aerial vehicle images

Soybean is an important food and oil crop in the world. It is of great significance to statics the planting scale accurately for optimizing the crop planting structure and world food security. The technology of accurately extracting the area of soybean planting areas at the field scale using UAV images combined with deep learning algorithms is important for the application. In this study, firstly, RGB images and multispectral images (RGN) were acquired simultaneously by the quad-rotor UAV DJ-Phantom4 Pro at a flying height of 200 m. And the features were extracted from the RGB and RGN images. Further, the fusion image of RGB + VIs and the fusion image of RGN + VIs were obtained by concatenating the band reflectivity of the original image with the calculated Vegetation Index (VI). Then, the soybean planting area was segmented from the feature fusion images by U-Net. And the accuracy of the two sensors was compared. The results showed that the Kappa coefficients obtained based on RGB image, RGN image, CME(the combination of CIVE, MExG, and ExGR), ODR(the combination of OSAVI, DVI, and RDVI), RGB + CME(the combination of RGB and CME), and RGN + ODR(the combination of RGN and ODR) were 0.8806, 0.9327, 0.8437, 0.9330, 0.9420, and 0.9238, respectively. The Kappa coefficient of the combination of the original image and the vegetation index was higher than the original image, indicating that the vegetation index calculation was beneficial to improving the soybean recognition accuracy of the U-Net model. Among them, the precision of the soybean planting area extracted from RGB + CME was the highest, and the Kappa coefficient was 0.9420. Finally, the soybean recognition accuracy of U-Net was compared with the results of DeepLabv3+, Random Forest, and Support Vector Machine. The accuracy of U-Net was the best. It can be concluded that this research proposed the method that was using U-Net trained the fusion image of the original image and vegetation index feature fusion image obtained by the UAV platform, which can effectively segment soybean planting areas. The conclusion of this work provided important technical support for farm level, family cooperatives, and other business entities to manage finely soybean planting and production at low cost.

Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration

To establish the uncertain influence that the joint distribution of precipitation and reference evapotranspiration has on net irrigation water requirement, a Copula function–Monte Carlo method (CFMC) was proposed to calculate the probability of irrigation water requirement. Taking the Jingdian Irrigation District in Northwest China as an example, the distribution laws of precipitation and reference evapotranspiration were studied. Furthermore, five typical years under different crop planting structure conditions were selected, and the variation characteristics of net irrigation water requirement in each typical year under the conditions of climate uncertainty were analyzed. The results revealed the optimal distribution functions of precipitation and reference evapotranspiration to be gamma distribution and lognormal distribution. The probability density map of the joint distribution of precipitation and reference evapotranspiration has a “saddle” shape; that is, irrigation water requirement and reference evapotranspiration are usually inversely related. As the probability of the irrigation water requirement increases, the net irrigation water requirement in the irrigation area also increases. The CFMC method can determine the design value of the net irrigation water requirement under a specific probability for typical years under different crop planting structure conditions, which can provide a reference for agricultural water resource allocation in irrigation areas.

Spatiotemporal Evolution of Crop Planting Structure in the Black Soil Region of Northeast China: A Case Study in Hailun County

Detailed characteristics of crop planting structure (CPS) evolution can inform the optimization of the crop yield proportion in the black soil region of Northeast China (BSRNC). Choosing Hailun County as an example, this study sought to analyze the geographic characteristics of CPS evolution from 2000 to 2020. Our analysis produced new spatiotemporal information based on the remote-sensing interpretation data, namely, Landsat4-5 TM, Landsat7 ETM+, and Landsat8 OLI images. The study characterized the temporal and spatial dynamics of CPS. Our results showed the following: (1) Soybean and maize were the main crops, with a total land area of 70%; they alternated as the most dominant crop. (2) The distribution breadth and aggregation intensity of soybean and maize were spatially complementary; rice had the smallest distribution range but strong water aggregation. (3) The evolution pattern of CPS was the interconversion between a single type of soybean and maize. Our results indicate that the future CPS adjustment of BSRNC needs to consider the county-level optimization of crop area proportion and crop spatial distribution. This context has excellent implications in geographically informing policymaking to adjust county-level CPS of BSRNC, thus safeguarding food security.

Parcel-Level Mapping of Horticultural Crop Orchards in Complex Mountain Areas Using VHR and Time-Series Images

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture.

Impacts of environment and human activity on grid-scale land cropping suitability and optimization of planting structure, measured based on the MaxEnt model

Making full use of agricultural resource endowment, determining the planting suitability of areas for different crops according to the environment and human activities, and optimizing planting structure are important ways to ensure stable increases in crop yield and improve food production capacity. Taking Songhua River Basin (SRB) as an example, this study used geographic distribution information on different crops and the Maximum Entropy (MaxEnt) model to determine the degree of suitability of land in SRB for cropping, and to optimize the layout of crop planting structure. The results showed that the main factors affecting land suitability for different crops, with a combined contribution >80%, were population density, Distance from road to cultivated land, normalized difference vegetation index, and total phosphorus. Under the joint influence of the environment and human activity, the total unsuitable area of the four crops became much more extensive, with the unsuitable area of soybean being the largest (173 thousand km2) and the smallest for wheat (128 thousand km2). The highly suitable area was largest for wheat (2 thousand km2), while the other three crops were less than 2 thousand km2. Suitable distribution areas for all four crops were mainly located in the center of the basin (Songnen Plain) and in a wedge in the northeast corner (Sanjiang Plain). The relationships between different crops and environment and human activities revealed that crop suitability distribution is mainly determined by human activities, rather than the environment. These results provide a scientific basis for optimizing crop layout and improving the planting system, ensuring the security of food production.

Optimization of Water and Energy Spatial Patterns in the Cascade Pump Station Irrigation District

Cascade pump station irrigation districts (CPSIDs) consume large quantities of water and energy. Water- and energy-saving results and income increases are guaranteed under the sustainable development of the CPSID. The CPSID is divided into several sub-districts based on the elevation difference of topography and pump station distributions. The spatial patterns of crops and irrigation technologies can be changed by adjusting crop planting structures and developing drip irrigation in each sub-district. Its optimization will change the spatial patterns of irrigation water and energy consumption to achieve water- and energy-saving results, increase income, and provide an ecological advantage. To obtain the optimal spatial patterns of water and energy in the CPSID, a multi-objective linear programming model of minimum irrigation water consumption, minimum energy consumption, and highest crop output value was established. This model was applied to the Jingdian Phase I Irrigation District in northwest China, and an optimal scheme of water and energy spatial patterns was obtained. Compared with the present situation, the optimal scheme could save water by 26.18%, save energy by 29.38%, and increase income by 29.55%. The increased investment in the drip irrigation project would lead to reduced irrigation water and energy consumption and increased crop output value. The research results provide a scientific basis for the sustainable development of agriculture and ecological environment protection in the CPSID.

Simulation of Water–Energy Nexus of the Spatial Patterns of Crops and Irrigation Technologies in the Cascade Pump Station Irrigation District

Cascade pump station irrigation districts (CPSIDs) consume vast amounts of irrigation water and energy. The aim of this study was to adjust the spatial patterns of crops and irrigation technologies in the CPSID to reduce the consumption of water and energy under the condition of conserving crop irrigation water. The irrigation district (ID) is divided into several sub-districts according to the topography elevation difference and the distribution of cascade pump stations (CPSs). The mathematical models of the irrigation water and energy consumption in each sub-district were established based on the relationship between the spatial patterns of crops and irrigation technologies in each sub-district. According to the present situation of the Jingdian Phase I Irrigation District in the arid region of northwest China, three modes of adjusting the crop planting structure and drip irrigation area were proposed. Based on the combination of these modes, three schemes of the spatial patterns of crops and irrigation technologies were generated. The annual energy consumption and irrigation water consumption of each sub-district in the ID of these three schemes were obtained through simulation. Compared with the present spatial patterns of crops and irrigation technologies in the Jingdian Phase I Irrigation District, Scheme 3 has the best water-saving and energy-saving effects, with an annual water saving and energy saving of 1753 × 104 m3 and 2898 × 104 kWh, and the water-saving rate and energy-saving rate were 12.34% and 15.74%, respectively. This paper also shows that the synchronous adjustment of crops and irrigation technologies among the sub-districts of ID can achieve significant water-saving and energy-saving effects.

Study on the Agricultural Air Pollution Aggravated by the Rural Labor Migration

In recent years, air pollution has received serious concerns from researchers, media, and the public sectors, but air pollution from agricultural production activities has not received enough attention. This paper focuses on agricultural air pollution in central China, which is aggravated by the ongoing rural labor migration trend. With a set of panel data released from Hubei and Hunan provinces in China, we adopt the mediating effect model to explore the relationship between rural labor migration and air pollution caused by agricultural activity in China. First, we use the inventory analysis method and principal component analysis method to calculate the comprehensive index of the air pollution of agriculture in 152 counties and districts from Hubei and Hunan provinces, and we empirically test the impact of labor migration on air pollution with a mediating effect model as well as carry out regional heterogeneity analysis on the pollution effect of these two provinces mentioned above. The analysis above indicates that the increase of labor migration has intensified the comprehensive index of air pollution caused by agricultural activity by changing the supply of labor force in the agricultural sector, the budget line of rural residents, and the scale of agricultural production and crop planting structure, but there is a difference in the indirect total effect between the two provinces mentioned above according to our regional heterogeneity analysis. This study is a necessary extension to studies on alleviating and controlling air pollution in China.

Carbon footprint of crop production in Heilongjiang land reclamation area, China

In the context of global warming, agriculture, as the second-largest source of greenhouse gas emissions after industry, had attracted widespread attention from all walks of life to reduce agricultural emissions. The carbon footprint of the planting production system of the Heilongjiang Land Reclamation Area (HLRA), an important commodity grain base in China, was evaluated and analyzed in this paper. On this basis, this paper sought feasible strategies to reduce carbon emissions from two aspects: agronomic practices and cropping structure adjustment, which were particularly crucial to promote the low-carbon and sustainable development of agriculture in HLRA. Therefore, using the accounting methods in IPCC and Low Carbon Development and Guidelines for the Preparation of Provincial Greenhouse Gas Inventories compiled by the Chinese government, relevant data were collected from 2000 to 2017 in HLRA and accounted for the carbon emissions of the planting production system in four aspects: carbon emissions from agricultural inputs, N2O emissions from managed soils, CH4 emissions from rice cultivation and straw burning emissions. Then carbon uptake consisted of seeds and straws. Finally, with farmers' incomes were set as the objective function and carbon emissions per unit of gross production value was set as the constraint, this paper simulated and optimized the cropping structure in HLRA. The results showed that there was a “stable-growing-declining” trend in the total carbon emissions and carbon uptake of the planting production system in HLRA, with total carbon emissions of 2.84×1010 kg and total carbon uptake of 7.49×1010 kg in 2017. In the past 18 years, carbon emissions per unit area and carbon emissions per unit of gross production had both shown a decreasing trend. To achieve further efficiency gains and emission reductions in the planting production system, it was recommended that the local governments strengthen the comprehensive use of straw resources, optimize irrigation and fertilization techniques, and adjust the cropping structure, i.e., increase the planting area of maize and soybeans and reduce the planting area of rice, and increase subsidies to protect the economic returns of planters. Keywords: carbon footprint, carbon emissions, carbon uptake, crop planting structure, Heilongjiang Land Reclamation Area DOI: 10.25165/j.ijabe.20221501.5588 Citation: Chu T S, Yu L, Wang D R, Yang Z L. Carbon footprint of crop production in Heilongjiang land reclamation area, China. Int J Agric & Biol Eng, 2022; 15(1): 182–191.

Spatiotemporal patterns of wheat and maize production under the evolution of crop planting structures in the Huang-Huai-Hai dry farmland, China

Spatiotemporal patterns of wheat and maize production under the evolution of crop planting structures in the Huang-Huai-Hai dry farmland, China

Synergic management of crop planting structure and biomass utilization pathways under a food-energy-water nexus perspective

With the advantages of avoiding land-use competition, biomass from agricultural residues is a promising solution to alleviate the energy crisis and environmental problems. In the traditional biomass utilization management problem, few studies have considered that land and water are key resources for the sustainable development of biomass from a life cycle view. Concerning the complex food-water-energy nexus, this paper focuses on the synergetic management of crop planting structure and biomass utilization as a whole system. An integrated framework is proposed for the co-optimization of cropland distribution and biomass utilization pathway under multiple uncertainties. Base on interval programming and fuzzy set theory, a type-2 fuzzy interval linear programming model is developed to handle multiple uncertainties with various characteristics and provide the optimum strategies for decision makers with different risk preferences. The proposed framework is verified by a case study of Hebei Province, China. Moreover, the impacts of water shortage and the implementation of the carbon price on the optimized strategies, economic and environmental benefits are investigated to provide deeper management insights when facing complex external factors. The obtained results suggest that corn and wheat will still be the primary crops, and bioethanol production will gain priority in the biomass utilization pathways for economic purposes. Water resources availability will greatly affect the crop planting allocation as well as total benefits but barely influence the biomass utilization pathways. Furthermore, with the implementation of the carbon trade market, a higher carbon price will stimulate biomass pellets production to replace fossil fuel consumption and improve the economic benefits directly.