Grain Production In China Research Articles

Monitoring the Spatio-Temporal Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua

The amount of cultivated land per capita in China is relatively low, and the phenomenon of non-agricultural cultivated land (NACL) in recent years has negatively impacted the stability of grain production in China. In this study, long-time series images obtained via satellite remote sensing were used to monitor spatio-temporal changes in NACL at the county scale. Seven-phase images were acquired from 1990 to 2020 (every five years) using medium-resolution Landsat MSS, TM, ETM+, and Sentinel MSI. Vegetation indices and texture features were extracted for all images. Terrain features such as slope, aspect and elevation were extracted from the DEM data. Combining vegetation index features, texture features, terrain features and multispectral bands, the image classification was performed using the random forest (RF) algorithm. The indices of classification accuracy assessment indices included overall accuracy (OA) and multiclass F-scores (F_m). Zonal statistics were used to calculate the area of cultivated land in towns for 1990 and 2020, and to create grades for the reduction of cultivated land. Finally, indicators including land use dynamic degree (LUDD), land use type change (LUTC) and land use change rate (LUCR) were adopted to reflect the spatio-temporal of NACL in the study area. The results show that RF classification algorithm achieves accurate and efficient land use extraction. The OA were greater than 86%, and the F_m were over 0.88. The cultivated land area in the study area showed decreasing trend. From 1990 to 2020, the ratio of cultivated land decreased from 59.75% to 50.21%. Meanwhile, the dynamic degree of cultivated land increased annually. The conversion of cultivated land into construction land was dominant, accounting for 31.84% of the total change in cultivated land over the past 30 years. This study also reveals that NACL is highly related to local economic and land-use policies. Multi-source remote sensing data have been used to quantitatively analyse the spatio-temporal changes in cultivated land conversion, providing a reference for relevant land management departments to master cultivated land use changes and adjust land management policies.

Coordinated relationship between urbanization and grain production in China: Degree measurement, spatial differentiation and its factors detection

The entropy method was used to measure urbanization, and applied the coupled coordination degree model (CCDM) to calculate the coupled coordination degree of urbanization and grain production (CCDUG) of 330 cities at the prefecture-level and above from 2000 to 2018 in China, the exploratory spatial data analysis method (ESDA) is used to reveal their spatial-temporal differentiation, and the geographical detector (GeoDetector) is used to identify the dominant factors and their interaction affecting this spatial differentiation. Although the CCDUG has entered a high coordination interval, there is still much room for improvement. The spatial differentiation and agglomeration characteristics of CCDUG are significant, showing a "center-periphery" distribution pattern with the east as the center gradually decreases towards the west. The CCDUG with high-high cluster is mainly distributed in the main grain-producing areas of Northeast Plain and North China Plain. Low-low cluster is primarily distributed in the grain balance and main marketing areas of Northwest, Southwest, and South China coast. Elevation, urban-rural income gap, opening to the outside world, economic growth, and cropland resources per capita are the dominant factors affecting the spatial differentiation of CCDUG. The interaction between factors will enhance this spatial differentiation, among which the interaction between basic endowment conditions and socio-economic factors is the strongest. Understanding and portraying the interactive relationship between urbanization and grain production and its spatial laws have important theoretical references for achieving balanced coordination between high-quality economic-social transformation and food security.

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Environmental factors have a profound impact on the evaluation of grain production efficiency. Taking environmental factors into account can more accurately measure grain production efficiency and identify the development stage of grain production. Based on the global super-efficiency SBM model, environmental factors are included in the grain production efficiency measurement system, while the temporal and spatial evolution characteristics of grain production environmental efficiency and the sources of inefficiency are analyzed. In addition, the Spatial Durbin Model is used to investigate the influencing factors and spatial spillover effects of China’s grain production environmental efficiency. The results show that: environmental factors have a significant impact on the efficiency of grain production in China; the environmental efficiency of grain production in China fluctuates with a downward trend; the environmental efficiency of grain production in different provinces show obvious spatial differentiation and geographical agglomeration; the main influencing factors of the environmental efficiency loss of grain production in major grain production areas are carbon emissions, non-point source pollution of grain production and labor input; the level of education, the level of technological development, and labor transfer have a significant positive spillover effect on the environmental efficiency of China’s grain production, yet disaster rate has a significantly negative one. Thus, it is important to pay attention to resource conservation and pollution control, strengthen cooperation and exchanges between provinces, and take the path of coordinated development between the environment and grain production.

The Impact of Transformation of Farmers’ Livelihood on the Increasing Labor Costs of Grain Plantation in China

Analyzing the recessive impacts of farmers’ livelihood transformation on the surging labor cost in grain production is conducive to finding optimization paths for grain production. This study developed the Residual Livelihood Ratio (RLR) and the Livelihood Simpson Index (LSI) to measure the transformation of farmers’ livelihood in China, and applied the multiple regression model to explore the influence of the transformation of farmers’ livelihood on the labor cost of grain production. The results show that because of the soaring increment in labor cost, the net profit of rice, wheat, and maize production decreased largely in China. The LSI increased, while the RLR decreased, which indicated that farmers’ livelihoods transitioned towards a more unbalanced income–expenditure but more flexible employment. The Residual Livelihood Ratio; the mechanization input; the grain yield per unit area; the non-grain plantation degree; and the non-agricultural land use degree showed negative impacts on labor cost in grain production, whereas the Livelihood Simpson Index and Engel’s coefficient of farmers showed positive impacts on the labor cost. This paper proposes targeted policy implications for labor cost control of the grain production in China.

Analysis on the Regional Types and Evolution Modes of Grain Production in China

<p>By adopting the spatial transfer coefficient of grain production, this article divides the 31 provinces in China into three regional types of grain production, and on this basis, into six production evolution modes. This article studies the production and marketing structure of China in recent years by studying the regional types and the evolution modes of grain production, aiming at providing stronger theoretical support for the future development of grain production in China.</p>

Regional variations in plant-available soil water storage and related driving factors in the middle reaches of the Yellow River Basin, China

Available soil water is considered the most important water resource for food production in agricultural ecosystems and sustainable vegetation restoration in arid and semi-arid regions. Compared to soil water content, plant-available soil water storage (ASWS) directly indicates the amount of soil water that can be used by plants. Understanding the spatial characteristics and controlling factors of ASWS can aid in improving the ability of the agricultural ecosystem to resist extreme natural disasters and stabilise grain production in China. In this study, ASWS in the 0–5.0 m profile was measured in an area of approximately 36.9 × 104 km2 in the middle reaches of the Yellow River Basin. The total ASWS in the 0–5.0 m profile of the study area was 113 × 1010 m3, 85% of which was stored in the > 450 mm rainfall zones. The spatial variation in ASWS was mainly controlled by climate and edaphic properties of the 0–1.0 m soil layer and < 450 mm rainfall zone. Regarding the 1.0–5.0 m soil layer and > 450 mm rainfall zone, edaphic properties were the dominant factors determining the spatial characteristics of ASWS. Based on the spatial characteristics of ASWS, the shallow soil water (0–1.0 m) in the middle region and deep soil moisture (1.0–5.0 m) in the southern region of the study area can support the rational use of soil water in agricultural and vegetation restoration activities. Large-scale revegetation activities using trees or shrubs might not be the optimal choice in regions with mean annual precipitation < 450 mm. Knowledge of the spatial characteristics and contributions of the ASWS controlling factors is indispensable for reasonable agricultural regionalisation and vegetation restoration, as well as for an invulnerable and stable grain output system.

Research on grain production efficiency in China’s main grain-producing areas from the perspective of grain subsidy

As an important foundation of economic and social development, the increase of grain production efficiency contributes to the steady growth of food output. In this paper, from the perspective of food subsidies, the DEA global Malmquist index model is applied to estimate the grain production efficiency of 13 main grain producing regions in China between 2008 and 2017, and the factors affecting the grain production efficiency are analyzed from two dimensions. The study found that from 2008 to 2017, the overall level of total factor productivity of grain production in China’s main grain-producing regions was relatively high and fluctuated, with an average annual rise rate of 1.85%. Discussions from the angle of spatial, the grain production efficiency in China’s main grain-producing areas is characterized by unbalanced spatial development, which is generally manifested as the decreasing trend from the central area to the eastern and western areas. Meanwhile, it can be seen from the decomposition index that the change of TFP of grain production in China’s major grain producing regions mostly decided by the variation of technological progress and pure technical efficiency.

Research on grain production efficiency in China's main grain producing areas from the perspective of financial support.

Grain production is vital to the national economy and people's livelihood, and improving grain production efficiency is of great significance to the sustainable development of China's economy and society. From the perspective of financial support, using the DEA global Malmquist productivity index model and based on the data of 13 main grain producing areas in China from 2001 to 2017, this paper discusses the evolution characteristics and regional distribution differences of the total factor productivity index of grain production in China's main grain producing areas. The results show that from 2001 to 2017, the total factor productivity index of grain production in China's main grain producing areas showed an overall fluctuation trend of gradual decline, with an average annual decline of 7.3%. From the perspective of spatial analysis, the grain production efficiency in China's main grain producing areas is characterized by uneven spatial development, which is generally manifested as the decreasing trend from the central region to the eastern and western regions. Meanwhile, it can be seen from the decomposition index that the change of total factor productivity of grain production in China's main grain producing areas mainly depends on the change of technical efficiency.

Assessment of the response of climate variability and price anomalies to grain yield and land use in Northeast China.

Evidence revealed that climate change has a significant impact on grain production in China. Northeast China has abundant agricultural resources which can make the maximum contribution to national food security. This study examines the effects of climate variability and price anomalies on grain yield and land use in Northeast China. The analysis showed that different climate variability phase combinations based on Pacific Decadal Oscillation and North Atlantic Oscillation present variations in signals and different magnitude of effects over the study area. The results revealed that land use by total grain crop negatively responds to the increase in price anomalies in Heilongjiang and Jilin Provinces. To assess the impact of climate change on crop yield model, the yield models under dynamically downscaled regional climate models revealed that climate variables significantly contribute to total grain yields. In the near future, minimum temperature (- 0.26 °C under CanESM2-4.5, - 4.42 °C under HadGEM2-ES), maximum temperature (- 2.82 °C under CanESM2-4.5, - 0.84 under HadGEM2-ES), and precipitation (ranged from 3.59 to 11.10%) positively contribute to total grain yields under both models. Overall, analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.

Change in grain production in China and its impacts on spatial supply and demand distributions in recent two decades

Change in grain production in China and its impacts on spatial supply and demand distributions in recent two decades

Spatial Agglomeration Pattern and Driving Factors of Grain Production in China since the Reform and Opening Up

Since the reform and opening up, regional imbalances in the development of market economy and urbanization have significantly changed the spatial agglomeration pattern of grain production (GP) in China. To characterize GP by the yield, we used the gravity center (GC) and standard deviation ellipse (SDE) to investigate the evolution characteristics of the spatial agglomeration pattern of GP and its three major crops with wheat, maize and rice in China, then establish a spatial econometric model to explore the natural and socioeconomic drivers of GP at the national level, at different crops and at different regions. The research results indicate that the GC of GP gradually shifts and expands to the northeast and presents a spatial distribution pattern in a “northeast–southwest” direction. The GC of wheat production mainly expands upwards in the east–west direction. The GC of maize production shifts in a direction that is more consistent with grain production, and there is an expansion trend in the east–west direction, while the GC of rice production has the largest north–south span, and continues to expand upward to the north by the east. The status of grain production in Northeast China and Northwest China is rising and the importance of grain production in the Southeast coastal areas are decreasing. The importance of wheat and corn production in North China continues to strengthen, and Northeast China is becoming more important for rice production, but the middle and lower reaches of the Yangtze River are still important rice producing regions. Changes in GP with significant spatial dependence are jointly affected by many factors, such as natural and socio-economic factors, and there are obvious differences among different food crops and different division regions, with the most prominent positive effects being the multiple crop index (MCI), arable land per capita (AL) and agricultural mechanization (MECH), while economic growth and urbanization are significantly negative.

Spatial spillover effects of grain production efficiency in China: Measurement and scope

With the tightening of resource constraints and the increase in urbanization, China’s grain production is facing a series of new challenges. The key to solving the current multiple dilemmas lies in the improvement of grain production capacity. This study investigates the efficiency of grain production in China by means of spatial measurement model and Markov chain, aiming to realise the sustainable development and guarantee food security. The results showed that China’s grain production efficiency confronted with the dual challenge of insufficient driving force and limited space for growth.The gravity center of grain production moved northwest, which exacerbated the fragility and instability of the food production system.Spatial spillover of grain production efficiency from neighbouring provinces can lead to a convergence effect in the province, but it is faced with problems such as increasing marginalization of the strong, the degradation of efficiency and the low proportion of the hollow pattern. At the same time, provincial spatial spillover effect presents an inverted "N" type change path, which diffusion range is mainly concentrated in a 0–3500 km zone with a evolution process of “ripple–scale–ripple”.It’s necessary to follow the laws of club convergence, gradually forming multiple overflow centres and reducing the spatial range of the cold point area. This study provides a new idea for improving grain production efficiency with the perspective of space, which has reference value in optimizing grain production.

A novel multi-variable grey forecasting model and its application in forecasting the grain production in China

In the multi-variable grey forecasting model GM(1,N), the extreme value of the independent variable is one of the important factors that affect the simulation and prediction results of the dependent variable. In this study, a smooth generation method was used to weaken the influence of the extreme value on the performance of GM(1,N), and a novel multi-variable grey forecasting model NMGM(1,N) based on the smooth generation of independent variable sequences with variable weights was constructed. The parameters of NMGM(1,N) were estimated and proved, and the time-response expression and the final restored expression of NMGM(1,N) were deduced. Besides, the detailed modeling steps were provided, and a MATLAB program for building the new model was developed. Moreover, the evaluation criteria of the model were introduced, and numerical examples were used to test the performance of the model. Lastly, the new model was applied to forecast China’s grain production. The global mean relative percentage error of the new model was only 0.689%, in comparison with the ones obtained from the GM(1,N), GM(0,N) and OGM(1,N), which were 4.268%, 3.480% and 1.302% respectively. The findings show that the new model has the best performance, which confirms the effectiveness of the structure improvement.

Technical Efficiency of China’s Agriculture and Output Elasticity of Factors Based on Water Resources Utilization

A stochastic frontier approach (SFA) model of translog production function was constructed to analyze the growth effect of agricultural production factors on grain production in China. Under the condition of unchanged cultivated land, the agricultural labor, capital, and water were regarded as input elements of the agricultural production function. The maximum likelihood estimation (MLE) method was used to analyze the technical efficiency, output elasticity, substitution elasticity, and relative variability of grain production in China from 2004 to 2018. The results showed that: (1) For the technical efficiency and output elasticity of the input factors of grain production, there were significant differences in different provinces. For example, the water resource was insufficient in Beijing and Shanghai, but the output elasticity of water was high. Heilongjiang was rich in water and had high technical efficiency. For Xinjiang, water was sufficient, but its output elasticity was deficient and the technical efficiency didn’t increase. (2) The overall technical efficiency level was relatively low and was still declining year by year; the output elasticity of water was much greater than that of capital. There was still great potential for grain growth. (3) Optimizing resource allocation and controlling the appropriate ratio of input factors to develop grain production could achieve the maximum benefits. Finally, according to the empirical results, this paper put forward some practical policy suggestions for optimizing the allocation of input factors, especially water and capital, which can ultimately improve agricultural productivity by improving technical efficiency.

Trends, distribution, and impact factors of carbon footprints of main grains production in China

China is the largest emitter of greenhouse gas (GHG) and has the largest agriculture production in the world, and thus, fully understanding the country’s agricultural GHG emissions is of considerable importance. This study evaluates the critical processes, patterns, and impact factors of the carbon footprint of grain production in China based on a new scale data set: agricultural statistics data of over 300 prefecture-level regions. Results show that fertilizer application, straw burning, and methane emissions in rice paddy are the main causes of GHG emissions. High production carbon footprint (PCF) typically appears in areas with low crop yield, whereas low PCF appears in areas with intensive production. Longitude, altitude, and per unit yield are significant correlated with the methane emissions in rice paddy. The increase in GHG emissions from fertilizer application on per unit crop yield highly contributes to the increase in PCF. Moreover, longitude, latitude, and soil properties are the most important factors in PCF variation. Promoting large-scale and intensive modern agriculture production remains a key measure to mitigate GHG emission. Findings can improve the understanding of China’s agricultural GHG emissions and subsequently be translated into management suggestions for GHG mitigation.

The Impact of Agricultural Service on Grain Production in China

The development of agricultural services was the most important change in China’s agricultural production in the past 20 years. Using nationally representative provincial-level panel data, this study shows that agricultural service has a positive impact on technical efficiency at a significance level of 0.06. Based on this study, if the share of agricultural service cost increases by 1%, technical efficiency will increase by 0.35%. In other words, this study provides an empirical explanation of the positive impact of agricultural service on productivity. Due to the heterogeneity of agricultural service, technical efficiency in Eastern China and major grain production regions is higher than that in other regions. Finally, this study confirms the convergence of technical change in China’s grain production.

Drivers of domestic grain virtual water flow: A study for China

Trade can conserve water resources in an administrative region if the region imports water-intensive commodities rather than producing them domestically. However, many recent studies of domestic virtual water flows (VWFs) have found that there is a net VWF from water-scarce regions to water-rich regions within large countries such as China. Inter-provincial grain trade will result in a considerable geographical redistribution of water through VWFs, which will make the situation of the water shortage more serious. Therefore, it is necessary to identify the governing factors of the VWFs to understand the less-than-ideal water-use patterns and to build well-informed regulations for saving water. Taking grain production in China as a case study, this paper studies the drivers of inter-regional VWFs. An integrated method combining a Logarithmic mean Divisia index (LMDI) decomposition method and a gravity model is proposed in this work. In the method, the changes in VWFs are decomposed into four portions with the LMDI method firstly. Based on that, a VWF gravity model is developed to analyze the social, economic and natural driving force behind the four changes and the total domestic grain VWFs. The main empirical results are as follows. (1) There is a trend toward the separation of grain consumers and grain producers in China. Net positive VWF within China has occurred from water-scarce north to water-rich south, and there has been a sharp growth trend in the size of this flow over time. The existing pattern of inter-province virtual water trade is exacerbating scarcities in already water-scarce regions. (2) The growth of grain exchanges was responsible for most of the increase in grain VWF. The decreasing unit water footprint of grain production drives down the number of grain VWFs; however, this decrease cannot offset the boost provided by the other effects. (3) Water endowments and geographical distance are demonstrated as have not significant influences in domestic VWFs. The most important drivers of inter-regional VWF within a country is the outward push force of the exporter and the inward pull force of the importer. The technological level of grain production and the economic conditions in the import region are less important drivers. The results indicate that the domestic VWFs of the grain trade are governed primarily by industrial distribution and regional division in the national economy rather than by water endowment. Although implementing water-saving policies in water-scarce and water-export regions further helped to alleviate this situation, the fundamental solution is to change the industrial distribution and regional division in the national economy.

Evaluating the potential health and economic effects of nitrogen fertilizer application in grain production systems of China

In past decades, large amounts of N fertilizers have been applied in agricultural production of China. N fertilizer has not only underpinned food production but also led to great concerns about negative environmental impacts. We used a life cycle assessment (LCA) approach to assess nitrogen fertilizer induced potential human health risk (HHR) and human capital loss (HCL) of rice, wheat and maize production systems. We found that the HHR of rice, wheat and maize was 633004, 264105 and 418644 DALY/yr, respectively. The rice production system caused the highest potential risk while the wheat production system the lowest. Fine particulate matter formation (FPMF) was the most serious health risk index, and accounted for nearly 87% of HHR. Sown area and crop type both play an important role in HHR. The potential loss of human capital caused by rice, wheat and maize production are 37.5, 15.6 and 24.8 billion RMB/yr, respectively. The total HCL accounted for about 5.9% of the Chinese grain yield value in 2017. We constructed 3 scenarios to evaluate the potential for reduction of human health risk. In scenario A, reducing N fertilizer rates to an appropriate level, the HHR decreased by about 23%, while in scenario B, enhancing nitrogen use efficiency and production technology, decreased HHR by about 22%. Furthermore, when we combined both scenario A and B, the HHR was reduced by as much as 37%. Improving fertilizer management practices in grain production in China has a great potential to reduce adverse impacts on human health, and more attention should also be paid to the impacts of air pollutant emissions from agricultural production on human health.

Modeling and optimization of technological process and means of mechanization of grain production for middle China based on Belarusian technology

One of the main problems in grain production in China is the high level of unit costs (high cost price). The main reason is the low level of mechanization. Development of efficient technologies and means of mechanization is a promising area allowing to decrease the level of unit costs at a given labor efficiency and maintaining the achieved yield. Grain production is a complex system associated with a large amount of information: agro-technological (crop variety, crop yield, physical-and-mechanical parameters of land plots, terms of operations, permissible speed range for specific operations, etc.), technical-and-economic (power and traction parameters, throughput, working width, operating weight, hopper volume , cost). At present, efficient methods for studying complex systems have appeared as a result of development and widespread implementation of computer mathematics systems, which allow us to study mechanization technologies and tools using multi-factor modeling and multi-criteria optimization. The paper presents a multi-factor mathematical model, peculiar for the fact that the three groups of simultaneously varying factors are taken for the first time engine power, MTA speed, timing of the main energy-intensive operations – plowing, harvesting, and as conflicting optimization parameters unit cost level, coefficient of crop losses, labor efficiency, which allowed a comprehensive study of the grain production process in any natural-production conditions. A technological process has been developed (units speed and timing of the main operations (plowing, harvesting) and the corresponding range of machines and equipment, ensuring minimum level of unit costs at a given labor efficiency and maintaining the achieved crop yield, taking into account the specifics of the Middle China (2 crops per year).

Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine

Timely and accurate information on crop planting areas is critical for estimating crop production, and earlier crop mapping can benefit decision-making related to crop insurance, land rental, supply-chain logistics, and food market. Previous efforts generally produce crop planting area maps after harvest and early season cropping information is rarely available. New opportunities emerge with rapid increase in satellite data acquisition and cloud computing platform such as Google Earth Engine (GEE) which can access and process a vast volume of multi-sensor images. Here we aimed to examine earliest identifiable timing (EIT) of major crops (rice, soybean, and corn) and generate early season crop maps independent of within-year field surveys in the Heilongjiang province, one most important province of grain production in China. The Random Forest classifiers were trained based on early season images and field samples in 2017, then were transferred (applied) to corresponding images in 2018 to obtain resultant maps. Six scenarios with different temporal intervals (10d, 15d, 20d, and 30d) and data integration (Sentinel-2 and Sentinel-1, a total of 16, 450 images) were compared to get the optimal crop maps. The results showed that the Sentinel-2 time series and 10-day composite outperformed in obtaining EITs and crop maps. We found various EITs for the three grain staples. Specifically, rice could be identified in the late transplanting stage (four months before harvest) with F1 score of 0.93, following by corn recognizable in the early heading stage (two months before harvest, with F1 score of 0.92) and soybean in the early pod setting stage (50 days before harvest, with F1 score of 0.91). The crop maps in the EITs based on the classifier transfer approach have comparable accuracies (overall accuracy = 0.91) comparing to the traditional post-season mapping approach based on current year’s all available images and samples (overall accuracy = 0.95). This study suggests the potential of growing fine resolution observations for timely monitoring of crop planting area within season, which provides valuable and timely information for different stakeholders and decision makers.