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Extensively managed grasslands are recognized globally for their high biodiversity and their social and cultural values. However, their capacity to deliver multiple ecosystem services (ES) as parts of agricultural systems is surprisingly understudied compared to other production systems. We undertook a comprehensive overview of ES provided by natural and semi‐natural grasslands, using southern Africa (SA) and northwest Europe as case studies, respectively. We show that these grasslands can supply additional non‐agricultural services, such as water supply and flow regulation, carbon storage, erosion control, climate mitigation, pollination, and cultural ES. While demand for ecosystems services seems to balance supply in natural grasslands of SA, the smaller areas of semi‐natural grasslands in Europe appear to not meet the demand for many services. We identified three bundles of related ES from grasslands: water ES including fodder production, cultural ES connected to livestock production, and population‐based regulating services (e.g., pollination and biological control), which also linked to biodiversity. Greenhouse gas emission mitigation seemed unrelated to the three bundles. The similarities among the bundles in SA and northwestern Europe suggest that there are generalities in ES relations among natural and semi‐natural grassland areas. We assessed trade‐offs and synergies among services in relation to management practices and found that although some trade‐offs are inevitable, appropriate management may create synergies and avoid trade‐offs among many services. We argue that ecosystem service and food security research and policy should give higher priority to how grasslands can be managed for fodder and meat production alongside other ES. By integrating grasslands into agricultural production systems and land‐use decisions locally and regionally, their potential to contribute to functional landscapes and to food security and sustainable livelihoods can be greatly enhanced.

With the rapid development of China’s economy, China has become the world’s largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China’s agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China’s agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an “inverted U-shaped” relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China’s agricultural carbon emission reduction and improvement of agricultural production efficiency.

PurposeThis article investigates the mechanism of the direct and indirect effects of epidemics on agricultural production and projects the impact of COVID-19 on agricultural output in China.Design/methodology/approachThis article first adopts a dynamic panel model and spatial Durbin model to estimate the direct and indirect effects, followed by a growth accounting method to identify the channels by which epidemics affect agriculture; finally, it projects the overall impact of COVID-19 on agriculture.FindingsThe incidence rate of epidemics in a province has a negative impact on that province's own agricultural productivity, but the increase in the input factors (land, fertilizer and machinery) can make up for the loss and thus lead to insignificant direct effects. However, this “input-offset-productivity” mechanism fails to radiate to the surrounding provinces and therefore leads to significant indirect/spillover effects. It is projected that COVID-19 will lower China's agricultural growth rate by 0.4%–2.0% in 2020 under different scenarios.Research limitations/implicationsIt is crucial to establish a timely disclosure and sharing system of epidemic information across provinces, improve the support and resilience of agricultural production in the short run and accelerate the process of agricultural modernization in the long run.Originality/valueConsidering the infectivity of epidemics, this article evaluates the mechanism of the direct and indirect effects by introducing a spatial dynamic model into the growth accounting framework. Moreover, besides the impact on input portfolio and productivity, this article also investigates whether epidemics reshape agricultural production processes due to panic effects and control measures.

Purpose The purpose of this paper is to analyze the impact of farmers’ agricultural production mode transformation, from the perspective of agricultural division of labor and cooperation, on their agricultural production efficiency including technical efficiency, pure technical efficiency and scale efficiency. Design/methodology/approach This paper analyzes the impact of the agricultural production mode’s transformation on farmers’ agricultural production efficiency, based on the classical theory of division of labor and specialization, transaction costs and cooperation. It uses 2013 survey data from 396 farms in 15 Chinese provinces to explore the contributing factors of agricultural production efficiency using a double selection model (DSM), which can correct the endogenous selection bias in farmers’ decisions. Findings Farmers that participate in agricultural division of labor and cooperation means transform their agricultural production from a traditional self-sufficient mode to one that is specialized and intensive. Agricultural division of labor measured by farmers’ participation in an agricultural division of labor in the production stages, or in agricultural products, and agricultural cooperation measured by farmers’ participation in farmers’ cooperatives significantly and positively influence their agricultural production efficiency after correcting farmers’ endogenous selection bias. Originality/value This paper proposes a unified framework to analyze the impact of farmers’ agricultural production mode transformation on their production efficiency. Further, it builds a DSM for an empirical analysis to avoid the endogenous biases in farmers’ self-selection behavior. This paper also provides ways for policy makers to improve farmers’ agricultural production efficiency from the modern agricultural production perspective.

Currently, the level of efficiency of an effective agricultural production process is determined by how it reduces natural environmental hazards caused by various types of technologies and means of agricultural production. Compared to conventional production, the aim of integrated agricultural cultivation on commercial farms is to maximize yields while minimizing costs resulting from the limited use of chemical and mineral means of production. As a result, the factor determining the level of obtained yield is the soil’s richness in nutrients. The purpose of this study was to conduct a comparative analysis of soil richness, depending on the production system appropriate for a given farm. The analysis was conducted for two comparative groups of farms with an integrated and conventional production system. The farms included in the research belonged to two groups of agricultural producers and specialized in carrot production.

Improving agricultural production efficiency is an effective means to ensure food security and promote agricultural sustainable development in China. Stable agricultural land property rights help optimize the allocation of production factors and improve production efficiency, and it is of great practical significance to study the influence of farmland tenure security on agricultural production efficiency. Therefore, this research utilizes the 2018 data of the China Labor Dynamics Survey (CLDS) to analyze the influence of farmland tenure security on agricultural production efficiency and its internal transmission mechanism under the background of agricultural land ownership confirmation. The results show that the enhancement of farmland tenure security not only directly improves agricultural production efficiency, but also indirectly affects agricultural production efficiency through the intermediary variable of agricultural investment. Moreover, it also shows that farmland tenure security has heterogeneity effects on different farmer regions and production modes and can significantly improve the production efficiency of farmers in plain and hilly areas who adopt fully mechanized and partially mechanized farming. We suggest that policymakers should also deepen the reform of the rural factor market, develop diversified rural financial institutions, actively promote the involvement of small farmers in the public sector economy, and improve the service level of agricultural machinery in order to guide the development of the tertiary industry in non-plain areas and to reduce the land endowment effect of farmers.

The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB's agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by -0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward.

As one of the largest agricultural countries in the world, China has always paid close attention to the sustainable development of agricultural production efficiency. However, with global climate change, extreme weather has become an exogenous factor that cannot be ignored, as it affects agricultural production. Most of the existing studies only consider the domestic natural resources and economic factors, without fully considering the external climate factors. This paper uses the super undesirable dynamic Slacks-Based Measures (SBM) under an exogenous variable model to simulate the external environmental factors by adding extreme weather days. The Dagum Gini coefficient and kernel density estimation are used to explore the regional differences in agricultural production in China. The results show that the agricultural production efficiency is higher in the eastern region, and the difference in agricultural production efficiency among the provinces in the middle and western regions is large, showing a trend of polarization. The difference in the Gini coefficient between the middle and western regions is more significant. The main contribution factor of the Dagum Gini coefficient is the inter-regional difference. The regional concentration degree of agriculture in China is decreasing, the regional distribution of agricultural water resources is more balanced, and the national regional difference gradually decreases. Finally, some suggestions are put forward, such as extreme weather control, agricultural water supply, and water-saving measures.

Agricultural eco-efficiency is a meaningful index that assesses agricultural sustainable development. Based on the super SBM-DEA approach incorporating agricultural carbon emissions and panel data regression, this study evaluates agricultural eco-efficiency and investigates the influencing factors in the agricultural production zone of Jilin Province. The empirical results show the following. (1) During the observation period, the average agricultural eco-efficiency exhibits a flat “M-shaped” fluctuating trend, a trend of fluctuant growth with phase characteristics, and the agricultural eco-efficiency of each county still has much room for improvement. (2) Significant spatial differences exist in agricultural eco-efficiency across counties. All of the studied counties, except for Nong’an, Huadian, Lishu, Yitong, Gongzhuling, and Qianguo, need to change their input and output structure to optimize agricultural eco-efficiency. (3) The panel data regression estimation results indicate that the agricultural technology extension level, multiple-crop index, agricultural economic development level, agricultural technology extension level, and urbanization level have close correlations with agricultural eco-efficiency. (4) The research findings have important implications for policy makers formulating agricultural environmental policies in accordance with the local conditions of various counties.

Excessive use of pesticides and fertilisers has been a key issue limiting sustainable agricultural development. China is a typical pesticide- and chemical-fertiliser-dependent agricultural production area. We have matched the target indicators related to sustainable agricultural development (SDG1 and SDG2) and analysed the gap between China and four developed countries in terms of fertiliser and pesticide use intensity and efficiency from 2002 to 2016. We have used an improved Logarithmic Mean Divisia Index model and cluster analysis to identify the factors and effects driving increased pesticide and fertiliser inputs in China, and we discuss the exploratory effects of different provinces in reducing pesticide and fertiliser application and increasing efficiency. The findings reveal that (1) China is a typical pesticide- and fertiliser-dependent agricultural production area. The average combined fertiliser application efficiency in China from 2002 to 2016 was only 28% of that of the Netherlands, and the country's average combined pesticide application efficiency was only 35% of that of the USA. (2) The most important of the three main drivers of the increase in pesticide and fertiliser inputs in China is the value added of the primary industry, contributing 56% for the period 2007-2016. (3) Further analysis at the provincial level according to four types-high-intensity high-yield type, high-intensity low-yield type, low-intensity high-yield type, and low-intensity low-yield type-clarified the provinces that should be focused on at the national level in terms of pesticide and fertiliser application reduction and efficiency increase in the future.