Large-scale Agricultural Production Research Articles

Breastfeeding efficiency among new mothers in a region impacted by large-scale agricultural production: Lessons from the SEMILLA study in Ecuador

Breastfeeding efficiency among new mothers in a region impacted by large-scale agricultural production: Lessons from the SEMILLA study in Ecuador

ICT and Agricultural Development in South Africa: An Auto-Regressive Distributed Lag Approach

The use of Information Communication Technology (ICT) forms a significant component of the Fourth Industrial Revolution (4IR). This study examined the impact of ICT on agricultural development in South Africa utilizing time series data from 1995 to 2022. Agricultural development was measured through agricultural output and agriculture total factor productivity as dependent variables. Traditional factors of production (land, labor, and capital) together with ICT variables (mobile cellphone subscriptions, Internet usage, and fixed telephone subscriptions) were used. Additional variables such as inflation, human development, access to energy and climate change were used. Data analysis was performed using the ARDL approach. The findings revealed that mobile phone subscriptions and Internet usage positively affect agricultural output and ATFP in the short and long run despite having a negative effect through the second lag in the short run. Fixed telephone subscriptions negatively affect ATFP in the long run while affecting output negatively in the short run through the first lag. Land, human development index, access to energy, and capital generally exhibited an increasing effect on both agricultural output and ATFP both in the short and long run through the various models estimated. Climate change and inflation were generally found to affect both agricultural output and ATFP negatively in the short and long run. The study concluded that ICT plays a significant role in promoting agricultural output and total factor productivity growth. Recommendations included that the South African government should promote the digitalization of the agriculture sector through the provision of ICT infrastructure that can be utilized by both smallholder farmers and large-scale agricultural producers.

Trends and Factors Influencing the Evolution of Spatial Patterns of Cropland toward Large-Scale Agricultural Production in China

Considering the essential expansion of agricultural production, current research primarily focuses on static factors, such as the distribution of fine-grained arable land, omitting an in-depth analysis of its developmental dynamics and key drivers. Addressing this knowledge gap is crucial for enhancing the scalability of agricultural production. This research utilizes landscape ecology techniques, correlation analysis, random forest algorithms, and structural equation modeling to explore spatial pattern trends of arable land in the Beijing–Tianjin–Hebei region. Its objective is to clarify how the expansion of agricultural production scale affects food production through changes in arable land patterns and to determine the impact of socio-economic factors on these configurations. The results show that: (1) the landscape pattern of arable land is transitioning to a more fragmented arrangement with complex contours, (2) grain yield per unit area correlates positively with the landscape pattern index in Beijing, negatively in Hebei, and exhibits no significant correlation in Tianjin, and (3) land ownership plays a crucial role in land fragmentation, alterations in land morphology, and influences other socio-economic variables. Analyzing the spatial pattern of arable land in conjunction with socio-economic factors is essential for developing holistic land management approaches, improving resource efficiency, minimizing external inputs, and mitigating food security challenges.

Winter Wheat Yield Estimation with Color Index Fusion Texture Feature

The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based on unmanned aerial vehicle (UAV) high-definition digital images, which can provide a reliable technical means for the high-precision yield estimation of winter wheat. In total, 22 visible color indexes were extracted using UAV high-resolution digital images, and a total of 24 texture features in red, green, and blue bands extracted by ENVI 5.3 were correlated with yield, while color indexes and texture features with high correlation and fusion indexes were selected to establish yield estimation models for flagging, flowering and filling stages using partial least squares regression (PLSR) and random forest (RF). The yield estimation model constructed with color indexes at the flagging and flowering stages, along with texture characteristics and fusion indexes at the filling stage, had the best accuracy, with R2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm2, 794.77 kg/hm2 and 728.85 kg/hm2, respectively. The accuracy of winter wheat yield estimation using PLSR at the flagging, flowering, and filling stages was better than that of RF winter wheat estimation, and the accuracy of winter wheat yield estimation using the fusion feature index was better than that of color and texture feature indexes; the distribution maps of yield results are in good agreement with those of the actual test fields. Thus, this study can provide a scientific reference for estimating winter wheat yield based on UAV digital images and provide a reference for agricultural farm management.

Effect of Texture Feature Distribution on Agriculture Field Type Classification with Multitemporal UAV RGB Images

Identifying farmland use has long been an important topic in large-scale agricultural production management. This study used multi-temporal visible RGB images taken from agricultural areas in Taiwan by UAV to build a model for classifying field types. We combined color and texture features to extract more information from RGB images. The vectorized gray-level co-occurrence matrix (GLCMv), instead of the common Haralick feature, was used as texture to improve the classification accuracy. To understand whether changes in the appearance of crops at different times affect image features and classification, this study designed a labeling method that combines image acquisition times and land use type to observe it. The Extreme Gradient Boosting (XGBoost) algorithm was chosen to build the classifier, and two classical algorithms, the Support Vector Machine and Classification and Regression Tree algorithms, were used for comparison. In the testing results, the highest overall accuracy reached 82%, and the best balance accuracy across categories reached 97%. In our comparison, the color feature provides the most information about the classification model and builds the most accurate classifier. If the color feature were used with the GLCMv, the accuracy would improve by about 3%. In contrast, the Haralick feature does not improve the accuracy, indicating that the GLCM itself contains more information that can be used to improve the prediction. It also shows that with combined image acquisition times in the label, the within-group sum of squares can be reduced by 2–31%, and the accuracy can be increased by 1–2% for some categories, showing that the change of crops over time was also an important factor of image features.

The Scaling-up of Agricultural Mechanization Services and the Re-shaping of Small-Scale Household Farming in China

Abstract Taking the case of the agricultural mechanization service industry as an example, this study discusses the dynamics of the transformation of China’s agricultural contracting service industry in the context of the scaling-up of agricultural production, and how the transformation is reshaping small-scale household farming in grain production. With the accelerated growth of farmland transfer, large-scale agricultural producers have been emerging rapidly in different parts of China in the past years. Since their demand for agricultural contracting services is different from that of small-scale household producers, the scaling-up of agricultural production has promoted the scaling-up of agricultural contracting services. Meanwhile, the capital surplus in the agricultural machinery manufacturing industry has created three consequences simultaneously: the excess supply of agricultural mechanization services, the acceleration of agricultural machinery renewal, and the rising cost of machinery purchases. The interactions of these three factors not only raised the threshold of the agricultural machinery service industry, which makes it difficult for part-time service providers to survive market competition, but also shortened the time window for machinery service provision, which makes it difficult for large-scale agricultural producers to balance land operation and machinery service provision. Consequently, large-scale agricultural machinery service organizations are continuously replacing those small-scale part-time service providers. During this process, small-scale household producers engaged in grain production are faced with increasing risks of being excluded from farming, both directly and indirectly, as it is difficult for small producers to deal with large service organizations, and the small-scale part-time service providers that used to serve them are continually being replaced.

ВКЛАД НИИ СЕЛЬХОЗМАШИНОСТРОЕНИЯ СССР В АГРАРНОЕ ПЕРЕУСТРОЙСТВО (1928–1932 гг.)

The modern technological culture of agricultural production based on mechanization has been formed several decades ago, underwent through transition from horse to artificial traction. This process can be viewed as the industrialization of agriculture, the technological revolution, and the modernization of the countryside. The author considers the formation and development of agricultural engineering and the formation of “motorcycle culture” in Russia’s village in the context of the “Great Waves” concept by C. Perez. The author associates the beginning of the technological revolution in the countryside with the formation of the theory of agricultural engineering (1900–1910s), its active phase — with scientific and practical developments in agricultural research institutions, an accelerated introduction of machines in large collective farms created in the USSR in 1928–1932. Established in 1928, the Scientific Research Institute of Agricultural Engineering named after V. P. Goryachkin (VISKhOM) played an important role in the technological reconstruction of the village. Based on the primary documents of the research institute, stored in the Russian State Archives in Samara, the author analyzes the Institute’s research work in the first years of its existence, the possibility of transferring foreign technologies of agricultural engineering, the channels of interaction between the main actors in the chain “science — industry — agriculture” during the period of mass collectivization of the countryside and the first five-year plan. At the same time, the Institute is considered as an actor and an active participant in the implementation of the government course for the machine development of large-scale agricultural production.

Spatial and temporal variability of rainfall in Western Bahia

Western Bahia is a region widely recognized for its significance in large-scale agricultural production, being one of the mainstays of the local economy. However, agricultural activities in this area are often challenged by extended periods of low precipitation, posing a constant threat to the productivity of major crops. Deep concerns are raised by unpredictability of rainfall, especially in February during the critical period of crops, which encompasses the flowering and/or grain filling stages of most crops in the region. In this scenario, this study assumes a central role in search for answers. Thus, this paper aimed to analyze the behavior of spatial and temporal variability of rainfall in the Western region of Bahia. The research focused on the month of February due to its critical relevance to the life cycle of local crops and its direct influence in agricultural productivity. Rainfall data were obtained from the stations network of the National Water Agency (ANA) through the Hydroweb platform for the period from 1986 to 2015. The Rain Anomaly Index (RAI) for the month of February was then calculated using equations developed by Rooy (1965). To analyze the space-time variability of drought events in their different intensities, RAI maps were generated containing eight categories ranging from extremely wet to extremely dry, for the measurement and classification of rainfall anomalies in relation to the historical average. The results revealed that throughout the analyzed period, approximately 53.33% of the February months exhibited negative anomalies, denoting drought events ranging from mild to extreme, while approximately 30% of the months displayed positive anomalies, indicating moisture levels ranging from low to extremely wet. Notably, in the years with the most significant negative anomalies (drought events), there was a predominance of spatial variability from east to west, with the lowest negative anomalies on the western side and the highest on the eastern side of the region. On the other hand, positive anomalies (wet events) exhibited a predominance of spatial variability from south to north in the region. This study provides valuable insights for the agricultural sector, water resource planning, and environmental management. It underscores the importance of adaptive strategies to address climatic fluctuations and ensure the sustainability of agricultural production in the Western Region of Bahia.

Polyhydroxybutyrate synthesis in Camelina: Towards coproduction of renewable feedstocks for bioplastics and fuels.

Plant-based co-production of polyhydroxyalkanoates (PHAs) and seed oil has the potential to create a viable domestic source of feedstocks for renewable fuels and plastics. PHAs, a class of biodegradable polyesters, can replace conventional plastics in many applications while providing full degradation in all biologically active environments. Here we report the production of the PHA poly[(R)-3-hydroxybutyrate] (PHB) in the seed cytosol of the emerging bioenergy crop Camelina sativa engineered with a bacterial PHB biosynthetic pathway. Two approaches were used: cytosolic localization of all three enzymes of the PHB pathway in the seed, or localization of the first two enzymes of the pathway in the cytosol and anchoring of the third enzyme required for polymerization to the cytosolic face of the endoplasmic reticulum (ER). The ER-targeted approach was found to provide more stable polymer production with PHB levels up to 10.2% of the mature seed weight achieved in seeds with good viability. These results mark a significant step forward towards engineering lines for commercial use. Plant-based PHA production would enable a direct link between low-cost large-scale agricultural production of biodegradable polymers and seed oil with the global plastics and renewable fuels markets.

Framing the Traditional: Counterrevolution and Gender in Mexican Ethnobotanical Research Through the 1970s and 1980s

The concept of traditional knowledge has been widely used in ethnobotanical studies from the 1970s onward. The aftermath of world-scale Green Revolution projects led to the realization that disparities were not bridged between small- and large-scale agricultural producers and between developed and developing countries. It is within this context that from the 1970s, Mexican ethnobotanical researchers began to integrate ecological, social, and political perspectives to promote alternative modalities of agricultural production. Here, ethnobotanists pushed for the revalorization of traditional agricultural knowledge as the main avenue for a more just and responsible agricultural system. However, in implementing this ideological counterrevolution, ethnobotanists constructed their own signification of the traditional, which shaped how it would be accounted for in the following decades. This paper explores the ways in which early ethnobotanical research in Mexico through the 1970s and 1980s imagined, celebrated, and constructed traditional techniques in agriculture as a counter-response to modern agriculture, and with this, how women were framed as secondary actors in a male-dominated narrative. The argument then proposes that these early works were hierarchical and gendered, which complicates celebratory accounts of the countermovement in Mexican ethnobotany and other fields of knowledge. Therefore, this analysis reflects on how the traditional within ethnobotanical research has been constructed under specific contexts, on how this directly shaped gender constructions, and on the latter's implications to the present day.

The Role of Home Gardens in Promoting Biodiversity and Food Security.

Plant genetic resources provide the basis for sustainable agricultural production, adaptation to climate change, and economic development. Many present crop plants are endangered due to extreme environmental conditions induced by climate change or due to the use of a limited selection of plant materials. Changing environmental conditions are a challenge for plant production and food security, emphasizing the urgent need for access to a wider range of plant genetic resources than what are utilized today, for breeding novel crop varieties capable of resilience and adaptation to climate change and other environmental challenges. Besides large-scale agricultural production, it is important to recognize that home gardens have been an integral component of family farming and local food systems for centuries. It is remarkable how home gardens have allowed the adaptation and domestication of plants to extreme or specific ecological conditions, thus contributing to the diversification of cultivated plants. Home gardens can help in reducing hunger and malnutrition and improve food security. In addition, they provide opportunities to broaden the base of cultivated plant materials by harboring underutilized crop plants and crop wild relative species. Crop wild relatives contain a wide range of genetic diversity not available in cultivated crops. Although the importance of home gardens in conserving plant genetic resources is well recognized, there is a risk that local genetic diversity will be lost if traditional plant materials are replaced by high-yielding modern cultivars. This paper provides an overview of home gardens and their present role and future potential in conserving and utilizing plant genetic resources and enhancing food and nutritional security under global challenges.

THE USE OF ORGANIC RESIDUES TO DEVELOP PACKAGING: TESTS IN MOLDED PULP

AbstractAt the end of its life the packaging may become a residue if it is not correctly discarded, becoming a visible component of the waste produced on the planet, with a defined shape and communicating with the world. In the same way, large-scale agricultural production generates organic residues that, although representing fiber-rich materials, are discarded, such as rice husk, coconut husk, wood fibers, among others. To contribute with solutions to minimize the environmental impact of packaging and organic residues, the present study aims at performing preliminary tests of molded pulp packaging manufacturing from organic residues, in a circular economy context. Action research was used as the method to guide the collective construction (project team and partner companies), and the direction of the preliminary tests of packaging to be manufactured. The tests performed on coconut fibers in transfer molding and thermoforming were promising, but they required the addition of binder substances like byproducts from starch, especially in the transfer molding method. Besides using fibers from alternative sources to cellulose, the proposed packaging is potentially compostable and may replace non-renewable material like oil-based polymers.

Carbon Emissions from Agricultural Inputs in China over the Past Three Decades

Global warming has become one of the major threats to the security of human survival, security, and sustainable development. Agricultural production has been widely suspected as one of the main sources of anthropogenic carbon emissions. Analyzing the changing characteristics and influencing factors of agricultural carbon emissions is of great significance for the mitigation of global climate change and the sustainable development in agriculture. Taking China, a large agricultural country, as an example, this study used the empirical model to quantify carbon emissions from agricultural inputs from 1991 to 2019, and analyzed the driving factors using ridge regression. We found that agricultural carbon emissions in China have been on the rise in the past 30 years, but at a markedly slower pace. From 2008 to 2019, the average annual growth rate of agricultural carbon emissions was 1.47%, down significantly from 2.92% between 1991 and 2007. The carbon emissions per unit of planting area showed an overall increasing trend, which grew from 179.35 t ce/km2 to 246.26 t ce/km2, with an average annual growth rate of 1.13%. The carbon emissions per unit of agricultural output mainly showed a decreasing trend, which decreased from 0.52 kg ce/CNY to 0.06 kg ce/CNY, with an average annual rate of change of −7.42%. China’s agricultural carbon emissions were closely related to macro-policies. Fertilizer inputs, agricultural industry structure, and energy use intensity were significantly positively correlated with carbon emission intensity. The degree of urban feedback to rural areas, public investment in agriculture, and large-scale planting were significantly negatively correlated with carbon emission intensity, but the impacts of these factors had a “lag effect”. In order to reduce carbon emissions from agriculture and promote development in green agriculture, we suggest that the government should further increase the degree of urban feedback to rural and public investment in the agricultural sector. In addition, large-scale agricultural production should be encouraged to increase resource efficiency and reduce carbon emissions.

THE IMPACT OF GEOGRAPHICAL ASPECTS FOR AGRICULTURAL DEVELOPMENT IN UZBEKISTAN

The last years of the current century witnessed an agricultural revolution in many developing and developed countries. This was achieved through the overlap of two main factors, the economic and geographical factors that control our daily lives and are considered among the most important factors that contributed major roles in the development of the agricultural sector. The economic aspect represents its role in how providing the necessary resources to increase agricultural output from various types of high-productivity inputs. As for the geographical factor, its role was represented in providing what the aforementioned sector needs in terms of land, water, heat and humidity for girls, given that these factors contribute together to achieving continuity in human life. Therefore, our study came to clarify how to use the factors Economic and geographical in achieving large-scale agricultural production sufficient to meet the need for self-sufficiency with the survival of a surplus for export, with an explanation of the mechanism performed or carried out by each sector with the intention of presenting it to researchers working in the field of agricultural research, especially in rural areas where the latest agricultural technologies are transferred to it with the aim of Adoption by farmers, and in order to achieve this goal, a number of economic factors have been used A number of researchers have prepared studies for this purpose.

RURAL SETTLEMENTS: PROBLEMS AND DEVELOPMENT TRENDS

Abstract. The article notes that in Russia, both the reduction and the grinding of rural settlements are currently taking place. Destructive processes with a low level of technical support for production processes are getting stronger. As a result, the spatial development of agriculture has slowed down in most rural areas, and only in some large settlements in a number of regions with a high bioclimatic potential is there a concentration of social, economic and production potential. However, in rural territories with large agricultural holdings there is a reduction in production activity by small forms of management and the departure of the rural population to the city. Therefore, it is necessary to develop a system of organizational and economic measures aimed at the development of rural territories in order to prevent the disappearance of rural settlements in depressed areas and the development of large-scale agricultural production capable of providing a sufficient volume of agricultural and food products, necessary not only for domestic consumption, but also for export supplies, ensuring food security. First of all, this applies to 15-17 regions of the country with relatively favorable natural and economic conditions for conducting intensive and competitive agriculture, ensuring mainly the country's food security, forming its main commodity resources of agricultural and food products for interregional exchange and export, especially constantly demanded in the global agri-food market.

Agriculture’s Energy: The Trouble with Ethanol in Brazil’s Green Revolution. By Thomas D. Rogers

Today, policymakers celebrate Brazilian sugar-based ethanol as a possible alternative energy source to address our climate crisis. Thomas Rogers brings a unique approach to the history of Brazilian sugar ethanol’s development in his new book, Agriculture’s Energy: The Trouble with Ethanol in Brazil’s Green Revolution. In 1975, Brazil famously transformed ethanol into a major domestic fuel source during the global oil crisis with the creation of the National Alcohol (Ethanol) Program, or Proálcool. As a historian of Brazilian agriculture, Rogers uses the sugar ethanol program to examine the costs of Brazil’s broader twentieth century agro-industrial development strategy. Rogers argues that the Green Revolution’s promise of development using fertilizers and adapted seeds to power large-scale agricultural production underwrote the program’s logic and created adverse consequences that are emblematic of bigger global development problems to this day (4, 8). Divided into two major parts, Rogers meticulously connects the origins of Brazil’s ethanol industry to a particular agricultural development model that evolved over the first half of the twentieth century. Sugar has long been a major agricultural product of Brazil. However, Chapter 1 examines how policymakers focused on “modernizing” the sugar sector with “research, modern techniques, and planning” to shake its backward image, particularly in the drought riddled Northeast, during the first half of the twentieth century (36). Chapter 2 examines how government officials and policymakers institutionalized the use of high-yield seeds and chemical fertilizers in line with Green Revolution logic through various policies and programs to intensify industrial sugar production in the 1960s and early 1970s (66-67). Chapter 3 then succinctly connects these big agriculture policies to Proálcool and its brief tenure as a major fuel alternative until 1990. Large-scale sugar ethanol production, mandated and incentivized by the military government, came to embody the “vision of agriculture-fueled development” that policymakers had built over the previous decades (81).

Recognition of spider mite infestations in jujube trees based on spectral-spatial clustering of hyperspectral images from UAVs.

Spider mite infestations are a serious hazard for jujube trees in China. The use of remote sensing technology to evaluate the health of jujube trees in large-scale intensive agricultural production is an effective means of agricultural control. Hyperspectral remote sensing has a higher spectral resolution and richer spectral information than conventional multispectral remote sensing, which improves the detection of crop pests and diseases. We used hyperspectral remote sensing data from jujube fields infested with spider mite in Hotan Prefecture, Xinjiang to evaluate their use in monitoring this important pest. We fused spectral and spatial information from the hyperspectral images and propose a method of recognizing spider mite infestations of jujube trees. Our method is based on the construction of spectral features, the fusion of spatial information and clustering of these spectral-spatial features. We evaluated the effect of different spectral-spatial features and different clustering methods on the recognition of spider mite in jujube trees. The experimental results showed that the overall accuracy of the method for the recognition of spider mites was >93% and the overall accuracy of the band clustering-density peak clustering model for the recognition of spider mite reached 96.13%. This method can be applied to the control of jujube spider mites in agricultural production.

Deep Learning Analysis of Rice Blast Disease Using Remote Sensing Images

Large-scale agricultural production systems require disease monitoring and pest management on a real-time basis. Monitoring disease phenology is one of the possible ways to save agricultural products from huge yield loss incurred due to diseases. Rice is one of the major food crops across the globe. Leaf blast disease in rice affects its productivity all over the world. Monitoring of leaf blast is essential for strategic and tactical disease management decisions. Conventional methods of large-scale disease monitoring are laborious, time taking, and above all, suffer from inaccuracy. Remote sensing parameters are useful for monitoring diseases and crop health on a large scale. Spectral indices derived from remote sensing data provide characteristic features to distinguish areas between healthy and infected crops facilitating strategic application. Assessment of leaf blast incidence based on land surface temperature (MODIS) and spectral indices NDVI, EVI, NDMI, SAVI, and moisture stress (Sentinel-2) have been used to predict disease patterns. A deep learning-based model is developed to assess the condition of rice blast disease at field scale. The model provided 90.02% training accuracy and 85.33% validation accuracy. The deep learning model on remote sensing images could assess leaf blast occurrence in real time.

Asynchronous Overlapping: An Image Segmentation Method for Key Feature Regions of Plant Phenotyping

Highlights Asynchronous overlapping—an automatic image acquisition method for key feature regions of plant phenotypes. The distance from the plant to the camera can be characterized by the brightness in the grayscale image. Asynchronously acquire daytime RGB and nighttime grayscale images of the plant to use the proposed algorithm. In the test of the plant images, the IoU is 0.8497, reaching a similar level of interactive algorithms. Abstract. Acquiring and describing plant phenotyping is an important proposition in botany and agronomy research. In this study, a computer vision-based asynchronous overlapping segmentation algorithm is proposed for automatic image acquisition of key feature regions of plant phenotyping. Firstly, day-time RGB and night-time grayscale images of infrared light filling the crop body at the same angle are asynchronously obtained using a common closed-circuit television surveillance camera. Then, thresholding and morphological filtering of grayscale images are conducted to extract the initial region contours. With this as a precondition, the algorithm adaptively finds edge paths of key feature regions in daytime RGB images. In the test of the cherry plant image, the intersection over union (IoU) of the algorithm to segment the key feature regions is 0.8497, reaching a similar level of interactive algorithms that require human involvement. The proposed method has low cost, high segmentation accuracy, and strong applicability. The proposed method can independently realize the acquisition of the key feature regions of plant image phenotypes and can be applied to large-scale agricultural production.

Coupling and Coordinating Relationship between Agricultural Eco-Efficiency and Food Security System in China.

Carbon peaking, carbon neutrality goals and food security are the basis of sustainable development, and exploring the coordination relationship between China's agricultural eco-efficiency and food security system has a major significance for the implementation of relevant strategies. This paper is based on collaboration research on the synergistic relationship between agricultural eco-efficiency and food security systems using methods such as entropy weight method, coupling coordination model, spatial autocorrelation model, etc., revealing the evolution-driven mechanism of the coupling coordination degree. This study found that a higher level of coupling coordination always occurs in those areas with high standard farmland construction and large grain production scale, while economically developed areas appear to have a lower overall coordination level limited by endowment constraints and division of labor in development planning. It shows a positive spatial correlation in terms of geographical distance between agricultural eco-efficiency and food security, and the positive spillover effect gradually increases but is not strong overall. China should combine regional resource endowment and development planning, pay attention to the improvement of large-scale and standardized agricultural production, continue to strengthen the development of clean agricultural production, and achieve food security under the constraints of the carbon peaking and carbon neutrality goals.