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Heavy metals and metalloids (HMs) are environmental pollutants, most notably cadmium, lead, arsenic, mercury, and chromium. When HMs accumulate to toxic levels in agricultural soils, these non-biodegradable elements adversely affect crop health and productivity. The toxicity of HMs on crops depends upon factors including crop type, growth condition, and developmental stage; nature of toxicity of the specific elements involved; soil physical and chemical properties; occurrence and bioavailability of HM ions in the soil solution; and soil rhizosphere chemistry. HMs can disrupt the normal structure and function of cellular components and impede various metabolic and developmental processes. This review evaluates: (1) HM contamination in arable lands through agricultural practices, particularly due to chemical fertilizers, pesticides, livestock manures and compost, sewage-sludge-based biosolids, and irrigation; (2) factors affecting the bioavailability of HM elements in the soil solution, and their absorption, translocation, and bioaccumulation in crop plants; (3) mechanisms by which HM elements directly interfere with the physiological, biochemical, and molecular processes in plants, with particular emphasis on the generation of oxidative stress, the inhibition of photosynthetic phosphorylation, enzyme/protein inactivation, genetic modifications, and hormonal deregulation, and indirectly through the inhibition of soil microbial growth, proliferation, and diversity; and (4) visual symptoms of highly toxic non-essential HM elements in plants, with an emphasis on crop plants. Finally, suggestions and recommendations are made to minimize crop losses from suspected HM contamination in agricultural soils.

To find a solution regarding sustainable arable land use pattern in the important grain-producing area during the rapid urbanization process, this study combined agricultural production, locational condition, and ecological protection to determine optimal arable land use. Dongting Lake basin, one of the major grain producing areas in China, was chosen as the study area. The analysis of land use transition, the calculation of arable land barycenter, the landscape indices of arable land patches, and the comprehensive evaluation of arable land quality(productivity, economic location, and ecological condition) were adopted in this study. The results showed that (1) in 1990–2000, the arable land increased by 11.77%, and the transformation between arable land and other land use types actively occurred; in 2000–2010, the arable land decreased by 0.71%, and more ecological area (forestland, grassland, and water area) were disturbed and transferred into arable land; (2) urban expansion of the Changsha-Zhuzhou-Xiangtan city cluster (the major economy center of this area) induced the northward movement of the arable land barycenter; (3) the landscape fragmentation and decentralization degree of arable land patches increased during 1990–2010; (4) potential high-quality arable land is located in the zonal area around Dongting Lake, which contains the Li County, Linli County, Jinshi County, Taoyuan County, Taojiang County, Ningxiang County, Xiangxiang County, Shaoshan County, Miluo County, and Zhuzhou County. The inferior low-quality arable land is located in the northwestern Wuling mountainous area, the southeastern hilly area, and the densely populated big cities and their surrounding area. In the optimized arable land use pattern, the high-quality land should be intensively used, and the low-quality arable land should be reduced used or prohibitively used. What is more, it is necessary to quit the arable land away from the surrounding area of cities appropriately, in order to allow more space for urban expansion. This study could provide guidance for sustainable arable land use by both satisfying the future agricultural production and the local economic development, which can be used for the other major grain-producing areas in this rapid developing country.

The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.

Attainable yields represent the yields that can be achieved under production conditions and are used to determine the exploitable yield gap. However, these yields are constrained by multiple factors, including soil properties that vary across different spatial scales, even within a single field. Thus, the attainable yields should be adjusted to specific soil units. This study uses results from multi-environment cultivar testing trials conducted by COBORU in Poland to estimate winter wheat attainable yields depending on arable land quality classes (ALQCs) and arable land suitability groups (ALSGs). The database comprises 10 years of observations from 18 locations and 156 experiments. The results indicate a clear relationship between the scores assigned to particular ALQCs and ALSGs in 1981. In contrast, the relationship between the average scores assigned to ALQCs within ALSGs was weaker. Attainable yields were estimated directly based on experimental data, using the median and 90th percentile (P0.9) of yields for well represented soil units, and regression analysis between the median and P0.9 and point scores for less-represented soil units. The results could be improved by using a more extensive dataset, particularly for underrepresented and not represented soils. The proposed method may be applied to estimate soil-adjusted attainable yields for other crops whose cultivars are tested by COBORU in multi-environment trials.

Assessing the arable land ecological security ( ALES ) and its quality is not only crucial for ensuring food security but also serves as an essential foundation for achieving sustainable agricultural development. This study focuses on three representative geomorphological areas in Northeast China as research subjects. It employs the bivariate spatial autocorrelation method to explore the spatial correlation between ALES and arable land quality, and uses spatial regression models to investigate the specific impact of ALES on arable land quality. The results show varying degrees of correlation between ALES and arable land quality in different geomorphological areas, and arable land quality changes with variations in ALES . To enhance arable land quality, the western low hilly area should prioritize improving the level of agricultural mechanization, while the central plain area and eastern mountain area mainly rely on the role of the net primary productivity index. When the net primary productivity index increases by 1 gC/m 2 ·a, arable land quality in the areas increases by 190.41 and 196.68, respectively. This study pioneers a geomorphological heterogeneity perspective to develop targeted arable land protection strategies based on regional resource advantages and key ecological constraints affecting arable land quality improvement. The findings provide a scientific foundation for local governments to formulate food security policies and promote sustainable agricultural development.

The most stringent arable land protection system in China has not effectively prevented construction expansion from excessive occupation of arable land. In this paper, the evolutionary game theory is innovatively used to explore why arable land protection policies did not engender the desired outcomes from the perspective of multi-subject behavior. We first analyze the logic of arable land protection behavior of different participants, including the central government, local administrations and farmers. Then, a tripartite evolutionary game model is established to examine how behavioral interaction among subjects affects policy implementation. And parameter analysis is used to identify the influencing factors of subjects’ behavior. Our results show that: First, the ideal strategy combination of (0,1,1) for China's arable land protection cannot be achieved, which indicates that local administrations and farmers will not spontaneously protect arable land in the absence of central government’s supervision. Second, the policy effect in Shanghai has undergone a dynamic process from serious failure (2004–2013), mild failure (2013–2018) to effective control (after 2018). Local administrations and farmers are solely responsible for the deviation of policy implementation. Third, local administrations and farmers are more sensitive to the variations of arable land conversion income, political achievements and economic punishment. Thus, political and financial constraints should be imposed on them to promote the strict implementation of arable land protection policy. This paper enriches the existing study regarding arable land protection policy effect. And it is of great practical importance to regulate the subject’s behavior and facilitate policy implementation.

The current agri-food systems are unable to fulfill global demand and account for 33% of all greenhouse gas emissions. Conventional agriculture cannot produce more food because of the scarcity of arable land, the depletion of freshwater resources, and the increase in greenhouse gas emissions. Thus, it is important to investigate alternate farming methods. Algae farming is a feasible alternative that produces food, feed, and feedstock using wastelands and unconventional agricultural settings such as coastal regions, salt-affected soils, and urban/peri-urban environments. This review focuses on three emerging scenarios. First is seawater, which makes up 97.5% of the water on Earth. However, it is nevertheless used less often than freshwater. Second is a growing trend of people moving from rural to urban regions for improved employment prospects, living standards, and business chances. However, most rural migrants are essentially skilled in agriculture, which limits their applicability in metropolitan environments. The third scenario focuses on excellent crop yields and soil fertility; it is essential to maintain appropriate levels of organic matter and soil structure. In this case, algae have remarkable potential for osmoregulation-based salt tolerance and may provide valuable metabolites when cultivated in brackish or saltwater. Using brackish water, treated wastewater, and saltwater, algal culture systems may be established in arid/semi-arid, urban/peri-urban, and coastal areas to fulfill the increasing need for food, feed, and industrial feedstocks. It may also provide migrants from rural areas with work possibilities, which would allay environmental footprints.

Exploring the spatial relationship and ecological compensation mechanism of each function of arable land in poor mountainous areas is important to promote rural revitalization and enhance arable land protection. Taking the mountainous region of Western Hubei (MRWH) as an example, this study quantified the “three living” functions of arable land and its secondary functions. Using the root mean square deviation method to calculate the trade-off index, a quantitative method can more scientifically reflect the trade-off relationship between arable land functions and measure the overall ecological compensation. Studies have shown that (1) the value of the production function exhibits a growing and subsequently a falling trend, whereas the value of living function and ecological function exhibits an increasing trend over time, with an average functional value of 5310, 220 and 6496 million yuan, respectively. The spatial pattern of the “three living” functional values decreases from west to east. Among them, water conservation and soil conservation function values show a high distribution in the south and low in the north, gas purification and agricultural pollution functional values show a scattered spatial pattern, and the value of other functions shows an increasing trend from southeast to northwest; (2) among the primary functions, the trade-off between production and ecological functions is the strongest, decreasing, and then increasing over time, with an average trade-off index of 0.89. Among the secondary functions, there is the most obvious trade-off between the food supply function and the five ecological functions, which requires coordination; (3) overall, the total amount of ecological compensation has shown an upward trend, with priority areas for level I ecological compensation increasing year by year. Optimized compensation zones and potential compensation zones are concentrated in the northwest, ecological balance zones are located in the central part, and optimized development compensation zones and key development compensation zones are located in the southeast. According to the research, MRWH is oriented to ecological function, followed by the production function, supplemented by the living function. Green agriculture should be vigorously developed and ecological function space should be compressed by strictly limiting the excessive expansion of production activities. Promoting the improvement of production function through ecological function, while exploring the potential value of living function. Ecological compensation in strict accordance with the priority of ecological compensation, zoning. Realizing cross-regional cooperation, low compensation areas drive high compensation areas to achieve sustainable development of arable land.

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection.