Farmland Quality Research Articles

Mainstream Planting Systems Influence Spatiotemporal Variations in the Soil Quality of Watershed Sloping Farmland

Mainstream planting systems of watershed sloping farmland span the globe and are closely related to the variations in the soil quality of watershed sloping farmland. However, little information is available about how mainstream planting systems influence spatiotemporal variations in the soil quality of watershed sloping farmland. The soil of 0–20 cm was collected at fixed points in three mainstream planting systems (a low-altitude citrus orchard system, a mid-altitude double-cropping system, and a high-altitude single-cropping system) at a fixed time each year for 15 years in a typical agricultural watershed of the Three Gorges Reservoir area of China. Fourteen physicochemical properties of the sampled soil were measured. We found that (1) the soil quality indexes of the citrus orchard system, double-cropping system, and single-cropping system decreased from 0.75, 0.71, and 0.67 in 2004 to 0.68, 0.57, and 0.55 in 2019, respectively; (2) the order of the six master control factors influencing soil quality was sand content > bulk density > total nitrogen > clay content > pH > total phosphorus in the citrus orchard system, sand content > bulk density > clay content > pH > total phosphorus > total nitrogen in the double-cropping system, and sand content > clay content > total phosphorus > pH > bulk density > total nitrogen in the single-cropping system; and (3) the total effects of soil erosion and fertilization on soil quality were −0.496 and −0.308 in the citrus orchard system, −1.254 and 0.371 in the double-cropping system, and −0.844 and 0.013 in the single-cropping system, respectively. We suggest that the three mainstream planting systems influence soil quality through variations in their master control factors caused primarily by soil erosion and secondarily by fertilization. These findings are important for controlling soil degradation through controlling soil erosion and rational fertilization in watersheds.

A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model’s instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture.

Spatial Differentiation and Influencing Factors of Available Potassium in Cultivated Soil in Mountainous Areas of Northwestern Hubei Province, China

This research was conducted based on 701 soil sampling points in cultivated land (0–20 cm) in Shiyan, a mountainous area in northwest Hubei Province, China. The methods of classical statistics, geostatistics, and geodetector were used to explore the spatial differentiation characteristics and influencing factors of soil available potassium (AK) in cultivated land in Shiyan. The results showed that the soil AK content in the study area ranged from 17.00 to 350.00 mg/kg, with an average value of 118.95 mg/kg and a coefficient of variation of 54.06%, exhibiting moderate variability. The spatial structure was well fitted by a spherical model; the block gold effect was 0.027, indicating strong spatial autocorrelation; and spatial variation was mainly caused by structural factors. The spatial differentiation characteristics of the soil AK content are obvious; overall, there was a spatial distribution pattern of high in the northeast and low in the southwest. The factor detection results show that soil pH plays a dominant role in the spatial variation in soil AK in the study area, followed by parent material and annual average temperature. The interaction detection results show that each environmental factor exhibits non-linear or dual factor enhancement between factors, with soil pH slope ranking first in explanatory power. The explanatory power of the interaction between soil pH, parent materials, annual average temperature, and other factors dominates. In the process of the fine management of soil AK in cultivated land in the study area, when considering the influence of dominant factors, the impact of the interaction of various factors on the spatial variation in soil AK should also be taken into account. This study could provide a theoretical reference for improving the soil and farmland improvement, improving farmland quality in this area.

Adoption of Fertilizer-Reduction and Efficiency-Increasing Technologies in China: The Role of Information Acquisition Ability

Reducing fertilizer use and increasing its efficiency will improve the quality of farmland and resource conservation. These are necessary steps to achieving green development in agriculture. Nevertheless, fertilizer-reduction and efficiency-increasing technologies (FREITs) remain limited. To improve the situation, 538 farmers in Jiangsu and Hubei Provinces were surveyed with the goal of measuring the information acquisition ability (IAA) of farmers using an Item Response Theory (IRT) model. A model of improved technology selection was employed in conjunction with an IV-probit model to examine the impacts of IAA on farmers’ adoption of FREITs. The results showed that 34.76% of the surveyed farmers had adopted FREITs, with 12.45% and 26.02% having adopted Soil Testing and Formula Fertilization Technology (STFFT) and Organic Fertilizer Replacement Technology (OFRT), respectively. Second, farmers who used more information access channels had greater IAA, which significantly improved their adoption of FREITs. Third, participation in technical training and an increased degree of technical understanding increased the probability of farmers adopting FREITs. The results remained robust after accounting for endogeneity and correlation. Consequently, enhancing farmers’ IAA, organizing technical training, and improving technical publicity will promote the adoption of FREITs.

Nitrous oxide (N2O) emission characteristics of farmland (rice, wheat, and maize) based on different fertilization strategies.

Fertilizer application is the basis for ensuring high yield, high quality and high efficiency of farmland. In order to meet the demand for food with the increasing of population, the application of nitrogen fertilizer will be further increased, which will lead to problems such as N2O emission and nitrogen loss from farmland, it will easily deteriorate the soil and water environment of farmland, and will not conducive to the sustainable development of modern agriculture. However, optimizing fertilizer management is an important way to solve this problem. While, due to the differences in the study conditions (geographical location, environmental conditions, experimental design, etc.), leading to the results obtained in the literatures about the N2O emission with different nitrogen fertilizer application strategies have significant differences, which requiring further comprehensive quantitative analysis. Therefore, we analyzed the effects of nitrogen fertilizer application strategies (different fertilizer types and fertilizer application rates) on N2O emissions from the fields (rice, wheat and maize) based on the Meta-analysis using 67 published studies (including 1289 comparisons). For the three crops, inorganic fertilizer application significantly increased on-farm N2O emissions by 19.7-101.05% for all three; and organic fertilizer increased N2O emissions by 28.16% and 69.44% in wheat and maize fields, respectively, but the application of organic fertilizer in rice field significantly reduced N2O emissions by 58.1%. The results showed that overall, the application of inorganic fertilizers resulted in higher N2O emissions from farmland compared to the application of organic fertilizers. In addition, in this study, the average annual temperature, annual precipitation, soil type, pH, soil total nitrogen content, soil organic carbon content, and soil bulk weight were used as the main influencing factors of N2O emission under nitrogen fertilizer strategies, and the results of the study can provide a reference for the development of integrated management measures to control greenhouse gas emissions from agricultural soils.

Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil > yellow moist soil > sandy soil > silt soil > mulching helilu soil> loessal soil> loamy soil > slope loessal soil > arenosol > tillage leaching gray cinnamon soil > calcareous gray cinnamon soil > red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

Farmland quality assessment using deep learning and UAVs

The transformation of agricultural landscapes due to the intensification of farming practices has placed a major threat on habitat and species diversity. Stopping the loss of biodiversity is addressed in various international treaties and adequate strategies are required to assess the initial status of biodiversity and to measure the success of policies. While traditional methods of field-based biodiversity monitoring are time consuming, costly, and highly surveyor-dependent, recent advances in remote sensing and machine learning enable a systematic, more general and cost-effective monitoring. Accordingly, this study investigated whether large-scale biodiversity monitoring campaigns such as the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) could be supported by the use of Unmanned Aerial Vehicle (UAV) based remote sensing and Convolutional Neural Networks (CNNs). For this purpose, the Structural Nature Value (SNV) indicator was proposed, which allows an intuitive estimation of the species richness of landscape sections. Subsequently, a CNN was trained to directly predict the SNV. For the accuracy assessment, a survey was conducted in which participants were asked to assess the SNV for various landscape patches, both to obtain an independent test set, and to evaluate the intuitiveness of the concept. The CNN achieved a mean weighted F1-score of 0.81 with an overall accuracy of 81 %. It was shown that the CNN can provide promising results to not only aid large-scale biodiversity monitorings, but to enhance the quality of their results through the automated analysis of fine-resolution UAV imagery.

2020年京津冀1 km格网沟渠密度数据集

As important ecological corridors in farmland ecosystems, ditches are not only important for collecting water, holding water as flowing channels, but also valuable for biodiversity conservation as various wetland ecological systems. They play an important role in purifying water quality and maintaining biodiversity. Ditch density is an important indicator of ditch structure in a certain area, directly revealing ditch configuration and farmland irrigation and drainage capacity. Taking the national land change survey data in 2020 as the data source, we extracted the skeleton lines of irrigation canals and ditches by using software, and calculated the density of 1km grid ditches in Beijing, Tianjin and Hebei in 2020 by using km grid production tools. This dataset can be used for research on rural planning, farmland irrigation and drainage convenience, farmland quality evaluation, farmland assets evaluation, etc.

Investigating soil quality in cold highland agricultural fields with different soil types

ABSTRACT To evaluate the soil quality of alpine farmland and elucidate the key factors influencing productivity levels under the current agricultural paradigm, we conducted an extensive collection of soil samples. In total, 234 soil samples (0–20 cm) were collected from Menyuan County, the heartland of the southern slope of the Qilian Mountains. These samples represented four predominant soil types, including cold calcic soil, luvic chernozem, calcareous meadow soil, and calcareous gray cinnamon soil. Moreover, by employing principal component analysis, we established a minimum data set (MDS) that encapsulated the essential parameters for the comprehensive evaluation of the soil quality of farmland in the study area. Upon evaluating the soil quality index, the rankings were as follows: luvic chernozem (0.63) > calcareous gray cinnamon soil (0.58) > calcareous meadow soil (0.58) > cold calcic soil (0.55). Overall, the results highlighted that the significant impact of soil total nitrogen (TN), available potassium (AK), and soil water content (SWC) on soil quality within the study area. Therefore, in the context of land management practices, careful attention should be paid to the management of soil TN, AK content, and SWC to avoid the degradation of cultivated land into abandoned fields.

Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.

Source Analysis and Contribution Estimation of Heavy Metal Contamination in Agricultural Soils in an Industrial Town in the Yangtze River Delta, China

Farmland soil quality is a crucial determinant for agricultural productivity, food safety, and human well-being. Among the various contaminants, heavy metals have emerged as pervasive factors significantly impacting farmland quality, attracting widespread societal concern. In this study, we systematically applied multivariate statistical analysis, geostatistical methods, and the positive matrix factorization (PMF) source apportionment technique to elucidate the sources and contributions of eight heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, Ni) in farmland soils within an industrialized town. Our findings reveal that Cd, Hg, Pb, and Zn exhibit pollution or enrichment in farmland soils compared to natural background levels, with Hg and Cd surpassing 164.3% and 50.2%, respectively. Notably, Zn demonstrates discernible point-source pollution. Source apportionment results highlight industrial point sources, coal combustion, and agriculture as the primary anthropogenic contributors to heavy metal contamination, with zinc-plating enterprises being the predominant industrial point sources. Addressing the specific issue of point-source pollution from Zn in industrial activities, further analysis establishes a correlation between soil Zn content and the distance from zinc-plating enterprises. Utilizing an atmospheric transport model, we observe that the impact of industrial activities on soil Zn is limited when the distance exceeds 1.5 km, emphasizing the importance of monitoring Zn pollution within areas less than 1.5 km. This study pioneers a progressive source apportionment approach, considering the origins of different heavy metals, pollution levels, distance factors, and the cost-effectiveness of environmental measures. The insights gained provide scientifically sound strategies for future decision making in environmental protection.

Impact of farmland quality on farmland rents and its constraints: Evidence from plots in six Chinese provinces

Farmland rents are related to farmland transfer efficiency and a key factor affecting the development of large-scale agricultural operations and food security. However, rents in some regions with poor-quality farmland are higher than those in high-quality regions, especially in China, which could distort farmland market development. To clarify the key factors driving this relationship between poor quality and high rents for farmlands, this study constructs a theoretical framework of the impact of farmland quality on rent and its limiting factors. An empirical analysis is performed based on 5176 farmland-inflow plots surveyed in six Chinese provinces from the “Farmland Assets Inventory Database” of the Ministry of Natural Resources in 2020. The effect of farmland quality on rents and its constraints are investigated using a moderating effect model by introducing three interaction variables: of market proximity, type of crop planted, and economic environment. The results reveal that, although overall farmland quality still contributes positively to rents, some farmlands in China have developed poor-quality, high-rent patterns. Specifically, the positive effect of farmland quality on rents decreases for three key reasons: (1) increased transportation costs as the distance between plots and agricultural sales markets increases; (2) increased production returns when non-grain crops are planted; and (3) increased production costs as the economic development level of the region where plot is located increases. These effects vary across topographic regions. In plains areas, farmland quality’s impact on rents is limited by market distance and type of crop planted, whereas in mountainous areas, this is mainly limited by type of crop planted. Ultimately, government should regulate the deviation of farmland rents in China by perfecting farmland use control and establishing a monitoring mechanism for rents.

Do county financial marketization reforms promote food total factor productivity growth?: a mechanistic analysis of the factors quality of land, labor, and capital

Improving food total factor productivity is a necessary way to break the double constraint of resources and environment, and promote the transformation of the food production system and the realization of the sustainable development goal of zero hunger. Based on the panel data of 729 counties in China from 2010 to 2019, this paper analyzes the effect of county financial marketization reform on food total factor productivity by using a two-way fixed effects model, focusing on the mechanism of the quality of factors such as land, labor and capital. The results show that county financial marketization reform promoted food total factor productivity growth by promoting technical progress, while technical efficiency did not play a significant role. Heterogeneity analysis shows that the effect of county financial marketization reform on grain total factor productivity is not significantly different in eastern and central China. There is a significant difference in the western region, where county financial market reform hindered the growth of grain total factor productivity. In terms of different functional areas of grain production, the facilitating effect is only played in the main grain production area, and the inhibiting effect is played in the main grain marketing area. Mechanistic analysis shows that the county financial marketing reform promoted the growth of food total factor productivity by improving the quality of labor and land, while the quality of agricultural capital has a masking effect. On this basis, it is necessary for the government to implement differentiated financial market-oriented reform strategies, and to guide and encourage county financial institutions to provide financial services to improve the quality of agricultural labor and farmland through tax incentives and loan interest subsidies.

An ensemble framework for farmland quality evaluation based on machine learning and physical models

Farmland quality (FQ) evaluation is crucial to curb agricultural land's “non-grain” behavior and promote ecological nitrogen trade-off in North China. However, a promising approach to obtain the verified spatial distribution of nitrogen emissions remains to be developed, making it difficult to achieve the precise FQ estimation. Facing this issue, we present a Machine Learning (ML) - Nitrogen Export Verification (NEV) ensemble framework for the precise evaluation of FQ, taking the Beijing-Tianjin-Hebei 200 km traffic zone (zone) as the case. This was done by employing physical models for the precisely spatial estimation of Nitrogen Export (NE) values and then using ML methods to compute the spatial distribution of FQ using the Farmland Quality Evaluation System (FQES) indicators. We found: (1) the ML - NEV framework showed promising results, as the relative error of the NEV method was lower than 5.25 %, and the Determination coefficient of the ML method in FQ evaluation was higher than 0.84; (2) the FQ results within the zone were mainly good-quality areas (~47.25 % and primarily concentrated in the southwest-northeast regions) with improvement significance, with Fractal Dimension, NE values, and unbalanced Irrigation or Drainage Capabilities serving as the primary driving factors. Our results would be helpful in offering decision support for improving FQ based on refined grids, benefiting to Agribusiness Revitalization Plans (i.e., safeguarding grain yield, activating agribusiness development, Etc.) in developing countries.

The impacts of farmer ageing on farmland ecological restoration technology adoption: Empirical evidence from rural China

Preserving and enhancing the quality of farmland holds significant importance for ensuring food security and promoting sustainable agricultural development. Drawing on survey data collected from 2600 households engaged in rice cultivation across 13 cities in Jiangsu Province, China, this study examines the impacts of farmer ageing on farmland ecological restoration technology adoption. The analysis employs the Probit model and instrumental variable approach to investigate this relationship. The study reveals the following key findings: (1) Farmer ageing hinders farmland ecological restoration technology adoption. (2) The influence of farmer ageing on farmland ecological restoration technology adoption varies across different land tenure arrangements, with farmers demonstrating a lower likelihood of adopting ecological restoration technology for rented farmland compared to their own farmland. (3) Agricultural training and agricultural social services have the potential to mitigate the negative effects of farmer ageing on farmland ecological restoration technology adoption. These research findings offer valuable insights for the implementation of farmland ecological restoration technology within the global context of population ageing.

Integrated Mining and Reclamation Practices Enhance Sustainable Land Use: A Case Study in Huainan Coalfield, China

In the coal-grain composite area (CGCA) of eastern China with a high groundwater table (HGT), underground coal mining subsidence has caused extensive submergence of farmland, posing a significant threat to regional food security. Currently, land reclamation techniques in mining subsidence areas primarily focus on post-mining reclamation (PMR) of stable subsidence land with a low reclamation rate. This study investigated the application of concurrent mining and reclamation (CMR) technology for unstable subsidence land in a representative HGT mining area, namely the Guqiao Coal Mine in the Huainan Coalfield. Firstly, mining subsidence prediction and geographic information technology were employed to simulate the spatio-temporal evolution of dynamic mining subsidence, taking into consideration the mining plan. Subsequently, phased reclamation parameters were quantitatively designed by integrating the dynamic mining subsidence and surface reclamation measures. Lastly, scenario simulations were conducted to discuss the effectiveness of CMR in comparison with non-reclamation (NR) and PMR. Additionally, reclamation and ecological restoration strategies for coal mining subsidence areas with comprehensive governance modes were proposed. The findings indicated that mining activities have led to a reduction in both the quantity and quality of original farmland, with 70% of the farmland submerged and rendered uncultivable. In contrast to PMR, which achieved a reclamation rate of 29%, CMR can significantly increase the farmland reclamation rate to 69% while also prolonging the service life of farmland. This study provides theoretical support and technical references for promoting sustainable mining practices, protecting farmland, and facilitating the high-quality development of coal resource-based cities.

Effects of Deep Vertical Rotary Tillage Management Methods on Soil Quality in Saline Cotton Fields in Southern Xinjiang

A long-term high-saline soil environment will limit the improvement of soil quality and cotton yield. Modified tillage management measures can improve soil quality, and the establishment of a soil quality evaluation system will facilitate evaluation of the soil quality and land production potential in southern Xinjiang. The objective of this study was to determine the effects of different tillage management methods on soil quality in saline cotton fields in southern Xinjiang. A three-year experiment was conducted in Tumushuke, Xinjiang, with different deep vertical rotary tillage depths (DTM20, 20 cm; DTM40, 40 cm; DTM60, 60 cm) and conventional tillage (CTM, 20 cm). The soil quality index (SQI) under different tillage management methods was established by using the full dataset (TDS) with a scoring function for eight indicators, including physicochemical properties of the soil from 0 to 60 cm, to evaluate its impact on the soil quality of the saline farmland in southern Xinjiang. The results of the study showed that deep vertical rotary tillage management can effectively optimize soil structure; reduce soil bulk density (BD), soil solution conductivity (EC), and pH; and promote the accumulation of soil organic carbon (SOC) and total nitrogen (TN) in the soil. However, the average diameter of soil water-stable aggregates (MWD) in a 0–60 cm layer becomes smaller with an increasing depth of tillage. This does not reduce crop yields but does promote soil saline leaching. In addition, the significant linear relationship (p < 0.001) between seed cotton yield and soil quality indicated that improving soil quality was favorable for crop yield. The principal component analysis revealed BD, MWD, pH, and EC as limiting sensitive indicators for seed cotton yield, while SOC and TN were positive sensitive indicators. The soil quality index (SQI) values of DT40 and DTM60 were significantly higher than that of CTM by 11.02% and 15.27%, respectively. Overall, the results show that DTM60 is the most suitable tillage strategy to improve soil quality and seed cotton yield in this area, and this approach will provide a reliable theoretical basis for the improvement of saline farmland.

Ways to improve land assessment methods in Azerbaijan

Motives: The article examines various approaches to improving the assessment of agricultural land in Azerbaijan. The quality, potential productivity, and normative price (value in monetary terms) of farmland need to be determined to increase production and competitiveness of agricultural products, to establish favourable land relations that meet modern requirements, and to promote efficient land use. The existing methods of assessing the quality and economic value of farmland for the needs of the land cadastre and determining the normative prices of land do not fully correspond to reality. Different and often contradictory approaches to land assessment have been proposed.Aim: The aim of this study was to optimize the methods of assessing the quality and economic value of agricultural land, determining the normative prices of land, and evaluating potential land productivity in view of modern requirements.Results: Improvements in land valuation will promote reliable assessments of agricultural development and the establishment of efficient farming systems; they will improve agricultural land-use efficiency and reliable determination of normative prices in market transactions involving agricultural property. These methods can be widely used to plan agricultural production, evaluate the operations of various types of farms, improve remuneration policies in the farming sector, determine the optimal scope of farming operations in view of natural conditions and economic factors, and resolve other issues to promote the development of agricultural production.

Place Attachment, Self-Efficacy, and Farmers’ Farmland Quality Protection Behavior: Evidence from China

Farmland pollution severely threatens humanity’s sustainable development. Exploring farmland quality protection behavior (FQPB) from the farmers’ perspective is considered one of the best ways to solve the farmland pollution problem. This study develops a theoretical framework for farmers’ FQPB from the perspectives of place attachment (consisting of place identity, dependency, and affection) and self-efficacy. We conducted a primary survey of 412 corn farmers from the northwestern Chinese province of Gansu and empirically examined the effects of place attachment and self-efficacy on farmers’ FQPB and verified the moderating effects that self-efficacy exerts on the influence of place attachment on FQPB by using hierarchical regression and propensity score matching models. The results indicate that: (1) among the three dimensions of place attachment, farmers with stronger place identity and place affection are more likely to implement FQPB; conversely, farmers who exhibit stronger place dependency are less likely to engage FQPB; and (2) self-efficacy not only effectively promotes farmers’ FQPB but also has an enhancing effect on the influence of place identity and place affection on FQPB. Our results suggest that policymakers should encourage farmers to maintain a place’s image and guide farmers to participate in place construction; thus, farmers’ place identity and place affection can be fostered. Meanwhile, the government should diversify the income sources of farmers to reduce their dependency on a single source. The finding that self-efficacy effectively promotes FQPB also implied that the formulation of farmland quality protection policies should shift from traditional command-based policies to participatory approaches, utilizing the initiative of farmers to enhance the policy’s effectiveness, which can not only promote farmers’ FQPB through self-efficacy but also strengthen the positive influence of place identity and place affection on FQPB.

Spatial Characteristics of Transfer Plots and Conservation Tillage Technology Adoption: Evidence from a Survey of Four Provinces in China

Degradation in farmland quality owing to overuse emphasizes the current need for the adoption of protective technologies to ensure food security and sustainable resource utilization. This study employs plot survey data from Heilongjiang, Henan, Zhejiang, and Sichuan provinces in China to investigate how the spatial attributes of transferred plots influence the current adoption of farmland protection methods, such as deep tillage and straw-returning. Findings reveal that larger or interconnected transferred plots significantly increase the likelihood of farmers adopting conservation tillage technologies. However, the influence of the plot’s location on technology adoption varies among different plots. As the farmland transfer market expands, the spatial features of these plots emerge as critical determinants in the use of protective technologies. This underscores the pressing need for an integrated farmland transfer trading system and strengthened policy measures promoting land consolidation to foster widespread adoption of these conservation strategies.