Farmland Management Research Articles

Differences in microbial diversity and environmental factors in ploughing-treated tobacco soil.

During agricultural production, plowing affects the existing traits of the planted soil, including environmental factors (physicochemical properties and soil enzymatic activity) and microbial community, but whether deep tillage and conventional tillage cause differences in soil microecology are unknown. In this study, the 16S rRNA high-throughput sequencing technology was combined with soil environmental factor detection to analyze the differences in microbial diversity of smokey soils at different depths. As a result, the composition and structure of microbial community varied in different soil depth after plowing. Two dominant phyla, Actinobacteria and Acidobacteria, have varied a lot between the deep-plowing treatment HS3 (the sample in 10–20 cm depth after deep-plowing treatment) sample and the conventional tillage HC3 (treatment the sample in 10–20 cm depth after conventional tillage) sample. The abundance of Actinobacteria has increased significantly, while the abundance of Acidobacteria has decreased significantly. Moreover, deep tillage increased the activity of sucrase (S-SC) and nitrate reductase (NR) in samples with soil depth below 20 cm. In summary, deep tillage disturbed spatial microbial diversity and environmental factors significantly. This would provide new guidance for improving farmland management strategies, optimizing the activation methods of soil layers, further improving crop planting soil, and increasing crop yield.

Proportional allocation with soil depth improved mapping soil organic carbon stocks

Soil organic carbon (SOC) is vital to the assessment of land quality, management of farmland and ecological environment, and carbon cycle. A more accurate spatial prediction of multilayer soil organic carbon density (SOCD) can contribute to a better interpretation of the changes in multilayer SOC stocks and carbon dynamics. However, previous mapping techniques still have limitations, such as ignoring the relationship of profile depths, not further taking advantage of vertical distribution and surface categorical information. In addition, it is unclear whether it is better to model each depth interval of SOC separately or to model the total layer and then allocate it. Here, we propose two new methods based on the proportional allocation of soil depth for multilayer mapping: vertical log-ratio method (VLR) of SOCD by applying the percentage of SOCD data and isometric log-ratio (ILR) transformation, and vertical distribution method (VD) of SOCD by considering different land-use types. We compared five methods, including the two new methods, the exponential and equal-area spline functions, and independent modeling without depth information. We combined these five methods with the generalized linear model (GLM) and random forest (RF) to produce predictions of the Sanjiang Plain, northeastern China. The results demonstrated that SOCD did not always decrease with increasing soil depth, and classification of SOCD vertical distribution features needs to be considered by combining with soil depths. For accuracy assessment, the exponential mode with both GLM and RF over-calculated the predicted values and performed poorly, indicating that the blind use of depth information increased the prediction error. The spline function prediction was scarcely better than that of independent modeling. The proportional allocation methods performed better than other separate modeling methods for accuracy and interpretability with GLM or RF, especially for the middle and surface layers. The GLM generated more aggregated predictions than the RF, losing the distribution pattern of the original data. Therefore, we recommend RF combined with proportional allocation methods for spatial SOCD prediction in large-scale study areas. We calculated the SOC stocks in the Sanjiang Plain using our new methods, which were more reasonable compared with those of previous studies and had the advantages of in-depth information, environmental variable selection, and model optimization. Our findings provide not only other perspectives for SOCD mapping, with more fully integrated depth information and more accurate assessment of multilayer SOC stocks, but also provide guidance for the evaluation of land quality, farmland, and ecological environmental management.

The Environmental and Socio-Economic Effect of Farmland Management Right Transfer in China: A Systematic Review

Small and fragmented arable land is a key challenge for small-scale agricultural countries, resulting in low labor productivity, ecological damage, and inefficient land use. To cope with this challenge, the farmland transfer (FLT) policy is implemented to establish modern agriculture based on specialization and scale enlargement. Despite the rising body of literature, an overview of the effect of this policy is lacked. This paper aimed to examine the multi-dimensional effects of FLT in China. A systematic search of the Web of Science and Scopus databases revealed 26 full-text peer-reviewed articles. We found that FLT had both positive and negative effects, and its effects were mainly mediated by intermediary factors. Non-contingent FLT and post-FLT actions were responsible for the adverse effects of FLT. In the literature, the influence of FLT on the environment is the most concerning issue. The results also show that the existing evidence on the effect of FLT is insufficient; therefore, this paper proposes to further exploration of the multi-dimensional effects of FLT, institutional feedback, and trade-offs. While FLT offers the potential to address socio-economic and environmental challenges, this study suggests that an institutional framework that takes into account spatial and temporal aspects, land-use, market systems, and household conditions is needed to promote favorable development, and mitigate potential land market problems and ensure sustainable development. Despite significant limitations remain, the literature on the socio-economic and environmental effects of FLT is increasing.

Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches

The vertical leaf nitrogen (N) distribution in summer maize is believed to be an important adaptive reaction to crop physiology and ecosystem functions. Therefore, it is of great significance to understand the vertical characteristics of leaf N concentration (LNC) across summer maize canopies for crop growth and production. However, the effect of vertical canopy position on LNC and subsequently leaf hyperspectral characteristics of the entire spectrum (350–2500 nm) for summer maize is poorly understood. The purpose of this work was to quantitatively study the effects of the N nutrition on vertical distribution of LNC, identify the sensitive layer and effective wavelength of leaves, and establish a in situ leaf spectrum monitoring model considering vertical distribution of LNC. Six N field trials were conducted for four consecutive years (2017–2020). In addition, the data of 30 farmers' conventional farmland management in 2020 were collected to check the robustness of the constructed optimal estimation model for LNC prediction. Leaf spectral measurements together with LNC were studied at three vertical leaf positions on the crop stem: upper, middle and lower. Spectral reflectance was processed by continuous wavelet transform (CWT); partial least square (PLS) was used to analyze the relationships between LNC of different layers and spectral reflectance. Field sampling indicated that LNC had a vertical distribution pattern and an evident decline from the upper to lower layer. CWT technique can significantly increase the prediction accuracy of LNC at different layers, and the best decomposition scale is CWT-1. The CWT-1-PLS model for predicting vertical LNC distribution had achieved relatively higher accuracy than that based on the full range of the raw hyperspectral reflectance (R), the LNC determination coefficient (R2val) of the validation dataset was 0.832, 0.857 and 0.811 for the upper, middle and lower layer, and the relative percentage deviations (RPDval) were 2.444, 2.432, and 2.181, respectively. Eventually, ten bands were selected as the effective wavelengths for predicting the vertical LNC distribution in the upper, middle and lower layer, respectively. Newly developed CWT-1-PLS model using the effective wavelengths for LNC prediction in different layers also performed well (RPDval>2.0) based on the field experiments validation. Moreover, the validation at the farmers’ fields also showed fine precision for upper (RPDval=2.012), middle (RPDval=2.137) and lower (RPDval=1.881) layer LNC prediction. These results are of great significance for the study of summer maize leaf reflectance modelling, especially for the studies of integrating hyperspectral measurements and leaf traits data.

Heterogeneous Preferences for Selecting Attributes of Farmland Management Right Mortgages in Western China: A Demand Perspective

Farmland management right mortgages (FMRMs) are emerging land financing products in China. However, the development of FMRMs and farmers’ demands for them are poorly understood. This study applied an in-the-field choice experiment of 1815 farmers, conducted in China, to examine farmers’ demands for FMRMs and explore their heterogeneous preferences regarding the attributes of FMRMs. Results from the random parameters logit model suggest that farmers are interest-rate sensitive and willing to pay for FMRM products that use farmland management rights as the sole collateral, enabling amortization, and without insurance and guarantee requirements. Results from the latent class model further suggest that guarantee takers strongly prefer lump-sum repayments and are more inclined to select insurance and guarantees relative to their counterparts, while guarantee averters strongly prefer to pay off loans with amortization and have positive propensities to purchase insurance. Moreover, female farmers with high education levels, entity identities, and loan experiences are more willing to pay off their loans at once and purchase insurance. Our findings provide insight into the roles of financial product attributes and borrowers’ characteristics in their demand for FMRMs. It may facilitate the design of optimal portfolios and adoption incentives for land mortgage products.

The effect of land titling policy on farm size: evidence from China

ABSTRACT This study utilizes regression discontinuity design to examine the impact of the land titling program on farm size in China between 2009 and 2016. Results indicate that the land titling policy effect upsizes farm by around 50% and increases the transaction of management rights in farmland by around 4% points in China for 2015 national survey. Using data from the 20th century, it shows private ownership historically had lowered farm size. Today, given ownership is stable, farmland management rights provide incentive structure for farmers to participate into farmland lease market and expand their farm size.

Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index.

Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.

Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4

Effective maize and weed detection plays an important role in farmland management, which helps to improve yield and save herbicide resources. Due to their convenience and high resolution, Unmanned Aerial Vehicles (UAVs) are widely used in weed detection. However, there are some challenging problems in weed detection: (i) the cost of labeling is high, the image contains many plants, and annotation of the image is time-consuming and labor-intensive; (ii) the number of maize is much larger than the number of weed in the field, and this imbalance of samples leads to decreased recognition accuracy; and (iii) maize and weed have similar colors, textures, and shapes, which are difficult to identify when an UAV flies at a comparatively high altitude. To solve these problems, we propose a new weed detection framework in this paper. First, to balance the samples and reduce the cost of labeling, a lightweight model YOLOv4-Tiny was exploited to detect and mask the maize rows so that it was only necessary to label weeds on the masked image. Second, the improved YOLOv4 was used as a weed detection model. We introduced the Meta-ACON activation function, added the Convolutional Block Attention Module (CBAM), and replaced the Non-Maximum Suppression (NMS) with Soft Non-Maximum Suppression (Soft-NMS). Moreover, the distributions and counts of weeds were analyzed, which was useful for variable herbicide spraying. The results showed that the total number of labels for 1000 images decrease by half, from 33,572 to 17,126. The improved YOLOv4 had a mean average precision (mAP) of 86.89%.

Existing evidence on the impacts of within-field farmland management practices on the flux of greenhouse gases from arable cropland in temperate regions: a systematic map

BackgroundReducing the emissions of greenhouse gases (GHGs) is vital for mitigating climate change and meeting commitments to international agreements such as the COP 21 Paris Agreement of 2015. Agriculture is reported to account for approximately 11 percent of total global GHG emissions such that: the agricultural sector has an important role to play in meeting climate change mitigation objectives. However, there is currently little consensus on how farm management and interventions, along with interactions with in-field variability, such as soil type, affect the production and assimilation of GHGs in arable crop lands. Practical recommendations for farmers are often vague or generalised, and models (e.g. on the amount of nitrogen fertiliser applied) are used despite limited understanding of the influence of local conditions, such as the importance of soil type. Here, we report the findings of a systematic map of the evidence relating to the impact on GHG flux from the in-field management of arable land in temperate regions.MethodsWe searched for, collated and catalogued research relating to the effects of in-field arable farming practices in temperate systems on GHG emissions. Results from 6 bibliographic databases, a web-based search engine and organisational websites were combined with evidence from stakeholders. Duplicates were removed and the results were then screened for relevance at title and abstract, and full-text levels according to a predefined set of eligibility criteria (following consistency checking). Relevant studies were then coded and their meta-data extracted and used to populate a systematic map database describing each study’s settings, methods and measured outcomes.ResultsThe mapping process identified 538 relevant studies from 351 articles. Nearly all of these (96%) were found from traditional research papers, with 42% from European countries and nearly half (203 studies) lasting for 12 months or less. Over half of all studies (55%) investigated multiple interventions with chemical fertiliser (n = 100), tillage (n = 70), and organic fertiliser (n = 30) the most frequently studied single intervention types. When combining individually studied and multiple interventions, the top three intervention types most frequently studied were: chemical fertiliser (n = 312); organic fertiliser (n = 176) and tillage (n = 158). Nitrous oxide was the most commonly studied outcome, with over double the number of studies compared to carbon dioxide, the next most studied outcome. Sandy loam and silty loam were the most commonly studied soils but there was a good distribution of studies across other types. However, studies predominately focused on humid sub-tropical (Cfa) and temperate oceanic (Cfb) climates, with hot summer Mediterranean (CSa) and warm summer Mediterranean (Csb) climate zones less represented.ConclusionsThe mapping process identified clusters of research for chemical and organic fertiliser especially in relation to nitrous oxide emissions and for both carbon dioxide and nitrous dioxide in relation to tillage. Therefore, there is potential for further synthesis for these interventions. The spread of research across soil textures and in the humid sub-tropical and temperate oceanic climates may enable further synthesis to provide tailored in-field advice for farmers and provide an evidence base to inform subsidies policy. However, smaller amounts of research relating to biochar, cover crops, crop rotation, and nitrogen inhibitors highlight gaps where further research would be beneficial.

Effects of Years of Rice Straw Return on Soil Nitrogen Components from Rice–Wheat Cropped Fields

Straw return is an important farmland management practice that influences the activity of soil nitrogen. Few studies have examined the distribution of soil nitrogen and its components in wheat–rice cropping fields in subtropical China. This study assesses the influence of different years of straw return on the distribution and variation of total soil nitrogen (TN), light fraction nitrogen (LFN), heavy fraction nitrogen (HFN), particulate nitrogen (PN), and mineral-bound nitrogen (MN). We conducted a field experiment with eight years of straw retention treatments in 2017 (no straw retention, NR; 1 year of straw retention, SR1; 2 years of straw retention, SR2; 3 years of straw retention, SR3; 4 years of straw retention, SR4; 5 years of straw retention, SR5; 6 years of straw retention, SR6; 7 years of straw retention, SR7) and one more treatment in 2018 (8 years of straw retention, SR8) in a rice–wheat cropping system at Yangzhou University Experimental Station in China. The results demonstrated that as the number of years of treatment increases, the content of TN, LFN, HFN, PN, and MN at each soil layer gradually increases. Compared with NR, the highest increase in TN, LFN, HFN, PN, and MN under SR1-SR8 in the 0–20 cm soils was 38.10%, 150.73%, 35.61%, 79.97%, and 27.71%, respectively, but increases in TN, HFN, and MN content gradually slowed after six years of straw return. The contents or variation of TN were extremely significantly correlated (p < 0.01) with that of LFN, HFN, PN, and MN, while LFN had the highest variation. In general, straw return could improve the quality of the 0–20 cm nitrogen pool. LFN was the best indicator of changes to the soil nitrogen pool affected by years of straw return.

Exploration of Informal Farmland Leasing Mode: A Case Study of Huang Village in China

How to effectively promote the large-scale and market-oriented farmland leasing process in China is one of the most important practical issues concerning the current academic circle and decision makers. However, restricted by the current situation of rural social development, farmers’ spontaneous and informal farmland leasing is still widespread. Exploring the long-term evolution characteristics of informal farmland leasing at the village scale is of great significance for optimizing the process of farmland leasing, perfecting the farmland leasing market, and promoting moderate-scale farmland management. Therefore, based on field survey data from the whole village and social network analysis methods, this research conducted a detailed empirical study on the characteristics, development process, and consequences of informal farmland leasing behavior in a traditional rural society in central China. The results show that with the development of time, the scope of informal farmland leasing in Huang village has been expanding, of which more than 70% of the farmland in 2020 was leased among acquaintances. Farmland leasing among acquaintances is becoming a trend toward informal farmland leasing in some villages. At present, 13 large-scale households lease 73.9% of the total area of farmland leasing in Huang village. The informal farmland leasing in the village has basically formed a centralized circulation pattern with the villager group as the core, which can promote moderate-scale farming to a certain extent. However, there are also problems, such as the ability of a simple internal leasing mode to resist external risks is limited. The findings may be helpful in rethinking China’s farmland leasing policy and provide useful insights into the multifaceted rural sustainability of other similar traditional villages.

An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods

Spectroscopy is essential to understand a series of phenomena in multiple fields of study. In remote sensing, vegetation analysis is one of the most prominent fields to explore, aiming to improve a specific task. As a task, modeling insect damage in the plants is essential to establish the correct management of agricultural farmlands. Hyperspectral data, which can be acquired with field spectroscopy at plant or leaf level, is a non-direct, rapid, and trustworthy approach to indicate its health. However, the spectral redundancy inherent is a challenge for the information extraction process, making the pre-processing phase an essential part of the analysis. Currently, artificial intelligence techniques, mostly based on machine and deep learning methods, are a standard application in data processing, being pre-processing techniques an essential part of it. But few studies aimed to measure the impact of such processes in vegetation monitoring, specifically with insect damage and spectral data. Here, we provide an analysis of the impact of pre-processing techniques on machine learning algorithms’ performance over said classification task. For this, we used a field spectroradiometer that operates within the 350–1,000 nm and 1,000–2,500 nm ranges. The dataset was composed of multiple spectral measurements that took place on different days in a controlled environment with soybean plants. As pre-processing techniques, methods like baseline removal, smoothing, first and second-order derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and principal components analysis (PCA) were investigated. Several machine learning algorithms and one deep learning method were applied to model the datasets. The impact of the pre-processing techniques was measured within validation metrics relate to its accuracy. Our results indicated that the Extra-Tree (ExT) algorithm was better, mainly when first-order derivative data were extracted from the dataset (accuracy equal to 93.68%). A ranking approach indicated that the most contributive spectral region situates at the near-infrared, between 784 and 911 nm. Our investigation also demonstrates that a deep neural network (DNN) did not return a satisfactory result over raw reflectance data. However, when considering a combination of PCA over the 2nd derivative data, it achieved similar results to the ExT algorithm (accuracy of 91.95%). The implications of such, alongside the ranking approach, are discussed in this paper. We hope that the information presented here serves as a framework for future research when applying pre-processing techniques alongside the machine and deep learning methods over spectral data.

Distribution of Culturable Phosphate-Solubilizing Bacteria in Soil Aggregates and Their Potential for Phosphorus Acquisition.

ABSTRACTDeciphering distribution patterns of phosphate-solubilizing bacteria (PSB) and phosphorus-cycling-related genes in soils is important to evaluate phosphorus (P) transformation. However, the linkage between PSB number and P-cycling-related gene abundance in soils, especially soil aggregates, remains largely unknown. Here, we estimated the numbers of PSB and abundances of P-cycling-related genes (i.e., gcd and bpp) in soil aggregates under different fertilization regimes as well as P-solubilizing performance and plant-growth-promoting ability of PSB. We found that tricalcium phosphate-solubilizing bacteria, phytate-degrading bacteria, and gcd and bpp abundances were more abundant in silt plus clay (silt+clay; <53 μm) than in macroaggregate (250 to 2000 μm) and microaggregate (53 to 250 μm). Fertilization treatment and aggregate fractionation showed distinct effects on PSB number and P-cycling-related gene abundance. We found significantly negative correlation between gcd gene abundance and tricalcium phosphate-solubilizing bacterial number (Col-CaP) and dramatically positive correlation between bpp gene abundance and phytate-degrading bacterial number (Col-Phy). P fractions were responsible for PSB number and P-cycling-related gene abundance. The isolated Pseudomonas sp. strain PSB-2 and Arthrobacter sp. strain PSB-5 exhibited good performances for solubilizing tricalcium phosphate. The inoculation of Pseudomonas sp. PSB-2 could significantly enhance plant fresh weight, plant dry weight, and plant height. Our results emphasized distinct distribution characteristics of PSB and P-cycling-related genes in soil aggregates and deciphered a close linkage between PSB number and P-cycling-related gene abundance. Our findings might guide the isolation of PSB from agricultural soils and provide a candidate plant-growth-promoting bacterium for agro-ecosystems.IMPORTANCE Phosphate-solubilizing bacteria are responsible for inorganic P solubilization and organic P mineralization. Elucidating the linkage between phosphate-solubilizing bacterial number and P-cycling-related gene abundance is important to isolate plant-growth-promoting bacteria for agro-ecosystems. Our findings reveal differentiating strategies of phosphate-solubilizing bacteria in soil aggregates, and the deciphered P fractions show strong effects on distribution patterns of phosphate-solubilizing bacteria and P-cycling-related genes. Additionally, we isolated phosphate-solubilizing bacteria with good plant-growth-promoting ability. This study enriches our knowledge of P cycling in soil aggregates and might guide the production and management of farmland.

Corn variable-rate seeding decision based on gradient boosting decision tree model

Variable-rate seeding (VRS) technology can adjust the seeding rate according to the growth environment of the crop so as to improve crop yield and resource utilization. The information collection of sensor-based VRS is carried out at the same time as the seeding operation, which improves the operation efficiency and reduces the operation cost. Adjusting the seeding rate according to the indicators that characterize soil fertility is the key to sensor-based VRS technology. However, most of the current research on seeding rate decisions is based on delineating farmland management zones, which cannot be applied to sensor-based VRS. In most studies, traditional linear regression was used to establish the relationship among soil indicators, seeding rate, and yield, which did not consider the impact of weather and management factors on yield. Given the above problems, in this study, corn yield prediction model based on gradient boosting decision tree (GBDT) algorithm was established by combining soil organic matter (SOM) data, weather data and management data, and was compared with the model built by random forest (RF) algorithm. Innovative seeding rate decision rules were developed by using the GBDT corn yield prediction model to simulate corn yield responses to a series of SOM contents and seeding rates. The result showed that the GBDT model (R2cv = 0.799) was better than the RF model (R2cv = 0.749). The simulation results of the GBDT model indicated that the yield showed a parabolic form with the increase of seeding rate under the same SOM, the yield increased first and then decreased with the increase of SOM under the same seeding rate, there was an agronomic optimum seeding rate (AOSR) under each SOM and the AOSR increased in a stepped shape with the increase of SOM. The following decision rules were obtained by summarizing the simulation results: when 10 g/kg ≤ SOM ≤ 12 g/kg, seeding rate = 84,900 seeds/ha; when 12 g/kg < SOM ≤ 13 g/kg, 84,900 seeds/ha < seeding rate ≤ 90,600 seeds/ha; when 13 g/kg < SOM ≤ 14 g/kg, 90,600 seeds/ha < seeding rate ≤ 92,110 seeds/ha; when 14 g/kg < SOM ≤ 20 g/kg, 92,110 seeds/ha < seeding rate ≤ 93,000 seeds/ha; when 20 g/kg < SOM ≤ 26 g/kg, 93,000 seeds/ha < seeding rate ≤ 93,880 seeds/ha. The crop yield prediction model is established through a machine-learning algorithm to create a controllable environment for exploring the change of yield with soil attributes and seeding rate, which is a promising method to study sensor-based variable-rate seeding decisions.

Regional intensity of biological disasters in farmland: quantitative assessment and spatiotemporal analysis.

Biological disasters in farmland have become a serious threat to global sustainability. Quantitative studies on spatiotemporal change in regional intensity of biological disasters in farmland (RIBDF) are crucial for promotingsustainable intensification of farmland useand global food security. Many studies have revealed the impacts of natural environment on biological disasters in farmland. However, research on the impacts of farmers' activities on biological disasters remains verylimitedfrom the perspective of induced substitution in agricultural production. Based on the principle of induced substitution in agricultural production, a theoretical framework for the impacts of farmland use intensity (FUI), the occurrence intensity of biological disasters (OIBD), the natural loss intensity resulting from biological disasters (NLIBD), and the actual loss intensity controlled by human activities (ALIBD) on change in RIBDF was presented, and we therefore established an assessment model integrating these four key indices in this study. Taking Guangdong Province in China as the study area, this study analyzed the spatiotemporal changes in RIBDF from 1996 to 2017 and found that RIBDF increased overall with three changestagesof slow growth, rapid growth, and decline at the provincial and regional levels. Moreover, the gravity center of RIBDF appeared to shift towards the coastal region in southwestern Guangdong at a speed of 18.20 km per year. The results reveal that the spatiotemporal change in RIBDF is determined by the key influencing factors including the substitution of chemical fertilizers for farmland and crop substitution. These findings suggest that it is necessary to expand the scale of farmland management and to encourage farmers to implement diversified cropping as well as chemical fertilizers reduction. The foremost contribution of this study is its exploration of an understanding of the linkage between farmland use activities characterized as induced substitution in agricultural productionand biological disasters. Moreover, policy implications for sustainable intensification of farmland usewerediscussedbased on these findings.

Risk Identification and Application of Farmland Management Right Mortgage Loan Based on Neural Network

There are a series of problems in China, such as low farmers’ income, difficulty in financing for farmers, and relatively lag in rural financial reform. The mortgage financing of farmland management rights is one of the important ways to solve these thorny problems and promote economic development. From the perspective of farmers, combined with empirical analysis, this paper fully considers farmers’ ideas, carries out risk identification and risk assessment for the mortgage loan of farmland management rights, and continuously improves the relevant risk prevention mechanism to reduce the operation risk of farmers’ farmland mortgage. This paper expounds the establishment principles of the risk evaluation index system in detail, lays a theoretical foundation for the selection of indicators, determines the risk evaluation system of land management right mortgage financing from the perspective of farmers, and builds a fuzzy-level comprehensive evaluation model. Based on the practical research data, the fuzzy hierarchy (F-AHP) comprehensive evaluation method is used to conduct an empirical comprehensive evaluation on the risk of the mortgage loan of the farmland management right and analyze the evaluation results. Among the surveyed farmers in the survey area, 242 have not actually participated in the mortgage loan of farmland management rights, accounting for 94.16% of the sample farmers. However, there are only 15 farmers who actually use the farmland management right for mortgage financing, accounting for 5.84% of the sample.

Private farmland autonomous adaptation to climate variability and change in Cameroon

ABSTRACT Rural communities areinherently vulnerable to global warming-induced climate change. This article examines farmland management choices and evaluates the income strategies made in climate change scenarios to ensure resilience. A Heckman probit model and a Ricardian revenue function are applied to information generated from 215 farms sampled in western and northern Cameroon. Economic factors, as well as geographic factors, are shown to influence the likelihood of perceiving and adapting to climate change. Climate influences revenue both in linear and quadratic forms, with temperature and rainfall significant in key farming seasons. The marginal impacts of climate are positive, whilst revenues decrease with summer temperature. Farm revenue is shown to be elastic for spring temperature but inelastic for summer temperature. A warming of 1.5°C leads to declines in revenue of about 4.3% without adaptation. When warming is increased to 2.5°C losses are shown to increase by 7.3%. These losses are lower when adaptation is considered.

Comparative Study on Farmland Circulation between Plains and Mountainous Areas in an Arid Region: A Case Study of Zhangye City in Northwest China

Farmland circulation is essential for agricultural scale management. Due to rapid urbanization and industrialization, a large number of rural laborers have migrated to cities, resulting in accelerated farmland circulation. Revealing the farmland circulation in different geographical environments is conducive to efficient farmland management but remain largely unknown. To this end, based on the questionnaire survey data and statistical data of Zhangye City, we compared the features of farmland circulation between plains and mountainous areas, and used the binary logistic regression model and other methods to analyze the main factors affecting differentiated farmland circulation at the plot level. The main circulation modes and proportions in the plains were leasing (54.4%), exchange (22.4%), and subcontracting (16.2%), while the single leasing mode in mountainous areas accounted for 89.5%. The scale management units of more than 33.33 ha accounted for 6.48% and 30.72% in plains and mountainous areas, respectively. The proportion of circulation periods exceeding 5 years were 28.13% and 2.23% in plains and mountainous areas, respectively. The factor of “degree of farmland fragmentation” positively affected (p &lt; 0.01) the farmland circulation in plains areas but negatively affected (p &lt; 0.01) that in mountainous areas. The “farmland circulation price” promoted (p &lt; 0.01) farmland circulation in both plains and mountainous areas. Whereas the “actual water diversion” (p &lt; 0.01) and “river source water” (p &lt; 0.05) only had varying degrees of negative impacts on farmland circulation in plains areas. Decision makers should practice management measures such as regulating farmland circulation behavior, formulating reasonable farmland circulation pricing models, and integrating farmland to promote the circulation and efficient use of farmland.

Effects of Plastic Film Mulching and Biochar Application on N2O Emission from a Vegetable Field

The aim of this research was to examine the effects of biochar addition (B0:0 t·hm-2, B20:20 t·hm-2, and B40:40 t·hm-2) and mulching (FM:film and NM:no film) on vegetables. The impact of N2O emissions in the field was based on the pepper-radish rotation vegetable field system on the farm of Southwest University, using static dark box/gas chromatography to conduct in-situ observations in the field for one year. In this experiment, a total of six treatments were set up, namely NMB0 (CK) and FMB0, NMB20 and FMB20, and NMB40 and FMB40. The results showed that FM significantly increased the content of ammonium and nitrate nitrogen in the pepper season soil (P<0.05) but had no significant effect on soil environmental factors in the radish season. Compared with that of NM, the pepper season FM increased the N2O emissions of the B0, B20, and B40 treatments by 52.87%, 52.97%, and 52.49% (P<0.05), respectively, but the radish season FM had no significant effect on N2O emissions. Biochar had no significant effect on soil environmental factors in the pepper and radish seasons. The addition of biochar in the radish season reduced N2O emissions by 28.76%-67.88% (P<0.01), and the addition of biochar in the pepper season had no significant effect on N2O emissions. Compared with that of NM, under different biochar levels, FM increased the yield of pepper by 15.85%-161.32% and increased the yield of radish by 43.97%-75.80%. Biochar significantly increased the yield of peppers and had no significant effect on the yield of radishes. Regardless of whether the film was covered or not, when the amount of biochar added was 20 t·hm-2, the yields of pepper and radish were the highest. The analysis of N2O emission intensity revealed that FM in the pepper season significantly reduced N2O emission intensity, whereas in the radish season FM and biochar significantly reduced N2O emission intensity, and both planting seasons reached the lowest N2O emission intensity under the FMB20 treatment. Therefore, mulching and applying 20 t·hm-2 biochar were the best farmland management measures for the pepper season and radish season, which could achieve high yields and the lowest N2O emissions, accomplishing a win-win for economic and environmental benefits.

An enhanced spatiotemporal fusion method – Implications for DNN based time-series LAI estimation by using Sentinel-2 and MODIS

The consequent and accurate monitoring of the seasonal dynamics of crop leaf area index (LAI) is critical to yield estimation and agriculture policy development. It is difficult for a single sensor to balance spatial and temporal resolution. Spatiotemporal fusion is an effective way to meet the need for high spatial and temporal applications. Among the fusion methods, regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) may be recommended for vegetation dynamic monitoring because of its outperformance for the cases with considerable phenological changes. However, it is not good at capturing image structure and textures. To overcome the limitations, an enhanced version of Fit-FC, referred as Enhanced-Fit-FC (EFF), was developed. The EFF method can be applied with one (unidirectional prediction, Uni-EFF) or two (bidirectional prediction, Bi-EFF) coarse-fine image pairs as input for near real-time or post-growth applications. The visual and quantitative assessments indicated that EFF mitigated the blurring effect of Fit-FC and generated accurate reflectance, especially Bi-EFF contributed to capturing land cover changes. Compared with Fit-FC, the correlation coefficient (CC) and quality index (QI) of EFF increased by more than 0.12, and the root mean square error (RMSE) decreased by 0.16 at the maximum. Further, we identified the robustness and adaptability of deep neural network (DNN) model for time-series LAI estimation. The results substantiated the effectiveness of DNN in dealing with nonlinear problems and alleviating spectral saturation with higher CC and lower RMSE and relative RMSE (rRMSE) at whole growth-stages (CC= 0.91, RMSE= 0.28, and rRMSE= 7.18%) and vegetative stage (CC=0.94, RMSE=0.24, and rRMSE= 5.81%). In conclusion, the EFF method proposed in this study is competent in constructing time-series synthetic images for near real-time or post-growth applications. Moreover, the DNN model shows the potential for LAI estimation at whole growth-stages. This research not only contributes to dense time-series remote sensing-based applications (e.g., land cover change monitoring, yield forecast), but also is valuable for high-spatial-precision farmland management. • A framework to enhance spatiotemporal fusion methods was constructed for near real-time or post-growth applications. • The Enhanced-Fit-FC (EFF) method provided more accurate reflectance and spatial details compared with Fit-FC. • The bidirectional prediction of EFF (Bi-EFF) contributed to capturing land cover changes. • The DNN model was potential in alleviating spectral saturation for LAI estimation at whole growth-stages.