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[Objective] Land consolidation has become an important component of national agricultural policies, and a thorough evaluation of its impact on farmland large-scale management is both practically and theoretically significant. [Methods] Based on the policy context of “consolidating small plots into a large field” (CSPLF), this study took the exogenous shock in Yangshan County, Guangdong Province as an observation window, and used data from a quasi-natural experiment and the generalized difference-in-differences model to identify its impact on farmland large-scale management from three dimensions: plot scale, operating scale, and continuous planting scale. [Results] The results indicate that the expansion effect of CSPLF policy on plot scale is significant, with an increase of 12% in the plot area and a 28.8% drop in the number of plots. The CSPLF policy motivates farmers to rent out land to new agricultural operating entities, changing the pattern of “small-scale farmers replications”, and achieving large-scale operating management. It also increases the possibility of farmers growing similar crops on adjacent plots, thus achieving continuous planting scale. Further analysis shows that the farmland large-scale management stimulated by the CSPLF policy can deepen the division of household labor, especially in non-agricultural employment. [Conclusion] The findings of this study demonstrate that CSPLF policy plays an important role in the transformation of China’s farmland large-scale management mode, which has far-reaching strategic implications. The study highlights that we need to change the engineering trend in the implementation of CSPLF policy of the past, and attach great importance to land tenure rights adjustment.

The value of ecosystem regulation services (ERSV) is a crucial aspect of gross ecosystem product (GEP). Understanding and mastering the spatiotemporal evolution characteristics and driving factors of ERSV is essential for the efficient management of regional ecosystems. This study proposes an ERSV accounting model at the plot scale and uses the barycentric analysis method, the optimal parameters-based geographical detector model (OPGD), and the constraint line extraction method to analyze the spatiotemporal evolution characteristics, main driving factors, and constraint rules of ERSV in Yunyang District, Hubei Province in 2016, 2018, 2020, and 2021. The results show that (1) In the temporal dimension, the overall ERSV of the district increased by CNY 1.664 billion from 2016 to 2021, with an increase rate of 3.68%. The contribution values of climate regulation function and water retention function to ERSV was significant. (2) In the spatial dimension, the ERSV was high in the north and south and low in the middle, with high-value areas mainly located in woodland and wetland areas. The center of gravity of the ERSV increase shifted to the southwest by 12,455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m from 2016 to 2021. (3) The interaction of any two driving factors had greater explanatory power for the spatial differentiation of ERSV than that of a single driving factor, and all of them showed nonlinear or double factor enhancement characteristics. The human active index (HAI) and construction land proportion (CLP) were the leading anthropogenic factors, while the land surface temperature (LST) and NDVI were the leading natural factors. (4) The ERSV could maintain a high and stable value output when the HAI was less than 0.3, CLP was less than 15%, LST was between 18 and 22 °C, and NDVI was greater than 0.5. These results can guide the practices of ecology, production, and life in Yunyang District and contribute to the high quality and sustainable development of the regional ecology and economy. [ABSTRACT FROM AUTHOR]

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types. [ABSTRACT FROM AUTHOR]

Gully erosion has the ability to transport enormous amounts of soil material, which can degrade tropical volcanic landscapes as the highest potential of agricultural land. It is critical to understand gully erosion control activities in order to reduce the initiation and development of gully erosion. The goals of this study were to: 1) classify the different forms of gully control; 2) pinpoint the advantages and disadvantages of the gully control system; and 3) suggest forthcoming changes to gully erosion control systems in tropical volcanic landscapes. Field surveys employing random sampling were used to collect data on gully erosion points and control system. The findings of the study were explained using exploratory descriptive analysis. The research findings demonstrate that gully erosion control was accomplished through biological (vegetative), mechanical and combination methods, which were classified into nine categories. While mechanical methods have benefits in terms of strength in slowing the rate of gully growth, biological systems have advantages in terms of cost and energy. Adopting a continuous live wicker and wicker check dam system might assist in preventing cracks in the gully dimension. This study's findings can potentially reduce gully erosion susceptibility for local communities and are expected to be implemented globally in settings with similar features. Further research could be conducted on multiple tropical volcanoes with comparable issues in order to improve the performance of research results.

Understanding the contributions of stream bank and bed erosion will allow us to implement the most effective management practices. The objective of this study was to assess different methods to measure bank and bed erosion at different scales, specifically the watershed, reach and plot. Innovative and traditional methods were utilized. At the watershed scale, indices based on free satellite images were used. For the reach scale, indices were used, but the images with higher accuracy were purchased and captured by unmanned aerial vehicles (UAVs). At the plot scale, erosion pins, cross-sections and laser scanning were applied. The watershed scale analysis showcased "hot spots". These "hot spots" were reaches vulnerable to erosion and deposition. The indices of the purchased images were applied to these "hot spots" and allowed us to narrow the length of the reaches where UAV flights took place. These flight images located where erosion and deposition occurred. Finally, at the plot scale, laser scanning provided more detailed and accurate data at a greater scale compared to the traditional methods. The implementation of these methods allows us to find the areas vulnerable to erosion and deposition. These are the areas where nature-based solutions should be implemented to effectively mitigate erosion problems. [ABSTRACT FROM AUTHOR]

Based on spot-scale measurements of lysimeter-based water fluxes and chamber-based carbon fluxes (ETWML or FSTC), and field- scale measurements of eddy covariance-based water and carbon fluxes (ETEC or FEC), a coupled water-carbon flux model was developed to predicted simultaneously hourly water and carbon fluxes at either spot or field scale in the water-saving irrigated paddy in Tai Lake Region of China. The coupled model integrated Penman-Monteith and Farquhar-von Caemmerer-Berry (FvCB) sub-models with the Jarvis sub-model, and was calibrated based on measured fluxes in 2015 and validated using data in 2016–2017. The performance of the coupled model was assessed by comparing simulated values with measured hourly ETWML, FSTC, ETEC and FEC under various conditions, including day/night/all day periods, different canopy coverage levels, and seasonal variations during rice seasons. The results revealed notable differences in the parameters of the Jarvis sub-model between plot and field scales. The ratio of leaf dark respiration to leaf maximum carboxylation rate were respectively calibrated as 0.041 and 0.047 to reflect the respiration of soil-vegetation system at plot and field scales. The maximum carboxylation rate (Vm) reached its peak during the jointing-booting stage and then decreased until the end of the rice season, and the Vm values at the plot scale were considerably higher than those at the field scale. The coupled model respectively exhibited poor and acceptable performance in estimating water and carbon fluxes during nighttime, and performed well during daytime or throughout the day. Additionally, the coupled model performed much better in estimating diurnal and seasonal variations in fluxes under high leaf area index conditions compared to low leaf area index conditions.

Gully erosion has the ability to transport enormous amounts of soil material, which can degrade tropical volcanic landscapes as the highest potential of agricultural land. It is critical to understand gully erosion control activities in order to reduce the initiation and development of gully erosion. The goals of this study were to: 1) classify the different forms of gully control; 2) pinpoint the advantages and disadvantages of the gully control system; and 3) suggest forthcoming changes to gully erosion control systems in tropical volcanic landscapes. Field surveys employing random sampling were used to collect data on gully erosion points and control system. The findings of the study were explained using exploratory descriptive analysis. The research findings demonstrate that gully erosion control was accomplished through biological (vegetative), mechanical and combination methods, which were classified into nine categories. While mechanical methods have benefits in terms of strength in slowing the rate of gully growth, biological systems have advantages in terms of cost and energy. Adopting a continuous live wicker and wicker check dam system might assist in preventing cracks in the gully dimension. This study's findings can potentially reduce gully erosion susceptibility for local communities and are expected to be implemented globally in settings with similar features. Further research could be conducted on multiple tropical volcanoes with comparable issues in order to improve the performance of research results. [ABSTRACT FROM AUTHOR]

The unique natural geographical conditions and land use patterns in semiarid sandy areas have resulted in a distinct cultivated landscape. Identifying and classifying the morphological characteristics of cultivated land are key to improving land utilization efficiency. This study focuses on the semiarid sandy areas of China. We obtained information on cropland morphology by judging high-precision remote sensing images and landscape pattern analysis, and then determined the spatial clustering characteristics of different plot morphologies through local spatial autocorrelation analysis. The results are as follows: cultivated land can be classified into five main types: simple large plots, complex large plots, simple small plots, complex small plots, and scattered plots, and there are significant differences in morphology between eastern and western plots. In addition, different morphology types of plots formed a variety of aggregation patterns; clustered plots are surrounded by scattered plots, showing a staggered distribution pattern. Farmers and land managers can make more informed decisions regarding irrigation, fertilization, and crop selection, and this knowledge can provide a basis for further optimizing the layout of cultivated land, identifying fragmented cultivated land, and scientifically recovering farmland to ensure more scientific and refined classification management and zoning protection of cultivated land. [ABSTRACT FROM AUTHOR]

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types.

Predicting the occurrence of erosive rain events and quantifying the corresponding soil loss is extremely useful in all applications where assessing phenomenon impacts is required. These problems, addressed in the literature at different spatial and temporal scales and according to the most diverse approaches, are here addressed by implementing random forest (RF) machine learning models. For this purpose, we used the datasets built through many years of soil loss observations at the plot-scale experimental site SERLAB (central Italy). Based on 32 features describing rainfall characteristics, the RF classifier has achieved a global accuracy of 84.8% in recognizing erosive and non-erosive events, thus demonstrating slightly higher performances than previously used (non-machine learning) methodologies. A critical performance is the percentage of erosive events correctly recognized to the observed total (72.3%). However, since the most relevant erosive events are correctly identified, we found only a slight underestimation of the total rainfall erosivity (91%). The RF regression model for estimating the event soil loss, based on three event features (runoff coefficient, erosivity, and period of occurrence), demonstrates better performances (RMSE = 2.30 Mg ha−1) than traditional regression models (RMSE = 3.34 Mg ha−1). [ABSTRACT FROM AUTHOR]

Soil erosion modelling applied to burned forests in different global regions can be unreliable because of a lack of verification data. Here, we evaluated the following three erosion models: (1) Water Erosion Prediction Project (WEPP), (2) Morgan‐Morgan‐Finney (MMF) and (3) Universal Soil Loss Equation‐Modified (USLE‐M). Using field plots that were either untreated or mulched with straw, this study involved observations of soil loss at the event scale at a burned pine forest in Central Eastern Spain. The erosion predictions of the three models were analysed for goodness‐of‐fit. Optimization of the MMF model with a new procedure to estimate the C‐factor resulted in a satisfactory erosion prediction capacity in burned plots with or without the mulching treatment. The WEPP model underestimated erosion in the unburned areas and largely overestimated the soil loss in burned areas. The accuracy of soil loss estimation by the USLE‐M model was also poor. Calibration of the curve numbers and C‐factors did not improve the USLE‐M model estimation. Therefore, we conclude that an optimized MMF model was the most accurate way to estimate soil loss and recommend this approach for in Mediterranean burned forests with or without postfire mulching. This study gives land managers insight about the choice of the most suitable model for erosion predictions in burned forests. [ABSTRACT FROM AUTHOR]

Understanding the contributions of stream bank and bed erosion will allow us to implement the most effective management practices. The objective of this study was to assess different methods to measure bank and bed erosion at different scales, specifically the watershed, reach and plot. Innovative and traditional methods were utilized. At the watershed scale, indices based on free satellite images were used. For the reach scale, indices were used, but the images with higher accuracy were purchased and captured by unmanned aerial vehicles (UAVs). At the plot scale, erosion pins, cross-sections and laser scanning were applied. The watershed scale analysis showcased “hot spots”. These “hot spots” were reaches vulnerable to erosion and deposition. The indices of the purchased images were applied to these “hot spots” and allowed us to narrow the length of the reaches where UAV flights took place. These flight images located where erosion and deposition occurred. Finally, at the plot scale, laser scanning provided more detailed and accurate data at a greater scale compared to the traditional methods. The implementation of these methods allows us to find the areas vulnerable to erosion and deposition. These are the areas where nature-based solutions should be implemented to effectively mitigate erosion problems.

The unique natural geographical conditions and land use patterns in semiarid sandy areas have resulted in a distinct cultivated landscape. Identifying and classifying the morphological characteristics of cultivated land are key to improving land utilization efficiency. This study focuses on the semiarid sandy areas of China. We obtained information on cropland morphology by judging high-precision remote sensing images and landscape pattern analysis, and then determined the spatial clustering characteristics of different plot morphologies through local spatial autocorrelation analysis. The results are as follows: cultivated land can be classified into five main types: simple large plots, complex large plots, simple small plots, complex small plots, and scattered plots, and there are significant differences in morphology between eastern and western plots. In addition, different morphology types of plots formed a variety of aggregation patterns; clustered plots are surrounded by scattered plots, showing a staggered distribution pattern. Farmers and land managers can make more informed decisions regarding irrigation, fertilization, and crop selection, and this knowledge can provide a basis for further optimizing the layout of cultivated land, identifying fragmented cultivated land, and scientifically recovering farmland to ensure more scientific and refined classification management and zoning protection of cultivated land.

Predicting the occurrence of erosive rain events and quantifying the corresponding soil loss is extremely useful in all applications where assessing phenomenon impacts is required. These problems, addressed in the literature at different spatial and temporal scales and according to the most diverse approaches, are here addressed by implementing random forest (RF) machine learning models. For this purpose, we used the datasets built through many years of soil loss observations at the plot-scale experimental site SERLAB (central Italy). Based on 32 features describing rainfall characteristics, the RF classifier has achieved a global accuracy of 84.8% in recognizing erosive and non-erosive events, thus demonstrating slightly higher performances than previously used (non-machine learning) methodologies. A critical performance is the percentage of erosive events correctly recognized to the observed total (72.3%). However, since the most relevant erosive events are correctly identified, we found only a slight underestimation of the total rainfall erosivity (91%). The RF regression model for estimating the event soil loss, based on three event features (runoff coefficient, erosivity, and period of occurrence), demonstrates better performances (RMSE = 2.30 Mg ha−1) than traditional regression models (RMSE = 3.34 Mg ha−1).

The main aim of the present study is to introduce Hyperion narrow band information into high spatial broad band imagery so that it will be helpful for the estimation of crop parameters at small field scale from multispectral data. Here, we are targeting to retrieve the nitrogen content of summer paddy at plot scale. Our findings concluded that paddy crop heterogeneity can be assessed by implementing narrow band index regression model designed for paddy crop from multispectral LISS IV data. Moreover, the proposed models to construct the synthetic narrow bands in green (GS), red (RS) and NIR (NS) regions of the spectrum for high-resolution satellite imagery (LISS IV) are also presented here { G S ρ L I S S I V = 0.032 · G ρ IH + 17.296 ; R S ρ L I S S I V = 0.072 · R ρ IH + 16.025 ; N S ρ L I S S I V = 0.087 · N ρ IH + 24.923 }. It showed the spatial variability of nitrogen content ranged from 3.37–6.04% obtained by leaf nitrogen concentration (LNC: R705, R717, R491) index regression model and 3.82–5.96% by simple ratio (SR: R533, R565) index regression model over a rice agriculture system. Thus, the details of spatial variability at crop field level over a rice agriculture system can be assessed from multispectral LISS IV data with 5.6 m spatial resolution through the synthetic broad bands. The results conclude that narrow band-based index regression model derived from Hyperion bands by implementing band average method can effectively evaluate the nitrogen content distribution of croplands subjected to varying field heterogeneity. It confirmed that the proposed methodology can be applied for the variability mapping of crop parameters at plot-scale level within the fields from space platform, which can be beneficial in achieving operational precision farming more efficiently. [ABSTRACT FROM AUTHOR]

Spatial variability of surface soil moisture is a prime factor in modeling many environmental and meteorological processes. This study aims to model surface soil moisture in bare fields using Sentinel-1A SAR data at a regional scale. The site/plot selected for the study falls in the tropical semi-arid region of Malavalli, Karnataka, India. The study site is divided into 43 grids to collect soil moisture samples from bare field plots synchronized with Sentinel-1A pass. Sentinel-1A, dual-polarized (VV and VH) data with 5.405-GHz frequency and central incidence angle of 33° are used. Six SAR imageries were procured from ESA, out of which five were used to model field soil moisture and one for validation. Processing of the SAR imageries is carried out using SNAP 7.0 software's standard tools, and the backscattered energy of each sample grid is extracted using R software. The relation between SAR backscatter energy with soil parameters like moisture, dielectric constant, and roughness was used to model soil moisture. Results revealed that Sentinel-1A has a high potential to record the soil moisture spatial variation at the plot scale. Volumetric soil moisture and backscattered energy showed a positive correlation with R2 of 0.59 and 0.51 for VV and VH polarization. Dielectric constant also showed a positive correlation with backscattered energy having R2 of 0.54 and 0.48 for VV and VH polarization. With this knowledge, surface soil moisture is modeled over bare fields and mapped. Soil moisture modeled is validated using field data, which has R2 of 0.88 and RMSE of 1.93. The developed model and surface soil moisture map are helpful in regional hydrological studies and crop water requirement assessment. [ABSTRACT FROM AUTHOR]

Based on spot-scale measurements of lysimeter-based water fluxes and chamber-based carbon fluxes (ET WML or F STC), and field- scale measurements of eddy covariance-based water and carbon fluxes (ET EC or F EC), a coupled water-carbon flux model was developed to predicted simultaneously hourly water and carbon fluxes at either spot or field scale in the water-saving irrigated paddy in Tai Lake Region of China. The coupled model integrated Penman-Monteith and Farquhar-von Caemmerer-Berry (FvCB) sub-models with the Jarvis sub-model, and was calibrated based on measured fluxes in 2015 and validated using data in 2016–2017. The performance of the coupled model was assessed by comparing simulated values with measured hourly ET WML , F STC , ET EC and F EC under various conditions, including day/night/all day periods, different canopy coverage levels, and seasonal variations during rice seasons. The results revealed notable differences in the parameters of the Jarvis sub-model between plot and field scales. The ratio of leaf dark respiration to leaf maximum carboxylation rate were respectively calibrated as 0.041 and 0.047 to reflect the respiration of soil-vegetation system at plot and field scales. The maximum carboxylation rate (V m) reached its peak during the jointing-booting stage and then decreased until the end of the rice season, and the V m values at the plot scale were considerably higher than those at the field scale. The coupled model respectively exhibited poor and acceptable performance in estimating water and carbon fluxes during nighttime, and performed well during daytime or throughout the day. Additionally, the coupled model performed much better in estimating diurnal and seasonal variations in fluxes under high leaf area index conditions compared to low leaf area index conditions. • Water-carbon model was developed by integrating P-M and FvCB with Jarvis model. • Model performed well in estimating daytime and daily water (ET) and carbon (F) fluxes. • Model performed poorly in estimating ET , acceptably in F during nighttime. • Estimated ET and F performed better under high leaf area index conditions. [ABSTRACT FROM AUTHOR]

Raindrop size, rainfall intensity and runoff discharge affect the detachment and transportation of soil particles. Among these three factors, the rainfall intensity seems to be more important because it can change other two factors. Storm patterns can be determined by changing the rainfall intensity during the storm. Therefore, the objective of this research is to test the influence of storm pattern on runoff, soil erosion and sediment concentration on a rangeland soil slope under field rainfall simulation. Four storm rainfall intensity patterns were selected for examining the effects of variations in storm event characteristics on soil erosion processes. The selected storm patterns were: I (45, 55 and 70 mm h−1); II (45, 70 and 55 mm h−1); III: (70, 55 and 45 mm h−1); and IV (55, 45 and 70 mm h−1). The last pattern is a new one instead of the uniform pattern which has been sufficiently studied in previous researches. The experiments were conducted in field plots (in Kojour watershed, Mazandaran Province, Iran) with an area of one square meter and an constant slope gradient of 18%, surrounded by galvanised sheets. Following the non-uniform prioritization of the storm patterns for the studied variables, time to runoff (I>II>IV>III), runoff volume (III>IV>II>I), sediment concentration (IV>III>I>II) and soil erosion (III>IV>II>I)), it can be generally inferred that each pattern has specific effect on soil erosion processes during a storm. The results of the general linear model (GLM) test indicated that the effects of storm pattern on time to runoff, total runoff volume, runoff coefficient and soil erosion were significant at a level of 99%. The Duncan test showed that the storm patterns can be divided into three groups of III, IV; II; I (for time to runoff), I, II; IV, III (for runoff coefficient), and I; II; IV, III (for runoff volume and soil erosion). [ABSTRACT FROM AUTHOR]

Rainfall simulators are research devices that can be used for studying runoff and sediment transport on the plot scale. This technical note introduces a new solution that combines the two most commonly used methods for generating artificial rain—swinging and pulse jet systems. Reasons for developing this device are its universal use, simple construction, and reduction of water consumption, with better spatial distribution of rain and rainfall kinetic energy close to that of natural conditions. Routine operations of this device are expected for plots of 1 × 1 m, with a height 2–2.5 m. The rained surface could be extended to 2 × 2 m with lower spatial distribution. The sprinkled area in this case was limited by the drain box that also collected the remaining water. The principle of the presented single-nozzle simulator can be extended to multi-nozzle simulators for larger experimental plots.

Graphical abstract Highlights • For the first time, the evaporation of intercepted water of a winter oilseed rape was determined. • Evaporation of intercepted water of winter oilseed rape cannot be neglected in making decisions on irrigation planning. • Less soil water is available with increasing amounts of fertilization because non-productive water losses increase. Abstract Oilseed rape (Brassica napus L.) is the major oil crop in Europe and Germany but its hydrological functioning is poorly understood. Studies on the interception processes in oilseed rape have been lacking until the present. The interception of rainfall by oilseed rape is expected to vary in response to substantial changes in canopy structure throughout the cropping cycle and fertilization treatment. To determine evaporation of intercepted water, field measurements of a winter oilseed rape crop were conducted for different stages of development and five treatments of fertilization (0, 60, 120, 180 and 240 kg N/ha). Precipitation (P) and throughfall (TF) were measured hourly, along with vegetation structure measurements of the leaf area index (LAI). A range of 0–21% for the percentage of stemflow (SF) of P was estimated from soil moisture measurements. For the vegetation period from March to June the cumulative P of 112 mm was converted into 49 mm TF (44%). The inferred evaporation of intercepted water (I) was 63 mm (56%). There was a gradual and clear increase in I from 45% to 67%, reflecting the increasing fertilization treatments. Hence, less soil water was available with increasing amounts of fertilization, since the water fraction evaporating from the canopy increases. Plant architecture, LAI and meteorological conditions during the cropping cycle appeared to be the main factors determining I. For irrigation planning and water use, studies of the evaporation of intercepted water must be considered. [ABSTRACT FROM AUTHOR]

Abstract Environmental stressors and population growth have significantly affected terraced rice ecosystems, such as in the Sapa district in northern Vietnam. The question arises how natural and socio-economic components determine the amount of rice yields. This study combines a hybrid neural-fuzzy inference system (HyFIS) with GIS-based methods to generate two models that can map suitability areas for rice cultivation at a regional scale and predict actual rice yields at a plot scale. Semi-structured interviews, the "Integrated Valuation of Ecosystem Services and Tradeoffs" tool and different statistical models were used to investigate the impacts of eight environmental variables and three socio-economic variables on rice production. Subsequently, two HyFIS models were trained with an accuracy higher than 88%. Because the predictive power values of the two proposed HyFIS models were higher than those of benchmark models, they are considered as useful tools to assess and optimize land use and related rice productivity. Highlights • Hybrid models integrating neural network, fuzzy logic and geographical information system approaches were developed to find areas suitable for rice cultivation (a so-called S-HyFIS model) and to predict rice yields (a so-called I-HyFIS model). • Impacts of environmental characteristics and additional human inputs on rice production were quantified. • The S-HyFIS model can extrapolate results from plot to regional scales in order to generate a map representing crop suitability areas for rice cultivation. • The potential of conversion from different land uses/covers to arable lands and vice versa were analyzed in the Sapa district, Lao Cai province, Vietnam. [ABSTRACT FROM AUTHOR]

Human activities have increased anthropogenic CO2 emissions, which are believed to play important roles in global warming. The spatiotemporal variations of CO2 concentration and flux at fine spatial scales in aquaculture ponds remain unclear, particularly in China, the country with the largest aquaculture. In this study, the plot-scale spatiotemporal variations of water CO2 concentration and flux, both within and among ponds, were researched in shrimp ponds in Shanyutan Wetland, Min River Estuary, Southeast China. The average water CO2 concentration and flux across the water-air interface in the shrimp ponds over the shrimp farming period varied from 22.79 ± 0.54 to 186.66 ± 8.71 μmol L−1 and from − 0.50 ± 0.04 to 2.87 ± 0.78 mol m−2 day−1, respectively. There was no remarkable difference in CO2 concentration and flux within the ponds, but significantly spatiotemporal differences in CO2 flux were observed between shrimp ponds. Chlorophyll a, pH, salinity, air temperature, and morphometry were the important factors driving the spatiotemporal patterns of CO2 flux in the shrimp ponds. Our findings highlighted the importance and spatiotemporal variations of CO2 flux in the important coastal ecosystems. [ABSTRACT FROM AUTHOR]

Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot’s surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution).

In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.

Water is the most important resource for the humankind, thus understanding hydrological processes could be considered a vital task. Therefore, the main aims of this papers are to assess: (i) the current status of hydrologic field experiments; (ii) the techniques and the stages of the field hydrologic experiments at the micro-scale/plot-scale. Microscale hydrological studies are important both socially and - economically as they emphasize the role of key factors (e.g. slope) in the utilization of water resources, the identification of critical hydrological thresholds for mobilizing, the propagation of soil particles in water flows and also the time it takes for pesticides, nutrients, and heavy metals to be mobilized. The key to conducting a successful hydrological microscale experiment lies in performing repeated at-tempts in the field. From an economic point of view, expedition (temporary) hydrologic field experiments are beneficial, as they shorten the working period and reduce the financial costs of the data acquisition process. One of the challenges of experimental hydrology is the manipulation of "upscaling" or the statistical approach taken towards gathering and processing data. [ABSTRACT FROM AUTHOR]

Ecological restoration can result in extensive land use transitions which may directly impact on water runoff and sediment loss and thus influence tradeoffs between multiple hydrological and soil ecosystem services. However, quantifying the effect of these transitions on runoff and sediment yields has been a challenge over large spatial scales. This study integrated and synthesized 43 articles and 331 runoff experimental plots in the Loess Plateau of China under natural rainfall to quantify the impacts of land use transitions on (a) runoff and sediment production, (b) runoff and soil loss reduction effectiveness, and (c) the tradeoffs between runoff and soil erosion. The effects of ecological restoration on runoff and sediment yields were quantified using a general mixed linear meta-regression model with a restricted maximum likelihood estimator on overall and individual ecological restoration types. The results showed that artificial grassland, forest, natural grassland, and shrubland had higher runoff and sediment reduction effectiveness. The annual runoff reduction effectiveness of the ecological restoration overall was 72.18% with the effects of artificial grassland, natural grassland, shrubland, and forest at 71.89%, 50.60%, 73.18%, and 73.08%, respectively. The annual sediment reduction effectiveness of the overall ecological restoration was 99.9% without a significant difference among the four land uses associated with ecological recovery. In addition, shrubland and forest significantly reduced sediment yields with relatively high runoff costs. Natural grassland was optimal for balancing water provisioning and soil conservation, and artificial grassland was second to natural grassland in this respect. Meanwhile, newly unmanaged abandoned land and cropland had relative weak functionality with regard to soil and water conservation. The implications of this study's findings are discussed along with their potential to contribute to an improved understanding of the effects of ecological restoration on water supply and soil retention for the water-limited terrestrial ecosystem at a regional scale. [ABSTRACT FROM AUTHOR]

Externalities are important elements in the process of rural-urban land conversion. The study analyses the effects of externalities, including various positive and negative effects such as cluster effect, land fragmentation, and pollution to agricultural land, et al. We propose a method by using land conversion probability instead of land price to identify externalities of rural-urban land conversion and present the measurements of externalities. In the empirical study, Probit model is used to estimate the externalities of rural-urban land conversion at the plot scale. Plot characteristics are considered in the model as control variables. Plot characteristic variables include distance from agricultural land to features, land price, land quality and prime farmland control, and externality variables contain externalities from commercial, residential and industrial land in different distances. Results show that: firstly, the distances from parcels to city, to town, to county, to highway entrance and to high level road could elevate the probability of land conversion, but the distances from parcels to county, to low level road and to runoff could decrease the probability. In addition, prime farmlands have lower probability of land conversion than lands in developable area, but land price and soil quality have no significant influence on the probability. Secondly, 1 hm2 agricultural land’s converting to industrial land raises the probability of agricultural land by 0.39%, 0.39% and 0.14% in the buffer area of 0-200, 200-400 and 800-1 600 m, respectively; 1 hm2 agricultural land’s converting to commercial and residential land increases the probability of land conversion by 0.08% in the 800-1 600 m extent. Therefore, the externalities of agricultural land conversion to industrial land are respectively 0.91×104, 0.91×104, 0.33×104 yuan/hm2 in 0-200, 200-400 and 800-1 600 m extend, externalities of agricultural land conversion to commercial and residential land is 0.18×104 yuan/hm2 in 800-1 600 m extent. Since the externality amount is relatively small, incentive of cluster effect is not enough in Jinghai District of Tianjin. [ABSTRACT FROM AUTHOR]

Severe soil erosion occurs in southwestern China owing to the large expanses of urbanization and sloping land. This field monitoring study was conducted to record the rainfall events, runoff, and sediment yield in 20-, 40-, and 60-m plots under conditions of artificial disturbance or natural restoration in the purple soil area of southwestern China. The study took place during the rainy season, and the plots were situated on a 15° slope. The results showed that rainstorms and heavy rainstorms generated runoff and sediment yield. Rainfall intensity had a significantly positive power relationship with runoff rate and sediment yield rate in artificially disturbed plots but not in naturally restored plots. Plot length had a significant effect on runoff rate under artificial disturbance but not natural restoration. Within the same land disturbance category, there was no significant effect of plot length on sediment yield rate but there was a significant effect on sediment concentration. Overall, runoff rate, sediment yield rate, and sediment concentration showed remarkable effects of land disturbance across all plot lengths: naturally restored plots had 62.8-77.5% less runoff, 95.1-96.3% less sediment yield, and 63.1-73.5% lower sediment concentration than artificially disturbed plots. The relationship between runoff rate and sediment rate under the different land disturbances could be described by an exponential function. The results not only demonstrate the effectiveness of natural restoration for controlling runoff and sediment yield but also provide useful information for the design of field studies, taking into consideration the complexity of terrestrial systems. [ABSTRACT FROM AUTHOR]

Disturbance of the tropical montane cloud forest (TMCF) produces secondary forests and coffee agroecosystems that conserve part of the arboreal-shrub vegetation of the native forests. However, it remains unknown whether the contribution of these two land use types to the conservation of the soil hydraulic properties is comparable to that of the original forest. Given the heterogeneous nature of both the land use within the landscape and the vegetation within the plots, the spatial distribution of these properties also remains unclear. We used variography and linear models to study the mean variation and spatial heterogeneity of field-saturated hydraulic conductivity ( K fs ) at two different scales (plot and landscape) and examined its relationship with soil properties that are sensitive to land use change. The results suggest that secondary forests and coffee agroecosystems have a high capacity for water conduction, although this capacity is significantly lower in the latter. No structure was apparent in the spatial distribution of K fs within the plots; however, a north–south gradient was detected at landscape level. In addition to a similar gradient, the soil properties that control K fs (i.e., organic matter content, initial water content and bulk density) presented nested patches of average diameter 119–150 m. The average values of K fs and its distribution in a gradient across the landscape were explained statistically by land use type, initial soil moisture and clay content. This suggests that there is an interaction between land use and the soil properties that determine K fs and its spatial distribution at landscape level. [ABSTRACT FROM AUTHOR]

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

Litter layers and topsoil have important effects on surface runoff. To investigate these effects at the plot scale, artificial rainfall experiments were conducted on micro-runoff plots in Guizhou Province, China. Three types of plots were selected, the thin litter layer with low soil bulk density type (T-L type), the thick litter layer with high soil bulk density type (T-H type), and the moderate litter depth and soil bulk density type (M type), and three artificial rainfall intensities (30 mm/h, 70 mm/h, 120 mm/h) were used. The runoff volume was largest in the T-H type plot at different rainfall intensities and durations. Runoff in the M type plot had characteristics of both the T-L and T-H type plots. The runoff yielding speed was significantly higher and the runoff yielding time was significantly lower in the T-H type plot. In general, the runoff coefficient was the smallest in the T-L type plot and largest in the T-H type plot. The variations in the runoff coefficient were 15.6%, 19.3%, and 5.8% for the T-L, T-H, and M type plots respectively. The results of this study can improve the understanding of surface runoff processes at the plot scale under different litter and surface soil conditions.

Terrestrial laser scanning (TLS) provides an accurate means of analyzing individual tree attributes, and can be extended to plots using multiple TLS scans. However, multiple TLS scans may reduce the effectiveness of individual tree structure quantification, often due to understory occupation, mutual tree occlusion, and other influences. The procedure to delineate accurate tree attributes from plot scans involves onerous steps and automated integration is challenging in the literature. This study proposes a fully automatic approach composed of ground filtering, stem detection, and stem form extraction algorithms, with emphasis on accuracy and feasibility. The delineated attributes can be useful to analyze terrain, tree biomass and fiber quality. The automation was experimented on a mature pine plot in Finland with both single scan (SS) and multiple scans (MS) datasets. With mensuration as reference, digital terrain models (DTM), stem locations, diameters at breast height (DBHs), stem heights, and stem forms of the whole plot were extracted and validated. Results of this study were best using the multiple scans (MS) dataset, where 76% of stems were detected (n = 49). Height extraction accuracy was 0.68 (r2) and 1.7 m (RMSE), and DBH extraction accuracy was 0.97 (r2) and 0.90 cm (RMSE). Height-wise stem diameter extraction accuracy was 0.76 (r2) and 2.4 cm (RMSE). [ABSTRACT FROM AUTHOR]

Process-based forest ecosystem models vary from simple physiological, complex physiological, to hybrid empirical-physiological models. Previous studies indicate that complex models provide the best prediction at plot scale with a temporal extent of less than 10 years, however, it is largely untested as to whether complex models outperform the other two types of models at plot and regional scale in longer timeframe (i.e. decades). We compared model predictions of aboveground carbon by one representative model of each model type (PnET-II, ED2 and LINKAGES v2.2, respectively) with field data (19–77 years) at both scales in the Central Hardwood Forests of the United States. At plot scale, predictions by complex physiological model were the most concordant with field data, suggesting that physiological processes are more influential than forest composition and structure. Hybrid model provided the best predictions at regional scale, suggesting that forest composition and structure may be more influential than physiological processes. [ABSTRACT FROM AUTHOR]

Hydrologic heterogeneities (e.g., macropores and gravel outcrops) in floodplains are hypothesized to play an integral role in impacting flow and leaching between the soil surface and shallow alluvial aquifers, which are intricately connected to streams. Infiltration is often assumed to be uniform, but this neglects the spatial variability common in anisotropic, heterogeneous alluvial floodplain soils. The objective of this research was to quantify infiltration and hydraulic conductivity across a range of scales (point to 100 m²) using a berm infiltration technique. Plot-scale leaching experiments were performed across a range of soil types at each of three floodplain sites in northeastern Oklahoma and northwestern Arkansas. Plots maintained a constant head of 2 to 9 cm for up to 52 h. Effective saturated hydraulic conductivity (Keff), based on plot-scale infiltration rates and a one-dimensional Darcy flow equation, ranged between 0.6 and 68 cm h-1 and varied considerably even within a single floodplain. The Keff was also calculated at the point scale using particle size distributions and Retention Curve (RETC). Point-scale estimates were significantly lower than plot-scale Keff and also failed to capture the variability of Keff. The estimated permeability of the limiting layer reported in soil surveys was consistent with point-scale estimates of Keff but was lower than plot-scale Keff at most sites. Tension infiltrometers showed that macropores accounted for approximately 84% to 99% of the total saturated hydraulic conductivity. The plot scale (1 to 100 m²) generally appears to be within the representative elementary volume (REV), but drift in Keff occurs beyond the REV due to changing geomorphic formations. Plot-scale infiltration tests are recommended over point-scale estimates, although only small plots (1 m x 1 m) are necessary. [ABSTRACT FROM AUTHOR]

• Standardization processes for the comparison of plot data under diverse conditions. • Investigating climate impacts at the plot scale with long-term field observations. • Climate leverages water erosion in many ways by imprints on rainfall and soil. • Precipitation and temperature hold two different dimensions of surface movements. Soil erosion by rainfall and runoff is a land surface process strongly controlled by climate and is one of the most threatening environmental problems in the world. Understanding erosion processes in different climates is exceptionally important but remains unclear. A wide range of climatic gradients in China provides an ideal opportunity to investigate the effects of climate on water erosion. In this study, we selected 8 bare plots orthogonally distributed in eastern China and obtained 40 plot-years of data from 424 natural rainstorms. Then, quality-controlled data were standardized to achieve effective comparisons among different climate types. We analyzed the rainfall erosivity (R), surface runoff (r), and soil loss (A) in different climate zones with a thermal gradient of Cold-Warm-Hot and a precipitation gradient of Continental-Oceanic. Our results indicate that climatic factors have complex leverage over water erosion in multiple pathways. (1) Basic rainfall characteristics increase with increasing climatic gradients of temperature and precipitation, as does the frequency of extreme events. The yields of resultants r and A are accordingly higher in the Oceanic than in the Continental zone, yet they discordantly increase in the order of Warm-Hot-Cold. Annual maximum storms in the Continental and Warm zones have more control over the annual values of R, r, and A. The R-r-A relationships are more sensitive in the Continental or Cold zones than in other zones. (2) The potential risks of surface movements differ significantly across climatic gradients. The potentialities of r and A generally increase along increasing precipitation gradients from Continental to Oceanic and increase in the temperature order of Warm-Hot-Cold. (3) The interactions between rainfall and soil exhibit different Continental and Oceanic patterns due to climate-induced differences in rainfall patterns and soil conditions. In the case of an extreme rainstorm, rill erosion is expected to occur more readily in the Cold and Continental areas. (4) Different climatic elements play various roles in the generation of runoff and erosion. Precipitation controls the magnitude of annual yield, while temperature dominates the potential risk. These results provide robust evidence that patterns of rainfall, runoff, erosion, and their relationships are not consistent among different climate zones. This study offers systematic insight into how climate drives surface processes on hillslopes. This work can serve as a reference for the improvement of land surface models for global applicability and climate change, as well as for the standardization approach to plot data in comparative analyses. [ABSTRACT FROM AUTHOR]

Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot's surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution). [ABSTRACT FROM AUTHOR]

In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context. [ABSTRACT FROM AUTHOR]