Search

• Topsoil SOC saturation (or steady-state) is reached after 12–13 years of straw return. • Soil N 2 O and NO show high interannual variability, largely driven by changes in rainfall. • Increases in SOC stocks are associated with increases in N 2 O emissions and crop yields. • Long-term straw return shows no stimulating effect on yield-scaled N 2 O and NO fluxes. Straw return to agricultural soils is considered as a key strategy to improve soil organic C (SOC) sequestration and crop production. However, changes in SOC can affect soil N-turnover and associated N 2 O and NO fluxes, as soil biogeochemical C- and N-cycles are strongly coupled. Understanding how and to what extent N-trace gas emissions per unit crop yield respond to changes in SOC following straw return, is critical for balancing food security and climate change mitigation. Here we report on an experiment in a Chinese wheat-maize system designed to understand the effects of long-term (2005–2020) different straw management options (straw return, burning or removal) on SOC stocks and yields, completed by N 2 O and NO flux measurements over the period 2016–2020. Our results showed that the increase in SOC stocks following straw return tended to level off over time, indicating that SOC saturation is reached after 12–13 years under straw return. Soil N-trace gas emissions showed high inter- and intra-annual variability, which were largely driven by changes in rainfall, soil hydrothermal conditions or soil C- and N-availability. Compared to straw burning or removal, long-term straw return increased annual N 2 O emissions by 9 %-49 %, to 2.72–2.98 kg N ha−1 yr−1, while NO emissions were decreased by 12 %-28 %, to 0.83–0.90 kg N ha−1 yr−1. The annual N 2 O emissions roughly offset the total C sink (SOC sequestration and CH 4 uptake) by 40 %-46 %, but will override the climate benefits of total C sink as the SOC pool becomes saturated. However, because yields were higher in straw return treatments, yield-scaled annual N 2 O emissions remained unchanged. Our unique dataset allows for the first time a better understanding of the dynamics between SOC sequestration, yields and N-trace gas emissions, with results calling for action to address the risk of increasing N 2 O emissions following long-term SOC increases induced by straw return. [Display omitted] [ABSTRACT FROM AUTHOR]

• National soil organic carbon maps for cropland based on soil reflectance composite. • Local ensemble model that accounts for spatial dependencies in predictions. • Local variable importance of the spectral bands is attributed to soil properties. • New approach to reduce spatial autocorrelation and increase model interpretability. • Enhancing digital soil mapping in regions with heterogeneous soil conditions. National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models. [ABSTRACT FROM AUTHOR]

Abstract By using the latest China population grid and land-use data, we assess the changing exposure of China’s population and land uses to the hazards of storm surges, droughts, earthquakes, floods, and landslides from 1995 to 2015. We found that the single-hazard areas and the multi-hazard areas covered 43% and 26% of China’s territory, respectively. Population grew faster in the hazard-prone areas than in the non-hazard areas. Built-up area expanded more rapidly in the areas prone to earthquakes and landslides. Cropland changed rapidly in many hazard-prone areas. The hazard-prone areas affected by floods featured the highest cropland loss rates, while the areas prone to earthquakes and landslides featured the highest cropland growth rates. We detected areas with significant exposure changes by using hot spot analysis. It was found that population and built-up land in the Pearl River Basin were increasingly exposed to storm surges, floods, and landslides. The Haihe River Basin and Huaihe River Basin also showed a consistent increase of population and built-up land exposure to droughts and earthquakes. These findings can provide a foundation for the design and implementation of protection and adaptation strategies to improve the resilience of Chinese society to natural hazards.

Abstract The study examined the relationship between child labour participation and household wealth (in terms of livestock and cropland) considering representative sample smallholders from four (Kurfa Chelie, Kersa, Fedis, and Melka Bello) districts in eastern Hararghe Zone of Oromia regional state of Ethiopia. The descriptive statics showed that average family size was 5.45 individuals per household, which was larger than the national average (4.9) in 2016. In the study area, children with age interval of 4–14 years, that were the subject matter of this study, constituted 43.3% of the total population. Around 23% of sampled households spent less than 2471.22 Birr per annum per adult equivalent for home consumption, which is an amount based on the inflation adjusted poverty line. Households in Fedis district experienced the worst poverty head count score, wherein 44% of them could not satisfy the minimum living standard requirement. Double-hurdle model result revealed that livestock and cropland holding of smallholders significantly affect child labour participation in domestic work. This finding implies that household’s asset ownership (possession of larger livestock and cropland) would not initiate children to budget more time for their education. The model result also showed that household head’s education level significantly reduced children’s domestic labour participation in the study area. Thus, this research recommended that there should be improvement in households’ education and awareness level in the study area to improve child school enrolment. Finally, it is easy to say that the Wealth Paradox about child labour holds true for smallholders in eastern Ethiopia.

Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

Accurate and spatially explicit cropland maps are crucial for many applications, which include sustainable crop monitoring, food security, and land and agriculture planning and management. Zimbabwe lacks reliable data on cropland extent of the old and new re-allocated areas for inventory purposes. Objectives of this paper are to map cropland utilizing: (1) automatic classification; (2) multi-classifier system (MCS); and (3) normalized difference vegetation index and bare-soil index (NDVI-BSI) thresholding and determine the spatiotemporal cropland changes. Change detection is implemented through a post-classification statistical method. The classified results are compared with SADC and ESA land cover products, GFSAD30AFCE cropland layer, and Google Earth imagery. Results reveal that MCS and NDVI-BSI performed the best and achieved overall accuracies of 80.54% and 79.32% for 2013, and for 2018, they attained accuracies of 87.90% and 88.56%, respectively. Automated classification, MCS, and NDVI-BSI thresholding produced average cropland areas of 3416396, 10346778, and 9788833 Ha, respectively. Visual assessment observations show that NDVI-BSI thresholding outperformed the other two techniques. Comparing further the MCS and NDVI-BSI thresholding approaches' results of total cropland areas of Zimbabwe's ten provinces for the years 2013 and 2018, coefficients of determination of 0.8404 and 0.9619, respectively, are achieved. Change detection shows a general increase in the cropland area due to human activities despite the prolonged drought. However, we recommend further exploration of NDVI-BSI threshold values to derive cropland layers since the method is robust and can be automated easily and faster without inputting training data. We also recommend simulation of the changes in cropland areas using cellular automata and/or agent-based modeling.

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement.

The accurate estimation of the soil organic carbon (SOC) sequestration rate is very important for studying farmland soil fertility and environmental effects. In this research, a typical fluvor-aquic soil area, Fengqiu county, located in the Yellow River basin of the Huang-Huai-Hai Plain of China, was chosen as a study area. The physicochemical properties of 70 soil samples collected from the surface layer (at a depth of 0–20 cm) in 2011 were analyzed, and related data about the sampling sites were also collected from the Second State Soil Survey of China (SSSSC), conducted in 1981. The results revealed that the SOC density (SOCD) in Fengqiu county increased greatly on a spatio-temporal scale. The average SOCD increased from 15.66 to 26.09 Mg ha−1, and the SOCD sequestration rate was more than 0.20 Mg C ha−1 year−1 in most regions. Few areas showed lost carbon in the past 30 years (1981–2011). In addition, the study suggested that all the areas present strong carbon sequestration potential in the coming decades from 2011, and the carbon sequestration potential was mainly between 32–40 Mg ha−1. Finally, the SOCD sequestration rate was not only affected by natural factors, such as soil type and pH, but also positively correlated with artificial soil management measures, such as fertilization and straw returning. Therefore, we concluded that the farmland in Fengqiu county showed significant carbon sequestration characteristics in the past 30 years (1981–2011). Considering that soil has a great potential for carbon sequestration in the future, the trend of carbon sequestration in farmland soil might continue for a period of time. Furthermore, the results of this study emphasized that strengthening soil scientific management may play a positive role in improving soil carbon sequestration.

Some insect populations are experiencing dramatic declines, endangering the crucial ecosystem services they provide. Yet, other populations appear robust, highlighting the need to better define patterns and underlying drivers of recent change in insect numbers. We examined abundance and biodiversity trends for North American butterflies using a unique citizen‐science dataset that has recorded observations of over 8 million butterflies across 456 species, 503 sites, nine ecoregions, and 26 years. Butterflies are a biodiverse group of pollinators, herbivores, and prey, making them useful bellwethers of environmental change. We found great heterogeneity in butterfly species' abundance trends, aggregating near zero, but with a tendency toward decline. There was strong spatial clustering, however, into regions of increase, decrease, or relative stasis. Recent precipitation and temperature appeared to largely drive these patterns, with butterflies generally declining at increasingly dry and hot sites but increasing at relatively wet or cool sites. In contrast, landscape and butterfly trait predictors had little influence, though abundance trends were slightly more positive around urban areas. Consistent with varying responses by different species, no overall directional change in butterfly species richness or evenness was detected. Overall, a mosaic of butterfly decay and rebound hotspots appeared to largely reflect geographic variability in climate drivers. Ongoing controversy about insect declines might dissipate with a shift in focus to the causes of heterogeneous responses among taxa and sites, with climate change emerging as a key suspect when pollinator communities are broadly impacted. [ABSTRACT FROM AUTHOR]

Single and combined fertilization additions are a common tool to assess the interactions between nutrients in a given ecosystem. While such experiments can allow systems to be defined into categories of nutrient interactions, for example, simultaneous co‐limitation or single resource response, this categorization may itself be sensitive to way nutrient interactions are mathematically formulated. To this end, we developed a theoretical analysis of nitrogen (N) and phosphorus (P) fertilization experiments based on the computation of ratios between plant demand and soil supply for each nutrient to explore two mathematical interaction formalisms: Liebig's law of minimum (LM) and the multiple limitation hypothesis (MH). We defined, for each interaction formalism, what conditions (in terms of supply and demand in N and P) are required to make the ecosystem in each category of nutrient interaction. Notably, we showed that synergistic co‐limitation could occur even using LM formalism under certain conditions. We then applied our framework to global maps of soil nutrient supply and of crop nutrient demand to achieve the potential yield. This was done to examine how the choice of interaction formalism influenced the occurrence of nutrient interaction categories. MH predicts true co‐limitation for ∼40% of the global maize area where LM predicts other categories of nutrient interaction, particularly single resource P limitation (whose the exact occurrence is, however, sensitive to the amount of P applied in the fertilization experiments). Our study identified areas where real fertilization experiments are required to choose between LM or MH to best represent nutrient interaction in croplands. Key Points: Synergistic N and P co‐limitation could occur even using the Liebig's law of minimum as the formalism of nutrient interactionBy assuming the multiple limitation hypothesis, a true co‐limitation could affect ∼40% of the global maize area [ABSTRACT FROM AUTHOR]

In agricultural regions, like Whatcom County, Washington, crop and dairy production co-exist, but increased agricultural specialization and intensification have divorced what was, historically, an integrated approach to production. This agricultural segregation contributes to farm, regional and watershed-scale nutrient imbalances, and several different management approaches have been devised to improve, correct and close these nutrient cycling disparities. In high-density production regions, utilizing locally produced bio-based fertilizers may provide one avenue toward closing regional nutrient loops. Technologies such as dairy-operated nutrient recovery systems may help improve the flow of nutrients between dairy and cropping systems by extracting nutrients from raw manure and producing materials that are more easily used on farms than raw manure. To evaluate the potential impact of a nutrient recovery system and its related product, we estimated nitrogen balances between cropland use and manure production within Whatcom County, Washington and examined a theoretical scenario in which a specific nutrient recovery product was utilized across the region. We considered one economic barrier, transportation cost, and calculated a hypothetical comparison for transporting nitrogen in two forms, a downstream nutrient recovery product and raw manure. The scenarios presented here demonstrate a potential gap between regional nutrient supply and demand, illustrate the tradeoffs with a technological approach, and make clear that both technological tools and practical management strategies are needed to address the challenges of redistributing nutrients in high-density production areas. [ABSTRACT FROM AUTHOR]

Abstract Synthetic Aperture Radar (SAR) data are well‐suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global‐scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J‐statistic, Cohen's Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J‐statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution.

Global warming and human activities have intensified the duration, frequency, and extent of climatic extremes. The projected rise in global mean annual temperature of 1.5 °C/2 °C is thought to have severe impacts on the population exposed to droughts. Although these impacts on humans have been widely explored, the impacts associated with the cropland exposed to droughts have not been widely investigated. Here, we have examined the spatiotemporal pattern of China’s drought conditions and cropland exposure to droughts under global warming of 1.5 °C and 2 °C, along with the avoided impacts (as evaluated by the cropland exposure to droughts) when limiting the global warming to 1.5 °C instead of 2 °C. Results suggest that compared to the reference period (1995–2014), drought conditions will be alleviated when the projected rise in mean global temperature is limited to 1.5 °C rather than 2.0 °C. Although severe droughts tend to be mainly distributed in northwestern China, drought severities are increasing in southern China, especially in the southeastern region. In addition, the total cropland exposure to droughts across China exhibits an increasing trend in response to the 0.5 °C of additional global warming, especially in northwestern China and Huang−Huai−Hai region. If global warming could be limited to 1.5 °C, the avoided impact will exceed 30%, especially in northwestern China, southwestern China, and the Huang−Huai−Hai Plain. Furthermore, the rising cropland exposure to droughts under the 2 °C global warming is likely to be triggered by the rising frequencies of moderate and extreme droughts. Therefore, climate mitigation strategies are urgently needed to keep the global temperature rise below 1.5 °C, for the future sustainability of China’s cropland.

Understanding the relationship between drought and the water use efficiency (WUE) in terrestrial ecosystems can help reduce drought risk. It remains unclear what the correlation between the cropland water use efficiency (CWUE) and drought during drought events. We aim to identify the spatiotemporal relationship between drought and the CWUE and to ensure the service capacity of cultivated land ecosystems. In this study, the cubist algorithm was used to establish a monthly integrated surface drought index (mISDI) dataset for the Huang–Huai–Hai Plain (HHHP), and the run theory was used to identify drought events. We assessed the spatio-temporal variations of drought in the HHHP during 2000–2020 and its influence on the CWUE. The research results were as follows: from the overall perspective of the HHHP, the mISDI showed a downward trend. Drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant. The CWUE response to drought had a three-month lag period and a significant positive correlation, and it was shown that the cultivated land ecosystems in this area had strong drought resistance ability. This study provides a new framework for understanding the response of the CWUE to drought and formulating reasonable vegetation management strategies for the HHHP.

Global agricultural lands are becoming saline because of human activities that have affected crop production and food security worldwide. In this study, the spatiotemporal variability of soil electrical conductivity (EC) in Central Asia was evaluated based on high-resolution multi-year predicted soil EC data, Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product, precipitation, reference evapotranspiration, population count, and soil moisture datasets. We primarily detected pixel-based soil EC trends over the past three decades and correlated soil EC with potential deriving factors. The results showed an overall increase in salt-affected areas between 1990 and 2018 for different land cover types. The soil EC trend increased by 6.86% (p < 0.05) over Central Asia during 1990–2018. The open shrub lands dominated by woody perennials experienced the highest increasing soil salinity trend, particularly in Uzbekistan and Turkmenistan local areas, while there was a decreasing soil EC trend in the cropland areas, such as in Bukhara and Khorezm (Uzbekistan). The main factors that affect the variability of soil salinity were strongly associated with population pressure and evapotranspiration. This study provides comprehensive soil EC variations and trends from the local to regional scales. Agriculture and land resource managers must tackle the rising land degradation concerns caused by the changing climate in arid lands and utilise geoinformatics.

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Groundwater resource overdraft is a serious water resource concern worldwide. Although afforestation has been recognized as conserving water resources, improving water quality and mitigating river flood, the role it plays with groundwater resources is not fully investigated. Here we applied the US Geological Survey's Mississippi Embayment Regional Aquifer Study (MERAS) model to estimate impacts of afforestation in croplands on groundwater resource availability in the Upper Yazoo River Watershed (a humid subtropical climate), Mississippi, USA. Simulations showed that the average groundwater level had declined 1.2 m in the croplands over a 20-year period from 1987 to 2007, whereas the average groundwater level had declined only 0.13 m after afforestation for the same simulation period and occurred mainly due to no groundwater pumping and a slight increase in groundwater recharge. Our study implies that afforestation on low-productive croplands in a humid subtropical region could be an alternative to mitigate groundwater depletion. [ABSTRACT FROM AUTHOR]

Wetland losses in the Northern Prairie and Parkland Region are largely attributed to agriculture. Since land use is known to influence bird habitat selection, bird community composition is likely sensitive to the extent of neighboring agricultural activity. We determined local and landscape habitat variables predictive of wetland-dependent avian assemblage occurrence in southern Alberta. We: 1) identified distinct bird assemblages with a cluster analysis, 2) identified species indicative of these assemblages using an indicator species analysis, and 3) predicted bird assemblage occurrence in wetlands using a classification and regression tree. Wetland-dependent birds formed distinct avian assemblages that were primarily differentiated by region and local-level habitat characteristics like wetland size, depth, or vegetation cover. Generally, however, wetland-dependent bird assemblages were surprisingly insensitive to surrounding landscape composition. The extent of agricultural activity in the surrounding landscape was weakly predictive of avian assemblages in wetlands in the Grassland Natural Region, with edge-nesting birds being excluded from wetlands with >42% agricultural land cover in the surrounding landscape. [ABSTRACT FROM AUTHOR]

Purpose: Climate warming is anticipated to change the terrestrial carbon/nitrogen cycle through its impact on the fluxes of greenhouse gases such as CO2 and N2O. This study investigated the effect of diurnal warming and acid rain on CO2 and N2O emissions from soil-plant systems in a winter wheat–soybean rotation cropland. Materials and methods: Field rotational experiments of winter wheat and soybean were conducted by simulating diurnal warming and acid rain. Manipulated experiments included the control (CK), diurnal warming (T, + 2 °C), acid rain (AR, pH = 2.5), and the combined treatment (TAR, + 2 °C and acid rain). CO2 and N2O fluxes from soil-plant systems were measured using a static chamber-gas technique. Results and discussion: The results showed that in the winter wheat and soybean growing seasons, compared with CK treatment, T, AR, and TAR treatments did not change the accumulative amount of CO2 emission (AAC) across the full growth period (p ˃ 0.05), although T treatment significantly increased soil AAC (p < 0.05) at the grain filling-maturity stage. On the contrary, T and AR treatments significantly increased the accumulative amount of N2O emission (AAN) in winter wheat and soybean croplands (p < 0.05), which was attributed to leaf nitrate reductase activity, total biomass, and soil NO3−–N content. In addition, there was a significant interaction between warming and acid rain on N2O flux. The AAN from the winter wheat cropland under treatments was in the order: TAR ˃ AR ˃ T ˃ CK. Conclusions: Our findings suggest that diurnal warming and acid rain had no significant effect on CO2 emissions from soil-plant systems, but significantly increase N2O emissions in winter wheat and soybean growing seasons. Moreover, diurnal warming would strengthen the positive effect of acid rain on N2O emissions from soil-plant systems in winter wheat farmland. [ABSTRACT FROM AUTHOR]

Socioeconomic reforms in agrarian sector together with climate changes induce spatial changes in the anthropogenic factors of agricultural soil erosion and soil erosion patterns in the Asian part of Russia. Comparison of pre- and post-reform quantitative parameters of erosion rate and soil loss from arable slopes was performed using logical-mathematical erosion models for different administrative regions of the Asian part of Russia. Significant spatially-differentiated decrease in the annual soil loss from arable slopes was revealed in most of the administrative regions, except for the Altai krai and Amur oblast. On the arable lands of other administrative regions, the decrease in the annual soil loss varied within 25–50% of the soil loss in 1960–1990. The maximum decrease was observed in arid landscapes of the republics of Tyva and Buryatia and in the Zabaykalsky (Transbaikal) krai (–73–93%). Spatial changes in the rate of erosion are less prominent on currently cultivated land; a significant growth in the rate of agricultural soil erosion has taken place in the Far East economic region. The main driver of the post-reform dynamics of agricultural erosion was the countrywide decrease of cropland area and the change in the soil-protecting capacity of agrocenoses. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) data are well‐suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global‐scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J‐statistic, Cohen's Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J‐statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution. Key Points: Crop/noncrop classifications were evaluated using Uninhabited Aerial Vehicle Synthetic Aperture Radar (SAR) and simulated NASA ISRO SAR data using three spatial resolutions and performance metricsAccuracy was close to or exceeded the NISAR science requirement of 80% over a wide range of CVthr values (0.2 to 0.4)Optimal crop/noncrop delineating thresholds monotonically decreased with spatial resolution for each performance metric [ABSTRACT FROM AUTHOR]

In the Northeast Himalayas (NEH) region, four major conventional land-use types are forest, Jhum lands, fallow Jhum lands and plantations, but little is known about their sustainability and responses to changes. We collected soil samples at two uniform depths (0-15 and 15-30 cm) from the Zunheboto district of Nagaland (India). The dataset was statistically analyzed by conducting an ANOVA-one way, principal component analysis (PCA) and calculating an additive soil quality index (SQIa). Our results confirmed that sand content, bulk density (BD), porosity, soil organic carbon (SOC), cation exchange capacity (CEC), exchangeable calcium and potassium showed significant statistical differences among soil depths depending on the land use management. PCA results showed that soil texture, BD, porosity, SOC and exchangeable cations could be considered the major indicators to define soil quality. After estimating the SQIa, Jhum soils showed the highest values at the surface, while at 15-30 cm soil depth, fallow Jhum soils phase showed the highest ones. The conversion from natural forest to plantation does not hamper the SQ, but their conversion into Jhum may even increase it, for a shorter duration. However, after 1-2 year of cultivation and conversion from Jhum into fallow Jhum land, soil quality could be reduced. [ABSTRACT FROM AUTHOR]

Abstract Reducing nitrous oxide (N2O) emissions from agriculture is critical to limiting future global warming. In response, a growing number of food retailers and manufacturers have committed to reducing N2O emissions from their vast networks of farmer suppliers by providing technical assistance and financial incentives. A key challenge for such companies is demonstrating that their efforts are leading to meaningful progress toward their climate mitigation commitments. We show that a simplified version of soil surface nitrogen (N) balance—or partial N balance—the difference between N inputs to and outputs from a farm field (fertilizer N minus crop N), is a robust indicator of direct N2O emissions from fields with maize and other major rainfed temperate‐region crops. Furthermore, we present a generalized environmental model that will allow food‐supply‐chain companies to translate aggregated and anonymized changes in average N balance across their supplying farms into aggregated changes in N2O emissions. This research is an important first step, based on currently available science, in helping companies demonstrate the impact of their sustainability efforts.

Weed expansion has the potential to severely affect global agriculture. Habitat suitability models (HSMs) have been widely used to assess weed expansion to enable effective prevention and control of weeds. However, few studies have focused on the habitat suitability of global croplands for these weeds. Previous studies listed the 10 serious weed species that have the largest negative impact on global agriculture. Here, we used Maxent modeling (an HSM with good performance) to quantify the habitat suitability of global croplands for these 10 weed species using occurrence records and environmental variables (i.e., climate factors, soil properties, and human footprint). We compared the habitat suitability values of these 10 serious weed species in 174 different types of cropland. The habitat suitability values for all the weed species studied was high in global croplands. Furthermore, habitat suitability may depend on the type of cropland and the spatial variation created by varying climate factors, soil properties, and human footprint. Cynodon dactylon, Echinochloa crus-galli, Eleusine indica, Panicum maximum, and Sorghum halepense had the highest habitat suitability values in chicory plantations. Cyperus rotundus and Echinochloa colona had the highest habitat suitability values in olive plantations. The habitat suitability values for Imperata cylindrica was the highest in mustard plantations, Eichhornia crassipes was most suited to expansion in raspberry plantations, and the habitat suitability values for Lantana camara was the highest in chickpea plantations. Risk prevention and control should be based on the cropland type for these 10 serious weed species, taking into account climate factors, soil properties, and human activities. Our study provides guidelines for effective management of weed risk in different croplands globally.

Life cycle assessments (LCA) of staple food (rice, wheat and corn) production and assessments of the associated greenhouse gas (GHG) and reactive nitrogen (Nr) releases from environmental and ecological perspectives help to develop effective mitigation options. Therefore, carbon (C) and nitrogen (N) footprint reductions in agroecosystems have become an increasingly popular topic related to global climate change and agricultural adaptation. The LCA method was used to calculate the product and farm C footprint (CF) and N footprint (NF) of double rice, rice-wheat and wheat-maize production based on governmental statistical datasets and published results. The spatial and temporal patterns of CF and NF were analyzed in China during 2004–2017, and their driving factors were analyzed to identify potential mitigation strategies. The results showed that of the various inputs, fertilizer application and diesel oil consumption contributed the most to both GHG and Nr emissions from farm inputs in the grain crop production process. The CFs for double rice, rice-wheat and wheat-maize were 0.83, 0.74, and 0.37 kg CO2-eq kg−1 year−1 at the yield scale, respectively. In addition, the NFs were 11.6, 13.4, and 15.4 gN-eq kg−1 year−1 at the yield scale for double rice, rice-wheat and wheat-maize, respectively. The largest fraction of CF and NF was the share of CH4 emissions and NH3 volatilization from the field soil, respectively. The annual CFs and NFs of the multiple crop in the southwestern provinces were higher than those in the central and northern provinces. The annual GHG and Nr emissions from the multiple crop maintained a relatively stable state from 2004 to 2017. The proportion of CO2/Nr production was partly reflected a soil C/N ratio, suggesting a higher C abundance for the double rice production, which could be used as a reference parameter for crop structure adjustment in the future. Based on our results and other studies, some effective solutions, especially optimized fertilization, farm machinery operation efficiencies and changes in regional allocation of grain cropping areas, are needed to mitigate the impacts of climate change and eutrophication on the main grain crop production in China.

Background A huge reservoir was formed by the Three Gorges Dam in China, which also formed a riparian zone along the bank of the reservoir. In the period of low water-level, the riparian zone in tributary bays of the Three Gorges Reservoir (TGR) was always unordered cultivated, owing to its gentle slope and high soil fertility. This land-use practice creates high potential of generating greenhouse gas (GHG) emissions with periodic water level fluctuation. Methods To evaluate potential GHG emissions from the soil-air interface, the static opaque chamber method was adopted to evaluate the effect of elevations (180 m, 175 m, 170 m and 165 m) and land use types (dry lands, paddy fields and grass fields) from April to September in 2015 and 2016. Results The results showed that carbon dioxide (CO2) was the main contributor of GHG emission in riparian zone most likely because of high organic carbon from residues. Furthermore, high soil water content in paddy fields resulted in significantly higher methane (CH4) flux than that in dry lands and grass fields. Compared to grass fields, anthropogenic activities in croplands were attributed with a decrease of soil total carbon and GHG emissions. However, inundation duration of different elevations was found to have no significant effect on CH4 and CO2 emissions in the riparian zone, and the mean nitrous oxide (N2O) flux from dry lands at an elevation of 165 m was significantly higher than that of other elevations likely because of tillage and manure application. The high N2O fluxes produced from tillage and fertilizer suggested that, in order to potentially mitigate GHG emissions from the riparian zone, more attention must be paid to the farming practices in dry lands at low elevations (below 165 m) in the riparian zone. Understanding factors that contribute to GHG emissions will help guide ecological restoration of riparian zones in the TGR.

This study documented the wild plants used by Tibetans and the related traditional knowledge in Burang Town (Karnali River Valley). Ethnobotanical surveys, including semi-structured interviews and participatory observations, were conducted in five Tibetan communities in July 2020 and August 2021. The informant consensus factor (ICF) and cultural importance index (CI) were used for data analyses. In total, 76 wild species belonging to 58 genera and 30 families were determined to be used. These included 26 edible, 29 medicinal, 34 fodder, 21 fuel, 17 incense, three economic, three dye, two ritual, two handicraft, and one species for tobacco plants species; many of these have multiple uses. The top five important plants are Carum carvi (CI = 1.88), Hippophae tibetana (CI = 1.45), Rheum moorcroftianum (CI = 0.87), Urtica dioica (CI = 1.45) and Chenopodium album (CI = 0.75). Of the wild plant species used, 53 were recorded in croplands and 25 were found in alpine pastures. This pattern of use is influenced by local livelihood patterns and culture. Plants in highland cropland have diverse ethnobotanical values that are often overlooked. These findings will inform strategies and plans for local communities and governments to sustainably use and protect plants at high altitudes.

Land abandonment in European mountains threatens habitats shaped for centuries by low-intensity agriculture and grazing. Hence, it is important to identify spatiotemporal patterns in rural abandonment, and relate them to biophysical and socioeconomic drivers. We pursued these goals in the theoretical context of transitions from traditional to productivist and then to post-productivist agriculture. We conducted a case study in a representative of southern Europe sub-mountainous marginal area that was once traditionally exploited (Pindus range, Epirus, Greece). Land cover was mapped from the outset of abandonment (years 1945, 1970, 1996 and 2015), and we subsequently calculated landscape metrics. An Intensity Analysis facilitated the comparison of rates of land cover change between time periods. By employing random forest modelling, we related socioeconomic, physiographic, geological and climatic predictors to land type occurrence and succession intensity. We found that farmland decreased from 30% to 3% during the 70 years of the study period, and that forest increased from 22% to 63%. The landscape’s heterogeneity, ecotone diversity, and spatial aggregation decreased. Abandonment and succession accelerated and relocated to lower elevation, especially during the latest time period, which was related to a second depopulation wave and livestock decrease. The remaining lowland farmlands were of productivist agriculture, and no widespread post-productivist regime was found. Thus, our study supports the view that policies, which have been mainly based on the linear transition of agricultural regimes in northern Europe, must take into account southern European mountains, where widespread abandonment can coexist with limited intensification and extensification.

Solar-induced chlorophyll fluorescence (SIF), when used as a proxy for plant photosynthesis, can provide an indication of the photosynthesis rate and has the potential to improve our understanding of carbon exchange mechanisms within an ecosystem. However, the relationships between SIF and vegetation indices (VIs) operating within different ecological contexts and the effect of other environmental factors on SIF remain unclear. This study focused on three ecosystems (cropland, forest, and grassland), with different ecological characteristics, located in Northeast China. These areas provide case studies where numerous relationships can be explored, including the correlations between the Orbiting Carbon Observatory-2 (OCO-2) SIF and MODIS products, meteorological factors, and the differences in the relationships between the three different ecosystems. Some interesting results and conclusions were obtained. First, in different ecosystems, the relationships between SIF and MODIS products show different correlations, whereby the enhanced vegetation index (EVI) has a close relationship with SIF in all the three ecosystems of forest, cropland, and grassland. Second, forest-type ecosystems appear to be sensitive to changes in daily temperature, whereas cropland and grassland areas respond more closely to changes in previous 16-day daily minimum temperature. Compared with forest and cropland areas, grasslands were more sensitive to precipitation (although the R2 value was small). Third, different ecosystems have different mechanisms of photosynthesis. Hence, we suggest that it is better to use SIF in areas exhibiting different ecological characteristics, and different models should be employed while simulating SIF.

Cropland soil carbon not only serves food security but also contributes to the stability of the terrestrial ecosystem carbon pool due to the strong interconnection with atmospheric carbon dioxide. Therefore, the better monitoring of soil carbon in cropland is helpful for carbon sequestration and sustainable soil management. However, severe anthropogenic disturbance in cropland mainly in gentle terrain creates uncertainty in obtaining accurate soil information with limited sample data. Within the past 20 years, digital soil mapping has been recognized as a promising technology in mapping soil carbon. Herein, to advance existing knowledge and highlight new directions, the article reviews the research on mapping soil carbon in cropland from 2005 to 2021. There is a significant shift from linear statistical models to machine learning models because nonlinear models may be more efficient in explaining the complex soil-environment relationship. Climate covariates and parent material play an important role in soil carbon on the regional scale, while on a local scale, the variability of soil carbon often depends on topography, agricultural management, and soil properties. Recently, several kinds of agricultural covariates have been explored in mapping soil carbon based on survey or remote sensing technique, while, obtaining agricultural covariates with high resolution remains a challenge. Based on the review, we concluded several challenges in three categories: sampling, agricultural covariates, and representation of soil processes in models. We thus propose a conceptual framework with four future strategies: representative sampling strategies, establishing standardized monitoring and sharing system to acquire more efficient crop management information, exploring time-series sensing data, as well as integrating pedological knowledge into predictive models. It is intended that this review will support prospective researchers by providing knowledge clusters and gaps concerning the digital mapping of soil carbon in cropland.

Advances in monitoring capacity and strengthened law enforcement have helped to reduce deforestation in the Brazilian Amazon since the early 2000s. Embargoes imposed on the use of deforested land are important instruments for deterring deforestation and enabling forest recovery. However, the extent to which landowners respect embargoes in the Brazilian Amazon is unknown. In this study, we evaluated the current recovery status of embargoes due to deforestation imposed between 2008 and 2017 to conduct the first large-scale assessment of compliance with embargo regulations. We observed forest recovery in only 13.1% (±1.1%) of embargoed polygons, while agriculture and pasture activities were maintained in 86.9% (±1.8%) of embargoed polygons. Thus, landowners openly continue to disrespect environmental legislation in the majority of embargoed areas. We attribute the marked non-compliance observed to limited monitoring of embargoed areas, as environmental agents seldom return to verify the status of embargoed lands after they have been imposed. Recent advances in remote sensing provide low-cost ways to monitor compliance and should form the basis of concerted efforts to ensure that the law is observed and that those responsible for illegal deforestation do not benefit from it.

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

Crop harvesting and residue removal from croplands often result in imbalanced biogeochemical cycles of carbon and nutrients in croplands, putting forward an austere challenge to sustainable agricultural production. As a beneficial element, silicon (Si) has multiple eco-physiological functions, which could help crops to acclimatize their unfavorable habitats. Although many studies have reported that the application of Si can alleviate multiple abiotic and biotic stresses and increase biomass accumulation, the effects of Si on carbon immobilization and nutrients uptake into plants in croplands have not yet been explored. This review focused on Si-associated regulation of plant carbon accumulation, lignin biosynthesis, and nutrients uptake, which are important for biogeochemical cycles of carbon and nutrients in croplands. The tradeoff analysis indicates that the supply of bioavailable Si can enhance plant net photosynthetic rate and biomass carbon production (especially root biomass input to soil organic carbon pool), but reduce shoot lignin biosynthesis. Besides, the application of Si could improve uptake of most nutrients under deficient conditions, but restricts excess uptake when they are supplied in surplus amounts. Nevertheless, Si application to crops may enhance the uptake of nitrogen and iron when they are supplied in deficient to luxurious amounts, while potassium uptake enhanced by Si application is often involved in alleviating salt stress and inhibiting excess sodium uptake in plants. More importantly, the amount of Si accumulated in plant positively correlates with nutrients release during the decay of crop biomass, but negatively correlates with straw decomposability due to the reduced lignin synthesis. The Si-mediated plant growth and litter decomposition collectively suggest that Si cycling in croplands plays important roles in biogeochemical cycles of carbon and nutrients. Hence, scientific Si management in croplands will be helpful for maintaining sustainable development of agriculture.

Abstract Introduction Ethiopia has made efforts to tackle the challenges of low crop and livestock productivity and degradation of land resources through various rural development strategies. However, increasing demands for food, animal feed, fuel, and income-generating activities are putting pressure on the land. In this paper, we describe the production pressure and competition between crop and livestock production, quantify rates of land-use/cover (LULC) changes, and examine driving forces and consequences of land conversion. Methods The study was conducted in Gudo Beret watershed, North Shewa Zone of Amhara region, Ethiopia. It used a combination of methods including remote sensing, household interviews, field observations, focus group discussions, and key informant interviews. Supervised and unsupervised image classification methods were employed to map LULC classes for 31 years (1984–2016). Results The results of satellite remote sensing revealed that 51% of the land in the study area was subject to accelerated land conversions. The household survey results indicated that feed resources and grain production pressures were 1.43 and 1.34 t ha−1 respectively. The observed annual changes in plantation and settlement areas were 2.6% and 2.9%. This was mainly at the expense of bushland and grazing land systems. Cropland increased (0.4% year−1) while grazing land reduced (3.5% year−1) under contrasting dynamics and competitive changes. An increase in human and livestock populations and farm expansion were major drivers of land conversion that adversely affected household livelihoods and the natural ecosystem. The consequences of these pressures resulted in a lack of animal feed, low crop-livestock productivity, and a reduction in natural vegetation coverage. Conclusions We suggest that sustainable land resource management, more integrated crop-livestock production, and the use of productivity-enhancing technologies could play a role in managing competition for land resources.

Bioenergy crops are considered to have the potential for climate mitigation and socio-economic benefits owing to their capacity to sequester C and partially replace the consumption of fossil fuels. In this study, the effects on soil respiration of a recent conversion of arable land to high (H) and very high (VH) density short-rotation coppices (SRCs), as well as of agronomic treatments (fertilization with or without irrigation) and lane/row location, were investigated in an alluvial area in Italy. A survey of soil variability was carried out by collecting soil cores (0-60 cm depth) at 67 points to characterize surface and subsurface spatial distributions of pH, organic carbon, nitrogen and carbonates and identify comparable points for monitoring soil respiration. Soil CO2 emissions were monitored over the period April 2010-November 2011 at 27 locations covering the whole study site. The influence of land use (H-SRC, VH-SRC, corn and alfalfa) or treatments on soil respiration was evaluated considering both factors as fixed effects in a linear mixed model. Our results showed that (i) the high variability of soil properties even at small spatial scale has to be considered when selecting points for monitoring soil respiration in the field; (ii) the cumulative soil respiration over the study period at the VH-SRC was lower (1299 ± 30 g C m-2) than in croplands (1600 ±145 g C m-2) and higher along the rows than in the lanes; (iii) no significant differences in soil respiration were found between the H-SRC and corn field; (iv) two years after VH-SRC establishment, agronomic treatments did not appear to influence soil respiration; (v) land-use change affected the vertical soil organic carbon distribution and soil surface temperature, as reflected in soil respiration differences.

Abstract Studying the responses of soil respiration (Rs) to soil management changes is critical for enhancing our understanding of the global carbon cycle and has practical implications for grassland management. Therefore, the objectives of this study were (1) quantify daily and seasonal patterns of Rs, (2) evaluate the influence of abiotic factors on Rs, and (3) detect the effects of soil management changes on Rs. We hypothesized that (1) most of daily and seasonal variation in Rs could be explained by soil temperature (Ts) and soil water content (Sw), (2) soil management changes could significantly affect Rs, and (3) soil management changes affected Rs via the significant change in abiotic and biotic factors. In situ Rs values were monitored in an agropastoral ecotone in Inner Mongolia, China, during the growing seasons in 2009 (August to October) and 2010 (May to October). The soil management changes sequences included free grazing grassland (FG), cropland (CL), grazing enclosure grassland (GE), and abandoned cultivated grassland (AC). During the growing season in 2010, cumulative Rs for FG, CL, GE, and AC averaged 265.97, 344.74, 236.70, and 226.42 gC m−2 year−1, respectively. The Ts and Sw significantly influenced Rs and explained 66%–86% of the variability in daily Rs. Monthly mean temperature and precipitation explained 78%–96% of the variability in monthly Rs. The results clearly showed that Rs was increased by 29% with the conversion of FG to CL and decreased by 35% and 11% with the conversion of CL to AC and FG to GE. The factors impacting the change in Rs under different soil management changes sequences varied. Our results confirm the tested hypotheses. The increase in Q10 and litter biomass induced by conversion of FG to GE could lead to increased Rs if the climate warming. We suggest that after proper natural restoration period, grasslands should be utilized properly to decrease Rs.

The European commission directive EC 128/2009 calls for monitoring pests and pathogens of major crops. The monitoring data may be analysed for trends over time, including tests for a potential loss of biodiversity in the domain of plant pests and pathogens. The monitoring programs carried out in Luxembourg since 2007 provided evidence for an increasing role of yellow rust and a decreasing role of brown rust on winter wheat. Vast inter-annual variability was observed at the level of Fusarium head blight and mildew symptoms on winter wheat as well as at the level of Ceutorhynchus counts in oilseed rape, but no trend towards extinction could be demonstrated. Septoria leaf blotch was present in winter wheat at high levels towards the end of all seasons. The maximum number of Brassicogethes aeneus individuals found per main stem and season on oilseed rape increased slightly but significantly between 2007 and 2017. Substantial evidence for highly dynamic changes in the pest populations was found, but no evidence for the vanishing of the monitored species could be demonstrated. [ABSTRACT FROM AUTHOR]

Recent evidence indicates that many native bee species are in decline due to the cumulative effects of multiple human‐induced stressors such as habitat loss, pesticide exposure, pathogens, and climate change. These declines have raised interest in the status of native bees and in developing tools that support management of bee communities and the ecosystem services they deliver.Native bees were surveyed using pan traps and netting over 2 years at 68 locations in croplands and rangelands across three ecological regions of Alberta's prairies – the Grassland, Parkland, and Boreal Natural Regions – to evaluate patterns in bee communities in response to disturbance and ecological gradients.Bee community composition was different across land use and ecoregions. While several cavity‐nesting species had a strong association with rangelands, cavity‐nesting bees tended to be less common in croplands and may be more sensitive to loss of rangeland habitat.Response patterns in overall bee abundance and richness were driven by interactions between region and land use, highlighting the need for regional studies to understand how bee communities respond to these factors.This survey is one of the first to sample the response of bee communities to landscape disturbance across a broad spatial area of the Canadian prairies. Large‐scale compositional studies are essential for understanding the status of native bee communities, and for monitoring long‐term trends over time. We recommend subsequent coordinated surveys using standardised methods across broad spatial scales. [ABSTRACT FROM AUTHOR]

Reducing nitrous oxide (N2O) emissions from agriculture is critical to limiting future global warming. In response, a growing number of food retailers and manufacturers have committed to reducing N2O emissions from their vast networks of farmer suppliers by providing technical assistance and financial incentives. A key challenge for such companies is demonstrating that their efforts are leading to meaningful progress toward their climate mitigation commitments. We show that a simplified version of soil surface nitrogen (N) balance—or partial N balance—the difference between N inputs to and outputs from a farm field (fertilizer N minus crop N), is a robust indicator of direct N2O emissions from fields with maize and other major rainfed temperate‐region crops. Furthermore, we present a generalized environmental model that will allow food‐supply‐chain companies to translate aggregated and anonymized changes in average N balance across their supplying farms into aggregated changes in N2O emissions. This research is an important first step, based on currently available science, in helping companies demonstrate the impact of their sustainability efforts. Plain Language Summary: As a powerful greenhouse gas, nitrous oxide that is emitted from agriculture contributes to climate change. Reductions in these emissions are not only possible—they are critical to addressing climate change. Food companies and others wanting to reduce nitrous oxide emissions in their food supply chains are looking for a way to show evidence of progress. Our research shows that a simple calculation of nitrogen (N) balance in crop fields (N in fertilizer minus N in the harvested crop) can be used as an indicator of nitrous oxide emissions. At the large scale, reducing high N balances will reduce overall emissions. We demonstrate the strong relationship between N balance and nitrous oxide emissions and show how this simple model can be used at scale to bring about positive environmental change. Key Points: Nitrogen balance is a robust indicator of nitrous oxide emissions from agricultural croplandFood‐supply‐chain companies and others can scale up field‐level N balance values to measure progress toward sustainability goals over timeThe relationship between N balance and N2O emissions is nonlinear, so the greatest benefit will come from reducing high field‐scale N balances [ABSTRACT FROM AUTHOR]

Understanding regional spatial pattern of soil organic carbon density (SOCD) and its potential influencing factors in cropland is of paramount importance when evaluating soil quality and assess the carbon sequestration potential. However, little information is available regarding the regional SOCD for the plough layer of the cropland soils with high‐density sampling sites. In this context, 14,058 soil samples were collected at a regional scale (21 × 104 km2) to investigate the spatial variation in SOCD and to identify the dominant factors influencing this variation using classical statistics, principal component analysis (PCA), residual maximum likelihood (REML), and geostatistical methods, such as semivariogram and ordinary kriging methods. The results indicated that soil organic carbon (SOC) and SOCD both demonstrated moderate variability ranging from 2.61 to 35.67 g kg−1 and 2.61 to 35.67 kg C m−2, respectively. Geostatistical analysis showed that the nugget‐to‐sill ratios for SOC and SOCD were 0.59 and 0.61, respectively, which demonstrated moderate spatial dependence. The PCA and a minimum data set method identified that organic nitrogen fertilizer, accumulated temperature ≥ 0°C, rice straw incorporation, precipitation, and soil pH were the major factors affecting the spatial variability in SOCD. The REML analysis showed that geomorphologic features, soil types, and irrigation conditions also exerted substantial influence on SOC and SOCD. This study provided reliable estimates of spatial variation in SOCD and thus is helpful in the formulation of strategic sustainable SOC management and regional policy decision making. [ABSTRACT FROM AUTHOR]

Surface runoff generation capacity can be modified by land-use and climate changes. Annual runoff volumes have been evaluated in a small watershed of tropical forest (Brazil), using the Soil and Water Assessment Tool (SWAT) model. Firstly, the accuracy of SWAT in runoff predictions has been assessed by default input parameters and improved by automatic calibration, using 20-year observations. Then, the hydrological response under land uses (cropland, pasture and deforested soil) alternative to tropical forest and climate change scenarios has been simulated. SWAT application has showed that, if forest was replaced by crops or pasture, the watershed's hydrological response would not significantly be affected. Conversely, a complete deforestation would slightly increase its runoff generation capacity. Under forecasted climate scenarios, the runoff generation capacity of the watershed will tend to decrease and will not be noticeably different among the representative concentration pathways. Pasture and bare soil will give the lowest and highest runoff coefficients, respectively. [ABSTRACT FROM AUTHOR]

The percentage of agricultural land cover effect on water quality in Culiacan River basin is studied in this research. The basin contains only intensive cropland as primary economic activity with 60% of the total area. Mathematical relationships between percentages of cropland and total phosphorus (TP) and total nitrogen (TN) concentrations were established. Sampling sites in middle and lower basin and water quality information during 2013–2018 were considered, and percentages of cropland were obtained by geospatial methods including variable area buffers. During rainy season, coefficients of determination were less than 0.2, although quantified nutrient concentration was higher, related to point sources of pollution in the basin. During dry season, coefficients of determination were higher than 0.76 and 0.90 for TN and TP, respectively, with an exponential mathematical trend. Results suggest that intensive agriculture practices generate accelerated loss of soil consolidation, which is transported to water bodies. These soils are in continuous contact with fertilizers and pesticides, mostly organophosphates which have been transported by runoff and underground flows. Using the information generated will help to establish environmental management plans, and to improve environmental diagnosis and effect in countries where there is not enough historical cartographic information and/or water quality data. [ABSTRACT FROM AUTHOR]

While improved management of agricultural landscapes is promoted as a promising natural climate solution, available estimates of the mitigation potential are based on coarse assessments of both agricultural extent and aboveground carbon density. Here we combine 30 meter resolution global maps of aboveground woody carbon, tree cover, and cropland extent, as well as a 1 km resolution map of global pasture land, to estimate the current and potential carbon storage of trees in nonforested portions of agricultural lands. We find that global croplands currently store 3.07 Pg of carbon (C) in aboveground woody biomass (i.e., trees) and pasture lands account for an additional 3.86 Pg C across a combined 3.76 billion ha. We then estimate the climate mitigation potential of multiple scenarios of integration and avoided loss of trees in crop and pasture lands based on region‐specific biomass distributions. We evaluate our findings in the context of nationally determined contributions and find that the majority of potential carbon storage from integration and avoided loss of trees in crop and pasture lands is in countries that do not identify agroforestry as a climate mitigation technique. [ABSTRACT FROM AUTHOR]

Multifunctional Agriculture (MFA) management has been increasingly important for sustainable cropland utilization in recent years. The farmland loss and changing agricultural activities have posed serious impacts on the structure and functions of cropland in the rapid urbanization. Therefore, it is necessary to explore the spatial-temporal evolution characteristics, the trade-off and synergy relationships of multifunctional cropland. In this study, taking Zhejiang Province, a representative region with rapid social-economic development in China as an example, a series of widely used quantitative methods were employed to systematically evaluate cropland multifunctional value on the county scale. According to four dimensions of cropland production, ecological, social, and cultural functions, eight main functions of cropland were identified, including food production, atmosphere regulation, environmental purification, water conservation, biodiversity, agricultural pollution, social security, and cultural leisure. Spearman's rank correlation coefficient method, bivariate local spatial autocorrelation model and Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model were used to investigate the trade-off and synergy relationships among cropland multifunctions, and the social-economic driving factors. The data was collected from Zhejiang Statistical Yearbook (2001-2016); digital land use/cover maps for 2000, 2010 and 2015 were obtained from the Chinese Ministry of Environmental Protection; the precipitation data came from the National Meteorological Information Center; and the soil type data of Zhejiang Province was from the second soil survey database of China. The results showed that: 1)the total value of cropland multifunctionality displayed a declined trend during 2000 and 2015, while the value of cropland multifunctionality per hectare decreased first and then gradually increased. The value of atmospheric regulation, water conservation and social security decreased significantly. Meanwhile, the value of food production remained constant, whereas, the value of cultural leisure increased rapidly. The total value of cropland multifunctionality showed different spatial characteristics, indicating high values in the north, but low in the south of Zhejiang Province. In the counties, Hangzhou City showed the highest value, while the lowest value was in Shengsi County. The cropland multifunctional value decreased in many counties to some degree, but the value increased dramatically in the southwest mountainous areas. 2) Synergy relationships were dominant among cropland multifunction, showing spatial heterogeneity. The high-high synergy areas were distributed in Hangzhou and Shaoxing cities, while the low-low synergy areas were clustered in Lishui and Quzhou cities in southwestern Zhejiang Province, where the low-high and high-low trade-off areas were distributed around the synergy areas. During 2000 and 2015, synergy relationships among cropland multifunctions totally weakened, as the decrease in the number of high-high synergy areas. 3) Social-economic system was proved to be related closely with cropland multifunctional value. Specifically, the disposable income of urban residents and the total power of agricultural machinery have negative impacts on the multifunctional value of cropland per hectare. The increase in the disposable income of rural residents can be conducive to promote the multifunctional value of cropland per hectare. It infers that the food production of cropland can be balanced with other functions, including ecological, social, and cultural functions, in order to develop multifunctional agriculture, and further to maximize the human welfare that provided by cropland production system. The findings can provide an important decision-making support to delimit cropland use and protection zone, and further to promote the overall coordinated utilization of multifunctional cropland. [ABSTRACT FROM AUTHOR]

Context: Albedo can be used to quantify ecosystem and landscape contributions to local and global climate. Such contributions are conventionally expressed as radiative forcing (RF) and global warming impact (GWI). We contextualize our results within landscape carbon production and storage to highlight the importance of changes in albedo for landscape GWI from multiple causes, including net ecosystem production (NEP) and greenhouse gas (GHG) emissions. Objective: To examine the spatiotemporal changes in albedo (Δα) in contrasting managed landscapes through calculations of albedo-induced RF (RFΔα) and GWI (GWIΔα) under different climatic conditions. Methods: We selected five contrasting landscapes within the Kalamazoo River watershed in southern Michigan USA as proof of concept. The daily MCD43A3 MODIS (V006) product was used to analyze the inter- and intra-annual variations of growing season albedo. In addition, the variations of RFΔα and GWIΔα were computed based on landscape composition and climate. Results: The RFΔα (− 5.6 W m−2) and GWIΔα (− 1.3 CO2eq ha−1 year−1) were high in forest-dominated landscapes, indicating cooling effects and CO2eq mitigation impacts similar to crops. The CO2eq mitigation of cropland-dominated landscapes was on average 52% stronger than forest-dominated landscapes. In the landscape with the highest proportion of forest, under dry and wet conditions CO2eq mitigation was reduced by up to 24% and ~ 30%, respectively; in one cropland-dominated landscape wet conditions reduced CO2eq mitigation by 23%. Conclusions: Findings demonstrate that quantifying spatiotemporal changes in albedo in managed landscapes and under different climatic conditions is essential to understand how landscape modification affects RFΔα and GWIΔα and thereby contributes to ecosystem-level GWI. [ABSTRACT FROM AUTHOR]

Context: Human appropriation of net primary productivity (HANPP) is employed as a measure of human pressures on biodiversity, though largely at global and national scales rather than landscape to regional scales where many conservation decisions take place. Though gaining in familiarity, HANPP is not widely utilized by conservation professionals. Objectives: This study, encompassing the US side of the Great Lakes basin, examines how regional distributions of HANPP relate to landscape-based biodiversity proxy metrics used by conservation professionals. Our objectives were (1) to quantify the HANPP of managed lands at the county scale; and (2) to assess spatial patterns of HANPP in comparison to landscape diversity and local habitat connectedness to determine if the metric can provide useful information to conservation professionals. Methods: We aggregated forest and cropland NPP data between 2005 and 2015 and coupled it with previously published potential vegetation maps to quantify the HANPP of each county in the study region. We mapped the outputs at 500 m resolution to analyze spatial relationships between HANPP and landscape metrics of biodiversity potential. Results: Area-weighted HANPP across our study region averaged 45% of NPP, down to 4.9% in forest-dominated counties. Greater HANPP correlated with reduced landscape diversity (p < 0.001, r2 = 0.28) and reduced local habitat connectedness (p < 0.001, r2 = 0.36). Conclusion: HANPP could be used as an additional tool for conservation professionals during regional-scale land use planning or conservation decision-making, particularly in mixed-use landscapes that both support important biodiversity and have high levels of primary production harvest. [ABSTRACT FROM AUTHOR]

The objective of this study was to find out how land use affects the soil microbial attributes in different soil types and to which depth. The study was performed in Slovakia (Europe) in three areas differing in soil type (Chernozem, Stagnosol, Cambisol). Within each area, three localities with different land use (forest, grassland, cropland), representing a gradient with different intensity of management, were chosen. The soil samples were taken along a single soil profile up to a depth of 1 m with 10 cm increments at each locality. In the soil samples, the basic soil chemical properties and microbial attributes were determined. The effect of the land use on the microbial biomass and basal respiration was mainly observed in the Chernozem in the top 30 cm, while in the Stagnosol, no difference in the trend in the microbial biomass between the different ecosystems along the soil profile was found. The N-mineralisation reflected the different management practices especially in the Cambisol in the top 20 cm. The most distinct differences in the catalase activity between the soils differing in land use were found in the Cambisol along the whole profile. The richness and diversity of the functional groups did not differ significantly between the soils with the different land use and also no uniform responses of the functional groups composition to the land use were observed. The microbial biomass and activity were mainly affected by the amount of the soil organic matter; the intensity of the impact differed according to the soil type. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)