Global Land Research Articles

HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin

Precise volumetric assessments of different hydrological variables, such as precipitation, evapotranspiration, and groundwater components, are necessary for comprehensive water resource management. This presents several challenges, including topographical complexity and economic limitations, mainly when aiming for high temporal and spatial resolution.The satellite mission Gravity Recovery and Climate Experiment (GRACE) has greatly improved the ability to quantify variations in GWS. We have described a two-stage downscaling system that integrates SWAT Hydrological Response Units (HRUs) with GRACE-derived Terrestrial Water Storage Anomalies (GRACE-TWSA) using Artificial Neural Networks (ANN). Using data from the Global Land Data Assimilation System (GLDAS), GWS was derived with GRACE-TWSA and further downscaled to the HRU level. The GWS trend in most of the study areas has not shown any significant trend from 2001 to 2014. The mean increasing trend was 2.16 mm/year and the mean decreasing was 2.28 mm/year. Using a decision-tree-based CatBoost model, the GWS has been used as an independent variable to determine distributed groundwater levels within the study area. A strong correlation between measured groundwater levels and GWS was observed, and a regularised robust optimization was used to determine the Specific yield. The surface water and groundwater budgeting indicate that most Gangetic area blocks are water-stressed. The study offers an extensive approach to integrated water resource management by providing insights into groundwater availability, aquifer storage characteristics, and budgeting with HRU scale GWS estimates.

A Cross-Resolution Surface Net Radiative Inversion Based on Transfer Learning Methods

Net radiation (Rn) is a key component of the Earth’s energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data and the relative scarcity of surface flux site data at home and abroad limit the potential of deep learning methods in constructing high spatial resolution net radiation models. To address this challenge, this study proposes an innovative approach of a multi-scale transfer learning framework, which assumes that composite models at different spatial scales are similar in structure and parameters, thus enabling the training of accurate high-resolution models using fewer samples. In this study, the Heihe River Basin was taken as the study area and the Rn products of the Global Land Surface Satellite (GLASS) were selected as the target for coarse model training. Based on the dense convolutional network (DenseNet) architecture, 25 deep learning models were constructed to learn the spatial and temporal distribution patterns of GLASS Rn products by combining multi-source data, and a 5 km coarse resolution net radiation model was trained. Subsequently, the parameters of the pre-trained coarse-resolution model were fine-tuned with a small amount of measured ground station data to achieve the transfer from the 5 km coarse-resolution model to the 1 km high-resolution model, and a daily high-resolution net radiation model with 1 km resolution for the Heihe River Basin was finally constructed. The results showed that the bias, R2, and RMSE of the high-resolution net radiation model obtained by transfer learning were 0.184 W/m2, 0.924, and 24.29 W/m2, respectively, which was better than those of the GLASS Rn products. The predicted values were highly correlated with the measured values at the stations and the fitted curves were closer to the measured values at the stations than those of the GLASS Rn products, which further demonstrated that the transfer learning method could capture the soil moisture and temporal variation of net radiation. Finally, the model was used to generate 1 km daily net radiation products for the Heihe River Basin in 2020. This study provides new perspectives and methods for future large-scale and long-time-series estimations of surface net radiation.

Utilization of the Google Earth Engine for the evaluation of daily soil temperature derived from Global Land Data Assimilation System in two different depths over a semiarid region

AbstractThe Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers.

Deforestation drivers in northern Morocco: an exploratory spatial data analysis

Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.

Colonization ability and uniformity of resources and environmental factors determine biological homogenization of soil protists in human land-use systems.

Humans have substantially transformed the global land surface, resulting in the decline in variation in biotic communities across scales, a phenomenon known as "biological homogenization." However, different biota are affected by biological homogenization to varying degrees, but this variation and the underlying mechanisms remain little studied, particularly in soil systems. To address this topic, we used metabarcoding to investigate the biogeography of soil protists and their prey/hosts (prokaryotes, fungi, and meso- and macrofauna) in three human land-use ecosystem types (farmlands, residential areas, and parks) and natural forest ecosystems across subtropical and temperate regions in China. Our results showed that the degree of community homogenization largely differed between taxa and functional groups of soil protists, and was strongly and positively linked to their colonization ability of human land-use systems. Removal analysis showed that the introduction of widespread, generalist taxa (OTUs, operational taxonomic units) rather than the loss of narrow-ranged, specialist OTUs was the major cause of biological homogenization. This increase in generalist OTUs seemingly alleviated the negative impact of land use on specialist taxa, but carried the risk of losing functional diversity. Finally, homogenization of prey/host biota and environmental conditions were also important drivers of biological homogenization in human land-use systems, with their importance being more pronounced in phagotrophic than parasitic and phototrophic protists. Overall, our study showed that the variation in biological homogenization strongly depends on the colonization ability of taxa in human land-use systems, but is also affected by the homogenization of resources and environmental conditions. Importantly, biological homogenization is not the major cause of the decline in the diversity of soil protists, and conservation and study efforts should target at taxa highly sensitive to local extinction, such as parasites.

Soil Moisture‐Temperature Coupling Increases Population Exposure to Future Heatwaves

AbstractHeatwaves have significant effects on ecosystems and human health. Human habitability is impacted severely as human exposure to heatwaves is projected to increase, however, the contribution of soil moisture effects to the increased exposure is unknown. We use data from four climate models, in which two experiments are used to isolate soil moisture effects and in this way to examine projected changes of soil moisture contributions to projected increases in heatwave events. Contributions from soil moisture to future population exposure to heatwaves are also investigated. With soil moisture effects combined with global warming, the longest yearly heatwaves are found to increase by up to 20 days, intensify by up to 2°C in mean temperature, with an increasing of frequency by 15% (the percentage relative to the total number of days for a year) over most mid‐latitude land regions by 2040–2070 under the SSP585 high emissions scenario. Furthermore, soil moisture changes are found to have a significant role in projected increases of multiple heatwave characteristics regionally compared with the global land area and contribute to more global population exposed to heatwaves.

Modeling of driving factors and headcut rates of ephemeral gullies in the loess plateau of China using high-resolution remote sensing images

ABSTRACT Ephemeral gully headcut erosion contributes significantly to global land degradation and increased sediment yields, but the underlying driving factors and prediction models remain poorly understood. We conduct a comprehensive quantitative analysis of ephemeral gully headcut erosion in the Loess Plateau using an optimal parameters-based geographical detector (OPGD) model, leveraging high-resolution remote sensing images. Our findings reveal a varied ephemeral gully head advance rate spanning 0.04–5.54 m yr−1 between 2009 and 2021 (average 1.37 m yr−1), with over 58% of the erosion rates falling between 0.50 and 2.00 m yr−1. Catchment area emerges as the primary driving factor, with an explanatory power of 61%. Moreover, the interactions between catchment area and slope degree, rainfall erosivity, and fractional vegetation coverage (FVC) had explanatory powers exceeding 80%. Furthermore, we developed a robust prediction model for ephemeral gully head advance rates based on the results from the OPGD model, incorporating the FVC factor. The validation of our model yielded a high coefficient of determination (R2 = 0.92 m yr−1) and low root mean square error (RMSE = 0.31 m yr−1). Our study offers new insights into ephemeral gully headcut erosion control in the Loess Plateau and serves as a valuable reference for loess regions worldwide.

AN EXTENDED THEORY OF PLANNED BEHAVIOUR MODEL TO PREDICT MALAYSIAN CONSUMERS’ INTENTION TO PURCHASE ORGANIC FOOD: A CONCEPTUAL STUDY

In response to the growing awareness of health and environmental concerns, there has been a noticeable increase in the acceptance and consumption of organic food globally. Despite governmental initiatives to boost the organic food market in Malaysia, the country's organic sector still represents a small portion of the global agricultural land. Furthermore, the adoption of organic food among Malaysian consumers is lower than in neighbouring countries, likely due to issues related to scepticism and affordability. Subsequently, understanding the factors that influence consumer behaviour becomes essential as the organic food market continues to develop in response to global and local changes. This conceptual study aims to understand potential propositions and their relation to consumer behaviour by integrating the Theory of Planned Behaviour and the Knowledge Attitude-Behaviour model to explore possible predictors related to the intention to purchase organic food. The finding will provide a clear understanding of the potential factors influencing organic food purchase intention and bridge a concrete argument of literature for better preparation for future study. It is expected that this conceptual study will serve as a foundation for further empirical research, guiding policymakers, industry stakeholders, and researchers in developing targeted strategies to accelerate the adoption of organic food consumption in Malaysia.

A dataset of land use/land cover fusion in Guangxi, China in 2000, 2010 and 2020

This study is based on six major land use/land cover remote sensing data products that are widely used in China and the world, namely ESA Global Land Cover Product (ESAGC), ESRI Global 10-meter Land Use/Land Cover Data (ESRI-LULC), Tsinghua University high-resolution global land cover product (FROM-GLC), Wuhan University China Land Cover Dataset (CLCD), global 30-meter high-resolution system land cover product (GLC_FCS30) and GlobeLand30 (GLC30). By reclassifying multiple data products, we developed a multi-source data product fusion method based on consistency assessment, and compiled a new Guangxi region land use/land cover data product in 2020 with a correlation coefficient greater than 0.94 with local statistical yearbook data. The study further used three sets of long sequence data (CLCD, GLC_FCS30 and GLC30) to create three land use/land cover fusion data products in Guangxi in 2000, 2010 and 2020. This study provides a methodological basis for the application of regional land use/land cover data products. The fusion of data can better support related research on regional environmental protection, agricultural production planning, and urban development planning in Guangxi.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850–1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850–1960 and 1961–2014, the export during the latter period (0.20 × 104 kg s−1) was smaller than that during the former period (0.28 × 104 kg s−1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

Enhancing the groundwater storage estimates by integrating MT-InSAR, GRACE/GRACE-FO, and hydraulic head measurements in Henan Plain (China)

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

Evaluating the effect of Multi-Scale droughts on autumn phenology of global land biomes with satellite observation

The end of the growing season (EOS), autumn phenology, is a significant indicator of vegetation health in terrestrial ecosystems. Higher frequency and intensity droughts are expected to have a greater impact on ecosystem homeostasis, and an urgent determination of the impact of temporal effects on autumn phenology is imperative to improve the understanding of ecosystem resilience and resistance and the stability of plant carbon sinks. This study aims to quantitatively assess the response of global autumn phenology to observed drought cumulative and lagged effects based on EOS and multi time-scales drought index (the Standardized Precipitation and Evapotranspiration Index, SPEI), and analyze the potential impact path of climate variables on EOS-SPEI relationship. Our results suggested that the cumulative and lagged effects of drought had a significant impact on the autumn phenology of approximately 27.00% and 48.73% of the vegetated area, respectively. The accumulated months were mostly concentrated on shorter time scales (1- to 3-month), and the lagged effect was mostly concentrated on 1 to 3 lagged months. These two effects on EOS were similar in different biomes and water availability, demonstrating that diverse biomes have different adaptation strategies to drought and the importance of water available for ecosystem drought mitigation. Precipitation and solar radiation had a direct negative impact on evaporation, whereas evaporation had a substantial directly positive impact on the intensity of drought effect on autumn phenology. The interaction between the climatic variables results in the accumulated months being directly positively regulated by thermal radiation, while the lagged months were directly positively influenced by precipitation, which indicates that the hydrothermal conditions at the onset of EOS occurrence govern the autumn phenology response to drought more than the occurrence time of EOS.

A scalable big data approach for remotely tracking rangeland conditions

Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management.

Phase and Amplitude Changes in Rainfall Annual Cycle Over Global Land Monsoon Regions Under Global Warming

AbstractLand monsoon rainfall has a distinct annual cycle. Under global warming, whether the phase and amplitude of this annual cycle would be changed is still unclear. Here, a global investigation is conducted using 34 CMIP6 and 34 CMIP5 models under a high emission scenario. Seasonal delays would occur in the Southern Hemisphere (SH) American (3.43 days), Northern Hemisphere (NH) African (5.98 days) and SH African (3.76 days) monsoon regions, while no robust signal is found in other monsoon regions. Except NH American monsoon, amplitude is enhanced in all the monsoon regions. Compared to amplitude, the phase changes dominate the future changes of precipitation in the SH American, NH African and SH African monsoon regions. In these phase‐dominated regions, atmospheric energetic framework is proved to be reliable at regional scale and the enhanced effective atmospheric heat capacity is found to be the dominant factor.

Human activities and increased anthropogenic emissions: A remote sensing study in Cyprus

BackgroundThe effects of human activity on the environment are widespread and widely known. Human activities on most continents can be generalized as shelters and food chains due to the basic requirements of human life. Most of these activities require Land-use Land-cover (LULC) transformations, and their effects can be seen as changes such as increases in the global Land Surface Temperature (LST) and air pollutant concentrations. The present research aims to use remote sensing to monitor LULC transformations in Cyprus. MethodThis research uses Sentinel-3 data, Python programming, geographical information systems, and remote sensing to develop a moving average research method for a case study of Cyprus. Importantly, this work eliminates all political and ethnic boundaries to produce a unified analysis. ResultBased on the research outcomes, the highest mean LST and sulfur dioxide, nitrogen dioxide, and particulate matter (with a diameter of 10 μm or less) emissions occur in the Limassol, Famagusta, Nicosia, and Larnaca districts. These emissions are mainly attributable to artificial surfaces, agricultural areas, and forested and semi-natural areas. These trends may relate to electric power plants, a cement factory, an airport, and intensified agricultural activities in the research area.

Assessing effects of climate and technology uncertainties in large natural resource allocation problems

Abstract. The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible, investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. In this paper, we apply the SCEQ algorithm (Cai and Judd, 2023) to solve a large-scale dynamic stochastic global land resource use problem with stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters and bounded shocks, the range of land conversion is considerably smaller for the dynamic stochastic model than for deterministic scenario analysis.

Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model

ABSTRACT Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for developing effective land restoration policies and mitigating desertification. We aimed to evaluate global segmental trends in desertification in arid land using Moderate Resolution Imaging Spectroradiometer images from 2000 to 2022. By constructing a robust MSAVI-Albedo Desertification Distance Index (DDI), we assessed the segmented development characteristics of desertification on Google Earth Engine. Additionally, we employed the SHapley Additive exPlanations (SHAP) model and machine learning methods to analyze the individual and interactive driving mechanisms of desertification. The results indicated an overall reduction in desertification, with approximately 23.21 million km2 (39% of the global arid region area) exhibiting negative trends in DDI. Approximately 31% of the land area showed a DDI of −0.002/y. Precipitation was consistently the primary factor influencing desertification, with an average SHAP value of 11.42. Secondary influencing factors included potential evapotranspiration, soil moisture, and vapor pressure differences. Notably, the coupling between precipitation and soil moisture exhibited the most significant impact on the desertification process, with SHAP coupling values of approximately 3.28 and 5.06 before and after the DDI turning point, respectively. These findings provide new insights into desertification on a global scale and offer valuable scientific support for promoting effective prevention and control measures.

What were the spatiotemporal evolution characteristics and influencing factors of global land use carbon emission efficiency? A case study of the 136 countries

Nations worldwide must collaborate to tackle the challenge of improving carbon emission efficiency and optimizing land use effectively. This study extends the study area from provinces and cities, regional city clusters, and countries to 136 major countries in the world, and shows the differences between countries. The paper analyzed the spatiotemporal evolution characteristics and influencing factors of land use carbon emission efficiency (LCEE) in these 136 countries from 2000 to 2019 using the super-efficiency slacks-based measure data envelopment analysis (Super-SBM DEA) model, global malmquist index, spatial autocorrelation measurement tools and Spatial Durbin model. It can help to promote low-carbon and green development while simultaneously improving the global LCEE. The findings indicate that: (1) Over the study period, there is a trend toward a decline in the LCEE worldwide. (2) The primary cause of the worldwide LCEE’s declining trend during the study period is the reduction in technical efficiency. (3) The low-low cluster type accounts for more, while the high-high agglomeration type accounts for less and exhibits a sluggish growth pattern. On the whole, there is a phenomenon of non-aggregation and non-uniformity. It is necessary to increase the driving effect of high-high cluster radiation. (4) Economic development level, investment level, population size, public service level and environmental sustainability level all have a significant impact on LCEE, while industrial development level has no significant impact on LCEE. From a global perspective, the radiation driving role of countries with high LCEE should be increased. Improve the efficiency and technological level of land use emission in light of local conditions, and promote independent innovation in green and low-carbon technologies. This is conducive to the establishment of long-term low-carbon growth.

Causal association between environmental variables and the excess cases of cutaneous leishmaniasis in Colombia: are we looking to the wrong side?

Our main aim was to estimate and compare the effects of six environmental variables (air temperature, soil temperature, rainfall, runoff, soil moisture, and the enhanced vegetation index) on excess cases of cutaneous leishmaniasis in Colombia. We used epidemiological data from the Colombian Public Health Surveillance System (January 2007 to December 2019). Environmental data were obtained from remote sensing sources including the National Oceanic and Atmospheric Administration, the Global Land Data Assimilation System (GLDAS), and the Moderate Resolution Imaging Spectroradiometer. Data on population were obtained from the TerriData dataset. We implemented a causal inference approach using a machine learning algorithm to estimate the causal association of the environmental variables on the monthly occurrence of excess cases of cutaneous leishmaniasis. The results showed that the largest causal association corresponded to soil moisture with a lag of 3 months, with an average increase of 8.0% (95% confidence interval [CI] 7.7-8.3%) in the occurrence of excess cases. The temperature-related variables (air temperature and soil temperature) had a positive causal effect on the excess cases of cutaneous leishmaniasis. It is noteworthy that rainfall did not have a statistically significant causal effect. This information could potentially help to monitor and control cutaneous leishmaniasis in Colombia, providing estimates of causal effects using remote sensor variables.

Potential long-term, global effects of enhancing the domestic terrestrial carbon sink in the United States through no-till and cover cropping

BackgroundAchieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing.ResultsWe find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops.ConclusionsWe conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations.