Assessing the application of a geographic presence-only model for land suitability mapping

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results

Land Suitability Modeling Using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops

Land Suitability Modeling Using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops

Evaluation of aggregate stability methods for soil health

Aggregate stability is a commonly used indicator of soil health because improvements in aggregate stability are related to reduced erodibility and improved soil–water dynamics. During the past 80 to 90 years, numerous methods have been developed to assess aggregate stability. Limited comparisons among the methods have resulted in varied magnitudes of response to soil health management practices and varied influences of inherent soil properties and climate. It is not clear whether selection of a specific method creates any advantage to the investigator. This study assessed four commonly used methods of measuring aggregate stability using data collected as part of the North American Project to Evaluate Soil Health Measurements. The methods included water stable aggregates using the Cornell Rainfall Simulator (WSACASH), wet sieved water stable aggregates (WSAARS), slaking captured and adapted from SLAKES smart-phone image recognition software (STAB10), and the mean weight diameter of water stable aggregates (MWD). Influence of climate and inherent soil properties at the continental scale were analyzed in addition to method responses to rotation diversity, cash crop count, residue management, organic nutrient amendments, cover crops, and tillage. The four methods were moderately correlated with each other. All methods were sensitive to differences in climate and inherent soil properties between sites, although to different degrees. None measured significant effects from rotation diversity or crop count, but all methods detected significant increases in aggregate stability resulting from reduced tillage. Significant increases or positive trends were observed for all methods in relation to cover cropping, increased residue retention, and organic amendments, except for STAB10, which expressed a slightly negative response to organic amendments. Considering these results, no single method was clearly superior and all four are viable options for measuring aggregate stability. Therefore, secondary considerations (e.g., cost, method availability, increased sensitivity to a specific management practice, or minimal within-treatment variability) driven by the needs of the investigator, should determine the most suitable method.This article is published as Rieke, Elizabeth L., Dianna K. Bagnall, Cristine LS Morgan, Kade D. Flynn, Julie A. Howe, Kelsey LH Greub, G. Mac Bean et al. "Evaluation of aggregate stability methods for soil health." Geoderma 428 (2022): 116156. doi:10.1016/j.geoderma.2022.116156. Posted with permission.Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Monocyte tissue factor–dependent activation of coagulation in hypercholesterolemic mice and monkeys is inhibited by simvastatin

Hypercholesterolemia is a major risk factor for atherosclerosis. It also is associated with platelet hyperactivity, which increases morbidity and mortality from cardiovascular disease. However, the mechanisms by which hypercholesterolemia produces a procoagulant state remain undefined. Atherosclerosis is associated with accumulation of oxidized lipoproteins within atherosclerotic lesions. Small quantities of oxidized lipoproteins are also present in the circulation of patients with coronary artery disease. We therefore hypothesized that hypercholesterolemia leads to elevated levels of oxidized LDL (oxLDL) in plasma and that this induces expression of the procoagulant protein tissue factor (TF) in monocytes. In support of this hypothesis, we report here that oxLDL induced TF expression in human monocytic cells and monocytes. In addition, patients with familial hypercholesterolemia had elevated levels of plasma microparticle (MP) TF activity. Furthermore, a high-fat diet induced a time-dependent increase in plasma MP TF activity and activation of coagulation in both LDL receptor–deficient mice and African green monkeys. Genetic deficiency of TF in bone marrow cells reduced coagulation in hypercholesterolemic mice, consistent with a major role for monocyte-derived TF in the activation of coagulation. Similarly, a deficiency of either TLR4 or TLR6 reduced levels of MP TF activity. Simvastatin treatment of hypercholesterolemic mice and monkeys reduced oxLDL, monocyte TF expression, MP TF activity, activation of coagulation, and inflammation, without affecting total cholesterol levels. Our results suggest that the prothrombotic state associated with hypercholesterolemia is caused by oxLDL-mediated induction of TF expression in monocytes via engagement of a TLR4/TLR6 complex