State and transition models (STMs) are used to organize and communicate information regarding ecosystem change, especially the implications for management. The fundamental premise that rangelands can exhibit multiple states is now widely accepted and has deeply pervaded management thinking, even in the absence of formal STM development. The current application of STMs for management, however, has been limited by both the science and the ability of institutions to develop and use STMs. In this chapter, we provide a comprehensive and contemporary overview of STM concepts and applications at a global level. We first review the ecological concepts underlying STMs with the goal of bridging STMs to recent theoretical developments in ecology. We then provide a synthesis of the history of

Mulching the soil with polyethylene sheets before sowing during the hot season, increased the soil temperatures, which resulted in the control of soil-borne pathogens and weeds. This method was tested in a field heavily infested with Egyptian broomrape (Orobanche aegyptiaca L.). Soil was irrigated and mulched for 36 days during August–September 1977, prior to sowing carrot (Daucus carota L. ‘Nantes Tip Top’) seeds. Mulching increased soil temperatures by 8 to 12 C, up to 56 C in the top 5 cm. In the non-mulched plots the carrot plants became stunted due to heavy parasitization with broomrape and they were completely destroyed by the end of the season. In contrast, broomrape and other weeds were controlled in the mulched plots and the carrot plants grew normally. This effect was less pronounced in the border rows of the mulched plots. Mulching also greatly reduced the infestation of other weeds. Egyptian broomrape was also controlled in two other field experiments with carrots and eggplants (Solanum melongena L. ‘Black oval’). As compared with fumigation, this new method of control is economical, simple, nonhazardous, and does not employ toxic materials.

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Abstract. Quantifying catchment-scale soil property variation yields insights into critical zone evolution and function. The objective of this study was to quantify and predict the spatial distribution of soil properties within a high-elevation forested catchment in southern Arizona, USA, using a combined set of digital soil mapping (DSM) and sampling design techniques to quantify catchment-scale soil spatial variability that would inform interpretation of soil-forming processes. The study focused on a 6 ha catchment on granitic parent materials under mixed-conifer forest, with a mean elevation of 2400 m a.s.l, mean annual temperature of 10 • C, and mean annual precipitation of ∼ 85 cm yr −1 . The sample design was developed using a unique combination of iterative principal component analysis (iPCA) of environmental covariates derived from remotely sensed imagery and topography, and a conditioned Latin hypercube sampling (cLHS) scheme. Samples were collected by genetic horizon from 24 soil profiles excavated to the depth of refusal and characterized for soil mineral assemblage, geochemical composition, and general soil physical and chemical properties. Soil properties were extrapolated across the entire catchment using a combination of least-squares linear regression between soil properties and selected environmental covariates, and spatial interpolation or regression residual using inverse distance weighting (IDW). Model results indicated that convergent portions of the landscape contained deeper soils, higher clay and carbon content, and greater Na mass loss relative to adjacent slopes and divergent ridgelines. The results of this study indicated that (i) the coupled application of iPCA and cLHS produced a sampling scheme that captured the greater part of catchment-scale soil variability; (ii) application of relatively simple regression models and IDW interpolation of residuals described well the variance in measured soil properties and predicted spatial correlation of soil properties to landscape structure; and (iii) at this scale of observation, 6 ha catchment, topographic covariates explained more variation in soil properties than vegetation covariates. The DSM techniques applied here provide a framework for interpreting catchment-scale variation in critical zone process and evolution. Future work will focus on coupling results from this coupled empirical-statistical approach to output from mechanistic, process-based numerical models of critical zone process and evolution.

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass‐yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county‐level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)‐driven grassland productivity models with and without assimilation of measured FAW into the models’ water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county‐level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild‐hay yields with soil moisture data assimilation (Nash–Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs.

Core Ideas Soil scientists estimate soil texture class with higher accuracy then previously reported in the literature. Seasonal field scientists and citizen scientists estimate texture‐by‐feel with similar accuracy to university students with limited training. When novice observers misclassify texture class, it is more likely because of errors in estimating ribbon length than estimating grittiness. Estimating soil texture is a fundamental practice universally applied by soil scientists to classify and understand the behavior, health, and management of soil systems. While the accuracy of both the soil texture class and the estimates of the percentage of sand and clay is generally accepted when completed by trained soil scientists, similar estimates by “citizen scientists” or less experienced seasonal resource scientists are often questioned. We compared soil texture classes determined by texture‐by‐feel and laboratory analyses for two groups: professional soil scientists who contributed to the USDA‐NRCS National Soil Characterization Database and seasonal field technicians working on rangeland inventory and assessment programs in the Western United States and Namibia. Texture accuracy was compared using a confusion matrix to evaluate classification accuracy based on the assumption that laboratory measurements were correct. Our results show that the professional soil scientists predicted the laboratory‐determined texture class for 66% of the samples. Accuracy for seasonal field technicians was between 27 and 41%. When a “correct” prediction was defined to include texture classes adjacent to the laboratory‐determined texture based on a standard USDA texture triangle, accuracy increased to 91% for professionals and 71 to 78% for seasonal field technicians. These findings highlight the need to improve options for increasing the accuracy of field‐textured estimates for all soil texture observers, with relevance to career soil scientists, seasonal technicians, and citizen scientists. Opportunities for improving soil texture accuracy include training, calibration, and decision support tools that go beyond simple dichotomous keys.

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