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.

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Spatially and temporally unpredictable rainfall patterns presented food production challenges to small-scale agricultural communities, requiring multiple risk-mitigating strategies to increase food security. Although site-based investigations of the relationship between climate and agricultural production offer insights into how individual communities may have created long-term adaptations to manage risk, the inherent spatial variability of climate-driven risk makes a landscape-scale perspective valuable. In this article, we model risk by evaluating how the spatial structure of ancient climate conditions may have affected the reliability of three major strategies used to reduce risk: drawing upon social networks in time of need, hunting and gathering of wild resources, and storing surplus food. We then explore how climate-driven changes to this reliability may relate to archaeologically observed social transformations. We demonstrate the utility of this methodology by comparing the Salinas and Cibola regions in the prehispanic U.S. Southwest to understand the complex relationship among climate-driven threats to food security, risk-mitigation strategies, and social transformations. Our results suggest key differences in how communities buffered against risk in the Cibola and Salinas study regions, with the structure of precipitation influencing the range of strategies to which communities had access through time.

Keywords:Off-site methods Bayesian methods Digital data collection Mediterranean basin Spain Patch-based survey a b s t r a c t In landscapes whose surface has been modified by terracing and other agricultural land-use, the spatial and temporal patterning of prehistoric settlement can be difficult to detect using traditional, siteorientated archaeological survey methods, especially for small-scale societies. In these contexts, methods that can reveal occupational patterns at landscape scales, without the need to pinpoint specific sites of human occupation, can be especially useful. We employ a stratified, randomly selected patchbased survey strategy to examine socio-ecological dynamics from the Middle Paleolithic through Bell Beaker (Chalcolithic) periods within the Canal de Navarr es, eastern Spain. We divide the study region into survey strata according to differences in topography and vegetation communities and use a random selection of demarcated, terraced fields as data collection patches. All survey data is digitally recorded using tablets in the field, creating a streamlined and more accurate workflow, where observations of artifacts, soils, ground visibility, and photographs are georeferenced and ready for analysis in a GIS. Surface artifact densities, estimated from sampled patches, are used to generate prehistoric land-use maps and empirical Bayesian methods allow us to track shifts in occupational patterns through time. Regional reference collections of well-dated lithic artifacts provide the "prior knowledge" required to make estimates of the probability of prehistoric occupation in each sampled patch. This combination of field and analytical methods makes possible the study of regional-scale land-use dynamics in agriculturally modified landscapes.