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© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tropical Conservation Science 10 (2017): 1-5, doi:10.1177/1940082917720669., Large-scale commercial cropping of soybeans expanded in the tropical Amazon and Cerrado biomes of Brazil after 1990. More recently, cropping intensified from single-cropping of soybeans to double-cropping of soybeans with corn or cotton. Cropland expansion and intensification, and the accompanying use of mineral fertilizers, raise concerns about whether nutrient runoff and impacts to surface waters will be similar to those experienced in commercial cropland regions at temperate latitudes. We quantified water infiltration through soils, water yield, and streamwater chemistry in watersheds draining native tropical forest and single- and double-cropped areas on the level, deep, highly weathered soils where cropland expansion and intensification typically occurs. Although water yield increased four-fold from croplands, streamwater chemistry remained largely unchanged. Soil characteristics exerted important control over the movement of nitrogen (N) and phosphorus (P) into streams. High soil infiltration rates prevented surface erosion and movement of particulate P, while P fixation in surface soils restricted P movement to deeper soil layers. Nitrogen retention in deep soils, likely by anion exchange, also appeared to limit N leaching and export in streamwater from both single- and double-cropped watersheds that received nitrogen fertilizer. These mechanisms led to lower streamwater P and N concentrations and lower watershed N and P export than would be expected, based on studies from temperate croplands with similar cropping and fertilizer application practices., The work described here was supported by National Science Foundation grants EF 1655432, IOS 1457662 and ICER 1342953 and grants from the Fundação de Amparo à Pesquisa do Estado de São Paulo.

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Agriculture, Ecosystems & Environment 144 (2011): 281–289, doi:10.1016/j.agee.2011.08.016., Clearing for large-scale soy production and the displacement of cattle-breeding by soybeans are major features of land-use change in the lowland Amazon that can alter hydrologic properties of soils and the runoff generation over large areas. We measured infiltrability and saturated hydraulic conductivity (Ksat) under natural forest, pasture, and soybeans on Oxisols in a region of rapid soybean expansion in Mato Grosso, Brazil. The forest-pasture conversion reduced infiltrability from 1258 to 100 mm/h and Ksat at all depths. The pasture-soy conversion increased infiltrability from 100 to 469 mm/h (attributed to shallow disking), did not affect Ksat at 12.5 cm, but decreased Ksat at 30 cm from 122 to 80 mm/h, suggesting that soybean cultivation enhances subsoil compaction. Permeability decreased markedly with depth under forest, did not change under pasture, and averaged out at one fourth the forest value under soybeans with a similar pattern of anisotropy. Comparisons of permeability with rainfall intensities indicated that land-use change did not alter the predominantly vertical water movement within the soil. We conclude that this landscape is well buffered against land-use changes regarding near-surface hydrology, even though short-lived ponding and perched water tables may occur locally during high-intensity rainfall on pastures and under soybeans., This research was supported by the US National Science Foundation (NSF) grant DEB-0640661 and the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Agriculture, Ecosystems & Environment 159 (2012): 19-28, doi:10.1016/j.agee.2012.06.013., The expansion of soybean cultivation into the Amazon in Brazil has potential hydrological effects at local to regional scales. To determine the impacts of soybean agriculture on hydrology, a comparison of net precipitation (throughfall, stemflow) in undisturbed tropical forest and soybean fields on the southern edge of the Amazon Basin in the state of Mato Grosso is needed This study measured throughfall with troughs and stemflow with collar collectors during two rainy seasons. The results showed that in forest 91.6% of rainfall was collected as throughfall and 0.3% as stemflow, while in soybean fields with two-month old plants, 46.2% of rainfall was collected as throughfall and 9.0% as stemflow. Hence, interception of precipitation in soybean fields was far greater than in intact forests. Differences in throughfall, stemflow and net precipitation were found to be mainly associated with differences in plant structure and stem density in transitional forest and soybean cropland. Because rainfall interception in soybean fields is higher than previously believed and because both the area of cropland and the frequency of crop cycles (double cropping) are increasing rapidly, interception needs to be reconsidered in regional water balance models when consequences of land cover changes are analyzed in the Amazon soybean frontier region. Based on the continued expansion of soybean fields across the landscape and the finding that net precipitation is lower in soy agriculture, a reduction in water availability in the long term can be assumed., This study was supported by grants from NSF (DEB-0640661), the Fundaçao de Amparo à Pesquisa do Estado de São Paulo (FAPESP 08/58089-9) and the German Academic Exchange Service (DAAD 50122036).

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 17 (2011): 1821–1833, doi:10.1111/j.1365-2486.2011.02392.x., Large-scale soy agriculture in the southern Brazilian Amazon now rivals deforestation for pasture as the region’s predominant form of land use change. Such landscape level change can have substantial consequences for local and regional hydrology, which remain relatively unstudied. We examined how the conversion to soy agriculture influences water balances and stormflows using stream discharge (water yields) and the timing of discharge (stream hydrographs) in small (2.5 to 13.5 km2) forested and soy headwater watersheds in the Upper Xingu Watershed in the state of Mato Grosso, Brazil. We monitored water yield for one year in three forested and four soy watersheds. Mean daily water yields were approximately four times higher in soy than forested watersheds, and soy watersheds showed greater seasonal variability in discharge. The contribution of stormflows to annual streamflow in all streams was low (< 13% of annual streamflow), and the contribution of stormflow to streamflow did not differ between land uses. If the increases in water yield observed in this study are typical, landscape-scale conversion to soy substantially alters water-balance, potentially altering the regional hydrology over large areas of the southern Amazon., This project was supported by grants from NSF (DEB-0640661) and the Fundaçao de Amparo à Pesquisa do Estado de São Paulo (FAPESP 03/13172-2).

Environmental scientists today enjoy an ever-increasing array of geostatistical methods to analyze spatial data. Our objective was to evaluate several of these recent developments in terms of their applicability to real-world data sets of the soil field-saturated hydraulic conductivity (Ks). The intended synthesis comprises exploratory data analyses to check for Gaussian data distribution and stationarity; evaluation of robust variogram estimation requirements; estimation of the covariance parameters by least-squares procedures and (restricted) maximum likelihood; use of the Matérn correlation function. We furthermore discuss the spatial prediction uncertainty resulting from the different methods. The log-transformed data showed Gaussian uni- and bivariate distributions, and pronounced trends. Robust estimation techniques were not required, and anisotropic variation was not evident. Restricted maximum likelihood estimation versus the method-of-moments variogram of the residuals accounted for considerable differences in covariance parameters, whereas the Matérn and standard models gave very similar results. In the framework of spatial prediction, the parameter differences were mainly reflected in the spatial connectivity of the Ks field. Ignoring the trend component and an arbitrary use of robust estimators would have the most severe consequences in this respect. Our results highlight the superior importance of a thorough exploratory data analysis and proper variogram modeling, and prompt us to encourage restricted maximum likelihood estimation, which is accurate in estimating fixed and random effects.