Abstract:Increasing global demand for oil seeds and cereals during the past 50 years has caused an expansion in the cultivated areas and resulted in major soil management and crop production changes throughout Bolivia, Paraguay, Uruguay, Argentina and southern Brazil.

Spring soil temperature and soil water content can be influenced by tillage system. If a tillage system and planting date interaction exists, planting on a single date, as is done in most tillage trials, could bias yield results. We tested for this interaction by comparing corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields using strip tillage, no tillage, and disk‐chisel tillage systems with planting dates determined by soil temperature and water content conditions within each tillage system. A 3‐yr study (2002–2004) was conducted on a site near Newton, IA that had three soil types: Cumulic Hapludolls, Aquic Hapludolls, and Cumulic Haplaquolls. A split‐plot design was used with tillage as whole plots arranged in four randomized complete blocks. Crops in all tillage system treatments were planted on three dates that comprised the split‐plots. The criteria to determine the planting dates were soil temperature (>10°C for corn and >13°C for soybean for 12 consecutive hours) and soil water content (less than or equal to the lower plastic limit for any of the tillage treatments) at the 0.05‐m depth. A planting date occurred for each of the tillage systems as these criteria were met. For both crops, the earliest date having these soil conditions occurred simultaneously for disk‐chisel and strip tillage. The no‐tillage plots exhibited these conditions between 4 and 28 d and between 6 and 15 d later for corn and soybean, respectively, than for the other tillage treatments. Corn planted with disk‐chisel tillage yielded 0.8 Mg ha−1 more than the mean of the other two tillage treatments across years. Planting date affected corn yield only in 2003. For soybean, planting date affected yield. Soybean planted at the early planting date yielded 0.16 Mg ha−1 more than the mean of the other planting dates across years. There was no interaction of tillage × planting date for yield of either crop. This research indicates that recommendations derived from existing tillage research using a common planting date are valid.

Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990–2018.

The Water Erosion Prediction Project (WEPP) model has been tested for its ability to predict soil erosion, runoff, and sediment delivery over a wide range of conditions and scales for both hillslopes and watersheds. Since its release in 1995, there has been considerable interest in adding a chemical transport element to it. Total phosphorus (TP) loss at the watershed outlet was simulated as the product of TP in the soil, amount of sediment at the watershed outlet, and an enrichment ratio (ER) factor. WEPP can be coupled with a simple algorithm to simulate phosphorus transport bound to sediment at the watershed outlet. The objective of this work was to incorporate and test the ability of WEPP in estimating TP loss with sediment at the small watershed scale. Two approaches were examined. One approach (P‐EER) estimated ER according to an empirical relationship; the other approach used the ER calculated by WEPP (P‐WER). The data used for model performance test were obtained from two side‐by‐side watersheds monitored between 1976 and 1980. The watershed sizes were 5.05 and 6.37 ha, and each was in a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Measured and simulated results were compared for the period April to October in each year. There was no statistical difference between the mean measured and simulated TP loss. The Nash–Sutcliffe coefficient was 0.80 and 0.78 for the P‐EER and P‐WER methods, respectively. It was critical for both methods that WEPP adequately represent the biggest sediment yield events because sediment is the main driver for TP loss so that the model can adequately simulate TP losses bound to sediment. The P‐WER method is recommended because it does not require use of empirical parameters to estimate TP loss at the watershed outlet.

Se parte de la información experimental obtenida en parcelas de escurrimiento bajo lluvia natural en tres sitios con Argiudoles en Uruguay: 1