One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial, and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effects of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of 100 independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), α‐lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation, but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.

The increased milk production of pastured‐based mixed dairy systems has involved higher use of concentrates and inputs, which led to increased environmental impacts. Usually these impacts are expressed per unit of product, which does not reflect the local impact per unit of land. The objective of this paper was to estimate global and local environmental impacts per kg of milk and per hectare. Fossil energy use, greenhouse gas emissions, water ecotoxicity, nutrient aquatic eutrophication potential, and integrated standardised indices of environmental impacts (IEI) were estimated for 277 pastured‐based dairy systems in Uruguay, clustered in seven milk production groups. Dairy systems with higher milk production had lower greenhouse gas emissions but higher ecotoxicity per kg of milk than systems with lower milk production, and the integrated index did not change. In contrast, all the individual indicators and the integrated indices per hectare were higher for the higher milk production systems. The results suggest that increasing milk productivity did not reduce environmental impacts per kg, and increased impacts per ha. Management factors including type of diet (pasture vs concentrate), amount of inputs, and type of grazing system may be more relevant in determining environmental impacts of dairy systems than productivity per se.

Grassland and savanna ecosystems are the original vegetation types of more than 30% of the Brazilian territory, but conservation and training of future professionals has largely focused on forests. In fact, no standard protocols exist for sampling grassland vegetation for environmental planning or licensing. Neglecting non-forest ecosystems may have deleterious consequences for the maintenance of biodiversity and the provisioning of ecosystem services. Herein, we provide practical guidelines on how to conceive and develop ecological studies of grassland vegetation for scientific, monitoring or technical purposes. Using examples mostly from southern Brazil, we explain and discuss the various components of the research process, from the question to be investigated to defining and implementing the sampling design, performing data analyses and presenting the results. Our guidelines should prove useful for training technicians and researchers working on grassland ecosystems and other non-forest ecosystems in Brazil and surrounding regions.

Steers production in integrated crop-livestock systems: pasture management under different sward heights