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.

Adopting no‐till (NT) after conventional tillage (CT) could affect surface runoff, but this effect may also depend on the rotation type, such as continuous grain crop (CC) or crop–pasture rotation (CP). The objective of this study was to assess the effects of soil‐tillage practices and rotation type on runoff in a long‐term field (LTF) trial established in 1993 on a Mollisol. This experiment was carried from 2012 to 2017 and included three tillage rotations (NT–CP, NT–CC, and CT–CP) and a natural pasture (NP) as a control, with three replications. Runoff was monitored under natural rainfall in microplots. The pasture of the CP treatment was rotationally grazed with dairy cows at high stocking rates before this experiment. We hypothesized that soil compaction by animal trampling during grazing would increase surface runoff regardless of tillage type. We measured the number of events and runoff depth in all treatments, grain yield, soil bulk density (BD), and infiltration rate (IR) in NT–CP and NT–CC. The results showed a statistically significant increase in average runoff (107 mm yr ^–1 ) for all rotations relative to NP (68 mm yr ^–1 ) and a significant difference for the mean of both CP rotations (119 mm yr ^–1 ) compared with NT–CC (83 mm yr ^–1 ). During the crop phase (excluding the pasture), the two NT rotations showed a similar trend, but with higher BD (1.23 g cm ^–3 ) and lower IR (27.2 mm h ^–1 ) for NT–CP. We concluded that animal trampling increased surface runoff regardless of tillage, but this effect would need to be confirmed at other sites.

Biofuels play an important role in the reduction of greenhouse gas emissions, but their production results in greenhouse gases such as nitrous oxide (N₂O), mainly from nitrogen (N) fertilization. Brassica carinata (carinata) is an unexplored winter crop in the world’s main cropping areas, with multiple applications (cover crop, jet biofuel, and animal feed, among others). We analyzed a set of on-farm fertilization experiments (2016–2018) in southern South America using quantile regression to establish a baseline for both seed yield (Y_SEED) and the different components of nitrogen-use efficiency (NUE). Maximum Y_SEED for the 50th and 90th percentiles ranged from 2.5 to 3.5 Mg ha⁻¹, with a N availability of 150–160 kg ha⁻¹ (soil + fertilization). The NUE ranged from 3 to 13 kg seed per kg of N available. Carinata, in the absence of other limiting factors, had a high N uptake (N_UPT) capacity (1.0 to 1.5 kg ha⁻¹ N_UPT per kg ha⁻¹ N available [soil + fertilization]). The explored N fertilization rates had no significant influence on oil concentrations (455 to 517 g kg⁻¹) and protein concentrations (192 to 253 g kg⁻¹). The region has a high potential for carinata production, with a high capacity to take up available N.

Current nitrogen (N) fertilization schedule for spring wheat was developed under a dominant crop-pasture rotation. After the year 2002, this cropping system was converted to continuous annual cropping systems under no-till, reducing soil N supply capacity progressively. Additionally, highest grain yield of new varieties increased N demand. The required additional N fertilizer can be adjusted by monitoring nutritional status of the crop. Our objectives were: i) to determine optimal N status at different phenological stages; ii) to quantify the wheat yield gap explained by N supply deficit, and iii) to assess the critical nitrogen nutrition index (NNI) value as a predictor of response to N fertilizer applied at GS 3.3. We adjusted the nitrogen dilution curve (Nc=4.17DM-0.31), deriving a critical NNI at GS 3.3 (NNI=1.24). Depending on soil N supply capacity and NNI at GS 3.3, wheat yield gap attributed to N supply deficit varied from 0 to 2.74 Mg ha-1, averaging 0.76 Mg ha-1. The critical NNI proposed at GS 3.3 was effective to diagnose the N crop demand to reach the attainable yield under different scenarios.