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In Brazil, the livestock systems are mainly forage‐based, and nitrogen inputs are an uncommon practice, despite its great benefits. The objective was to evaluate the effect of nitrogen input on the physiological responses of Marandu palisadegrass (Urochloa brizantha [Hochst. ex A. Rich.] R. D. Webster) and Mombaça guineagrass (Megathyrsus maximus [Jacq.] B. K. Simon & S. W. L. Jacobs), and its impacts on the forage accumulation rate (FAR). The experiment was carried out in Sinop, MT, Brazil, in a randomized block design with a 2 × 2 × 5 factorial arrangement (two cultivars: Mombaça and Marandu; unfertilized and fertilized [50 kg N ha−1 cycle−1]; and five seasons: autumn 2015, winter 2015, spring 2015, summer 2015 and autumn 2016) with three replicates. The experimental period was from March 2015 to June 2016, when plots were harvested to mimic intermittent stocking. Fertilized Mombaça presented greater FAR pastures (74.0 kg DM ha−1 day−1) than unfertilized (26.6 kg DM ha−1 day−1) and Marandu fertilized (52.5 kg DM ha−1 day−1), with the greatest values during the summer (147.6 kg DM ha−1 day−1). The greatest leaf (A) and canopy photosynthesis (CP) rates occurred on fertilized pastures during the summer 2015 and autumn 2015. Marandu presented 19% greater A than Mombaça. During the winter, under water stress, Marandu had greater water use efficiency (WUE). However, during the summer 2015 and autumn 16, Mombaça had the greatest WUE (12.76 μmol CO2/mol H2O). In all seasons, Marandu presented the greatest chlorophyll index, which may support the greatest photosynthetic rates. It was concluded that Mombaça was highly responsive to nitrogen input during the rainy season with accumulation over 140 kg DM ha−1 day−1 and is recommended to intensified systems. Marandu had the greatest WUE in the lower precipitation seasons and is an alternative to a year‐round strategy in forage‐based systems. [ABSTRACT FROM AUTHOR]

Warm-season grasses are economically important for cattle production in tropical regions and tools to aid in management and research on these forages would be highly beneficial both in research and the industry. This research was conducted to adapt the CROPGRO-Perennial Forage model to simulate growth of the tropical species guineagrass (Panicum maximum Jacq. cv. 'Tanzânia') and to describe model adaptation for this species. To develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation, and partitioning during a 17-mo experiment with Tanzania guineagrass in Piracicaba, SP, Brazil. Compared with starting parameters for palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. 'Xaraes'], dormancy effects of the perennial forage model had to be minimized, partitioning to storage tissue or root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield was 6576 kg ha-1, averaged across 11 regrowth cycles of 35 (summer) or 63 d (winter), with a RMSE of 494 kg ha-1 (Willmott's index of agreement d = 0.985, simulated/observed ratio = 1.014). The model also gave good predictions against an independent data set, with similar RMSE, ratio, and d. The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of guineagrass and can be used to simulate growth. [ABSTRACT FROM AUTHOR]

Water deficit is a major factor responsible for soybean yield gap in Southern Brazil and tends to increase under climate change. An alternative to reduce such gap is to identify soybean cultivars with traits associated to drought tolerance. Thus, the aim of this study was to assess soybean adaptive traits to water deficit that can improve yield under current and future climates, providing guidelines for soybean cultivar breeding in Southern Brazil. The following soybean traits were manipulated in the CSM-CROPGRO-Soybean crop model: deeper root depth in the soil profile; maximum fraction of shoot dry matter diverted to root growth under water stress; early reduction of transpiration under mild stress; transpiration limited as a function of vapor pressure deficit; N 2 fixation drought tolerance; and sensitivity of grain filling period to water deficit. The yields were predicted for standard and altered traits using climate data for the current (1961–2014) and future (middle-century) scenarios. The traits with greater improvement in soybean yield were deeper rooting profile, with yield gains of ≈300 kg ha −1 , followed by transpiration limited as a function of vapor pressure deficit and less drought-induced shortening of the grain filling period. The maximum fraction of shoot dry matter diverted to root and N 2 fixation drought tolerance increased yield by less than 75 kg ha −1 , while early reduction of transpiration resulted in a small area of country showing gains. When these traits were combined, the simulations resulted in higher yield gains than using any single trait. These results show that traits associated with deeper and greater root profile in the soil, reducing transpiration under water deficit more than photosynthesis, creating tolerance of nitrogen fixation to drought, and reducing sensitivity of grain filling period to water deficit should be included in new soybean cultivars to improve soybean drought tolerance in Southern Brazil. [ABSTRACT FROM AUTHOR]

Common bean production in Goiás, Brazil is concentrated in the same geographic area, but spread across three distinct growing seasons, namely, wet, dry and winter. In the wet and dry seasons, common beans are grown under rainfed conditions, whereas the winter sowing is fully irrigated. The conventional breeding program performs all varietal selection stages solely in the winter season, with rainfed environments being incorporated in the breeding scheme only through the multi environment trials (METs) where basically only yield is recorded. As yield is the result of many interacting processes, it is challenging to determine the events (abiotic or biotic) associated with yield reduction in the rainfed environments (wet and dry seasons). To improve our understanding of rainfed dry bean production so as to produce information that can assist breeders in their efforts to develop stress-tolerant, high-yielding germplasm, we characterized environments by integrating weather, soil, crop and management factors using crop simulation models. Crop simulations based on two commonly grown cultivars (Pérola and BRS Radiante) and statistical analyses of simulated yield suggest that both rainfed seasons, wet and dry, can be divided in two groups of environments: highly favorable environment and favorable environment. For the wet and dry seasons, the highly favorable environment represents 44% and 58% of production area, respectively. Across all rainfed environment groups, terminal and/or reproductive drought stress occurs in roughly one fourth of the seasons (23.9% for Pérola and 24.7% for Radiante), with drought being most limiting in the favorable environment group in the dry TPE. Based on our results, we argue that even though drought-tailoring might not be warranted, the common bean breeding program should adapt their selection practices to the range of stresses occurring in the rainfed TPEs to select genotypes more suitable for these environments. [ABSTRACT FROM AUTHOR]