Abstract
Integrated crop-livestock systems (ICLSs) can be a viable option for overcoming global issues of food security, climate change, and sustainable food production, as they are designed to explore synergies and emerging properties from interactions between soil-plant-animal-atmosphere of areas that integrate agriculture and livestock production activities. This Project is a continuation of Regular Research Projects (Grants FAPESP 2013/23853-9 and 2011/12155-3) and Scholarships (Grants FAPESP 2015/25413-1, 2014/21772-4, 2014/00772-6, 2014/14935-4, 2014/14840-3, 2014/12950-6, 2013/13702-3, 2012/04458-9, 2012/04562-0, 2011/03662-9, 2011/03898-2 and 2010/12992-0). We propose to evaluate how different ICLSs influence agricultural production of crops (grains and silage), pasture and sheep (feedlot lambs and semi-feedlot ewe), contribute to nutrient cycling and soil C and N accumulation, affect soil quality, and mitigate greenhouse gases emissions. The research will be conducted during two growing seasons at Botucatu, São Paulo, Brazil, in three subprojects. Subproject 1 - soybean silage intercropped with guineagrass in the summer and black oat as cover crop and managed with two ewe grazing intensities (i.e. sward height managed to 0.15 and 0.30 m - intensive and moderate grazing, respectively) and a control treatment without grazing from autumn to spring; Subproject 2 - comparison of four soybean silage diets produced in Subproject 1 for feeding lambs (0, 20, 40, and 60% based on dry matter); Subproject 3 - comparison of sorghum grain crop produced in autumn solely or intercropped with (a) palisadegrass, (b) pigeon pea cv. Iapar 43, and (c) palisadegrass and pigeon pea cv. Iapar 43 following soybean in summer. Subproject 3 will be conducted in an eucalyptus agroforestry system. Data will be analyzed using the UNIVARIATE, MIXED, REG and MEAN SAS procedures (version 9.4; SAS Inst Inc., Cary, NC, USA) and submitted to analysis of normality and variance. Mean values will be adjusted by the least square method and used in the analysis of multiple comparisons (P d 0.05). (AU)
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