This work evaluated N dynamics that occurs over time within swine slurry composting piles. Real-time quantitative PCR (qPCR) analyzes were conducted to estimate concentrations of bacteria community harboring specific catabolic nitrifying-ammonium monooxygenase (amoA), and denitrifying nitrate- (narG), nitrite- (nirS and nirG), nitric oxide- (norB) and nitrous oxide reductases (nosZ) genes. NH3-N, N2O-N, N2-N emissions represented 15.4 ± 1.9%, 5.4 ± 0.9%, and 79.1 ± 2.0% of the total nitrogen losses, respectively. Among the genes tested, temporal distribution of narG, nirS, and nosZ concentration correlated significantly (p<0.05) with the estimated N2 emissions. Denitrifying catabolic gene ratio (cnorB+qnorB)/nosZ ≥ 100 was indicative of N2O emission potential from the compost pile. Considering our current empirical limitations to accurately measure N2 emissions from swine slurry composting at field scale the use of these catabolic genes could represent a promising monitoring tool to aid minimize our uncertainties on biological N mass balances in these systems.

A common agricultural problem in many regions of Brazil is maize lodging, as a consequence of strong winds and rain which impacts on crop growth and yield. However, collecting data using ground-based, manual field measurement methods is inefficient. An emerging tool is the Remotely Piloted Aircraft System (RPAS), capable of delivering spatial data with high resolution and flexible periodicity. In this study, the potential to detect the maize lodging using crop surface models derived from RPAS was assessed. Our RPA-based approach uses a quantitative threshold to determine lodging percentage.The threshold values of plant height, used to detect the occurrence of lodging, were based on fixed and variable values. The validation of percentage lodging was performed using the RGB orthomosaic. The derived lodging estimates showed a very high correlation to the reference data. High correlations were observed for the fixed threshold at 60% (R² = 0.93, RSME = 8.72%) and the variable thresholds, Jenks natural breaks and iso-clusters (R² = 0.92, RSME = 8.89% and R²= 0.92, RSME = 9%, respectively). This study demonstrated the potential of the use of this technique, reducing the subjectivity of ground-based evaluation and the laborious traditional technique of lodging inference.

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Coffee is a crop of great relevance in socioeconomic terms for Brazilian agribusiness, which is the world’s largest producer in cultivated areas. The implementation of precision agriculture in the coffee culture has provided countless benefits to its development, which over the years has been cultivated in the same area. However, there is a lack of studies that address the impact of the application of variable-rates inputs in soil on the energy efficiency and sustainability of these systems. This study aimed to analyze how variable-rate fertilization influences energy efficiency in coffee growing. A production area subjected to variable and fixed rates of fertilizer in alternating rows was evaluated. Geo-referenced yield data was collected to assess yield response for fixed and variable rate applications. The energy assessment was combined with the Geographic Information System (GIS) to determine site-specific energy indicators. To determine the energy flow, only NPK fertilizer applications were considered as inputs and the yield as output. The results obtained indicated that the variable rate fertilizer application has a small difference, indicating greater energy efficiency concerning the applied fertilizer and coffee production per crop season. It was observed in the 06/07 crop, the incorporated energy was 10.7 MJ kg−1 for VR and 10.2 MJ kg−1 for UR and for the 07/08 crop it was 30.7 MJ kg−1 for VR and 34.9 MJ kg−1 for UR. The energy balance was more efficient at variable rates, as it provided fertilizer savings without compromising yield. However, the difference between the embodied energy per mass of coffee produced was very small compared to the fixed rate.

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with a concentration in Socio-environmental Performance in Production Processes, which she finished in May 2012. The dissertation produced the paper: Correlation of denitrifying catabolic genes with N2O and N2 emissions during swine slurry composting,