Mitigation of nitrous oxide emissions in grazing systems through nitrification inhibitors: a meta-analysis

Grasslands are the largest contributor of nitrous oxide (N2O) emissions in the agriculture sector due to livestock excreta and nitrogen fertilizers applied to the soil. Nitrification inhibitors (NIs) added to N input have reduced N2O emissions, but can show a range of efficiencies depending on climate, soil, and management conditions. A meta-analysis study was conducted to investigate the factors that influence the efficiency of NIs added to fertilizer and excreta in reducing N2O emissions, focused on grazing systems. Data from peer-reviewed studies comprising 2164 N2O emission factors (EFs) of N inputs with and without NIs addition were compared. The N2O EFs varied according to N source (0.0001-8.25%). Overall, NIs reduced the N2O EF from N addition by 56.6% (51.1-61.5%), with no difference between NI types (Dicyandiamide-DCD; 3,4-Dimethylpyrazole phosphate-DMPP; and Nitrapyrin) or N source (urine, dung, slurry, and fertilizer). The NIs were more efficient in situations of high N2O emissions compared with low; the reduction was 66.0% when EF > 1.5% of N applied compared with 51.9% when EF 10 kg ha(-1). NIs were less efficient in urine with lower N content (<= 7 g kg(-1)). NI efficiency was negatively correlated with soil bulk density, and positively correlated with soil moisture and temperature. Better understanding and management of NIs can optimize N2O mitigation in grazing systems, e.g., by mapping N2O risk and applying NI at variable rate, contributing to improved livestock sustainability

Using temporal stability to estimate soya bean yield: a case study in Paraná state, Brazil

Crop identification is an important task in the process of yield estimation; however, sometimes it can be difficult when using images of medium to low spatial resolution due to mixing of heterogeneous areas within the pixel. Therefore, selecting pixels that best represent an area/crop could be an alternative to input data in yield estimate models. The objective of this study was to select soya bean pixels based on temporal stability technique and test the ability of these pixels to predict yield. The study was conducted at county level in Paraná state, Brazil, during 11 years of soya bean growing season. To estimate yield, we created a linear regression model and used accumulated enhanced vegetation index during four periods according to soya bean phenological stages, which are as follows: emergence to maturity, emergence to flowering, flowering to grain filling, and flowering to maturity. Among all periods of the crop season, emergence to flowering showed the lowest precision while flowering to maturity was the period with the best agreements when compared with official data; the root mean square error ranged from 0.07 to 0.37 t ha–1. The temporal stability method has proven to be an efficient tool to select pixel that could represent the crop for predicting yield. In addition, we could verify the most suitable period for making soya bean yield prediction.37512231242COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems

Assessment of yield gaps on global grazed-only permanent pasture using climate binning

To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)-the difference between current and potential yield of agricultural systems-indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed-only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree-days. We then estimate YGs from intra-bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed-only permanent pasture assessed, we find potential to increase productivity several-fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk-focused production systems were found to be seven times as productive as meat-focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or "spare" large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification26318201832FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2014/26767‐9; 2017/25023‐4; 2016/20307‐1; 2017/08970‐0; 2016/08741‐8; 2016/08742‐4; 2017/06037‐4; 2018/11052‐5We thank two anonymous reviewers for their valuable comments. We appreciate suggestions from Dr. Mario Herrero, Dr. Stephen Polasky, Dr. Marcelo Galdos, Dr. Jansle Rocha, W. Evan Sheehan, and Dr. Charles P. West in preliminary development of this work. We further thank Dr. Mario Herrero for providing permission and access to global livestock production data used in this study. Funding provided by FAPESP process nos 2014/26767‐9, 2017/25023‐4 and 2016/20307‐1, 2017/08970‐0, 2016/08741‐8, 2016/08742‐4, 2017/06037‐4, 2018/11052‐5, the Iola Hubbard Climate Change Endowment managed by the Earth Systems Research Center at the University of New Hampshire, and the National Science Foundation Graduate Research Fellowship under Grant No. DGE‐1313911. LL was supported by the Center for Bioenergy Innovation a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. AA was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE‐1313911. DJ acknowledges support from the National Science Foundation: Innovations at the Nexus of Food, Energy, and Water Systems under Grant EAR‐163932