Environmental characterisation to guide breeding decisions in a changing climate

Substantial evidence now exists suggesting that agricultural yields will have to increase significantly in order to meet food needs during the 21st century. One such way of increasing yields is to develop high yielding cultivars through crop improvement. This Working Paper summarises the results of a CCAFS project named Target Population of Environments (TPE). The project aimed at providing actionable information to crop breeders and, therefore, inform breeding decisions. We developed and applied a methodology for classifying crop growing environments, determining stress profiles and, finally, assessing the potential benefit of improved breeding practice. We present two contrasting case studies, one for upland rice in central Brazil and another for common beans in Goiás (Brazil). Analyses are also currently being conducted for lowland irrigated rice in Colombia, and plans to conduct research on rice in sub-Saharan Africa. Results of the TPE project are publicly available in the form of dynamic maps and graphs at http://www.ccafs-tpe.org

Climate change determined drought stress profiles in rainfed common bean production systems in Brazil

Reductions in agricultural productivity with consequences for food security associated to climate change are expected in the absence of adaptation. For common beans, across South America, a decrease in climatic suitability has been projected, with heat and drought stresses being the key drivers for such suitability reductions. Breeding programs will play an important role in the adaptation of common beans to the changing climates. However, breeding targets may vary as climate changes during the 21st century. Here, we assess historical and future (2030) probabilities of occurrence, intensity and impact of seasonal variations of drought stress, which is the most important stress for common beans in the Goiás state. We focus on two rainfed (wet and dry) target population environments (TPEs), which encompass ca. 62% of the bean cropped area in the state for 2016, and address potential breeding implications of future projected changes. The analysis revealed two environment groups for both TPEs (highly favorable environment and favorable environment), and four drought stress profiles within these environmental groups (drought stress free, reproductive stress, terminal stress, and joint reproductive-terminal stress) across all climate and management (cultivars and sowing dates) scenarios. Results suggest that, with respect to the historical (1980–2005) period, climate change will make drought more frequent, but less severe, across the region. For the dry TPE, the probability of occurrence of drought stress situations (reproductive and/or terminal) changes from 29.6% (baseline) to ca. 70% (2030, RCP [Representative Concentrations Pathway] 8.5), whereas for the wet TPE, it increases from 16% (baseline) to ca. 43% (2030, RCP 8.5). Results are consistent across RCPs, although benefits from stringent (RCP 2.6) mitigation are evident. We conclude that drought tailoring under climate change is needed for the Embrapa dry bean breeding program

Estimating the abundance of marine mammal populations

Support for this project was provided by the Lenfest Ocean Program.Motivated by the need to estimate the abundance of marine mammal populations to inform conservation assessments, especially relating to fishery bycatch, this paper provides background on abundance estimation and reviews the various methods available for pinnipeds, cetaceans and sirenians. We first give an “entry-level” introduction to abundance estimation, including fundamental concepts and the importance of recognizing sources of bias and obtaining a measure of precision. Each of the primary methods available to estimate abundance of marine mammals is then described, including data collection and analysis, common challenges in implementation, and the assumptions made, violation of which can lead to bias. The main method for estimating pinniped abundance is extrapolation of counts of animals (pups or all-ages) on land or ice to the whole population. Cetacean and sirenian abundance is primarily estimated from transect surveys conducted from ships, small boats or aircraft. If individuals of a species can be recognized from natural markings, mark-recapture analysis of photo-identification data can be used to estimate the number of animals using the study area. Throughout, we cite example studies that illustrate the methods described. To estimate the abundance of a marine mammal population, key issues include: defining the population to be estimated, considering candidate methods based on strengths and weaknesses in relation to a range of logistical and practical issues, being aware of the resources required to collect and analyze the data, and understanding the assumptions made. We conclude with a discussion of some practical issues, given the various challenges that arise during implementation.Publisher PDFPeer reviewe

Agricultural intensification can help protect the Amazon Forest and reduce global warming / Protecting the Amazon forest and reducing global warming via agricultural intensification

The Amazon basin includes 550 M ha covered with rainforests, with 60% of this area being in Brazil. Conversion of rainforest for soybean production raises concerns about the degree to which Brazil can reconcile production and environmental goals. Here we investigated the degree to which intensification could help Brazil produce more soybean without further encroachment of the Amazon Forest. Our analysis shows that continuation of current trends in soybean yield and area would lead to conversion of additional 5.7 M ha of forests and savannas during the next 15 years, with an associated 2550 Mt of CO2eq released into the atmosphere. In contrast, acceleration of yield improvement, coupled with expansion of soybean area only in areas currently used for livestock production, would allow Brazil to achieve similar economic benefits without deforestation and with substantially lower global climate warming

Robustness of potential biological removal to monitoring, environmental, and management uncertainties

Support for this project was provided by the Lenfest Ocean Program.The potential biological removal (PBR) formula used to determine a reference point for human-caused mortality of marine mammals in the United States has been shown to be robust to several sources of uncertainty. This study investigates the consequences of the quality of monitoring on PBR performance. It also explores stochastic and demographic uncertainty, catastrophic events, sublethal effects of interactions with fishing gear, and the situation of a marine mammal population subject to bycatch in two fisheries, only one of which is managed. Results are presented for two pinniped and two cetacean life histories. Bias in abundance estimates and whether there is a linear relationship between abundance estimates and true abundance most influence conservation performance. Catastrophic events and trends in natural mortality have larger effects than environmental stochasticity. Managing only one of two fisheries with significant bycatch leads, as expected, to a lower probability of achieving conservation management goals, and better outcomes would be achieved if bycatch in all fisheries were managed. The results are qualitatively the same for the four life histories, but estimates of the probability of population recovery differ.Publisher PDFPeer reviewe

Estimating bycatch mortality for marine mammals : concepts and best practices

Support for this project was provided by the Lenfest Ocean Program (Contract ID: #31008).Fisheries bycatch is the greatest current source of human-caused deaths of marine mammals worldwide, with severe impacts on the health and viability of many populations. Recent regulations enacted in the United States under the Fish and Fish Product Import Provisions of its Marine Mammal Protection Act require nations with fisheries exporting fish and fish products to the United States (hereafter, “export fisheries”) to have or establish marine mammal protection standards that are comparable in effectiveness to the standards for United States commercial fisheries. In many cases, this will require estimating marine mammal bycatch in those fisheries. Bycatch estimation is conceptually straightforward but can be difficult in practice, especially if resources (funding) are limiting or for fisheries consisting of many, small vessels with geographically-dispersed landing sites. This paper describes best practices for estimating bycatch mortality, which is an important ingredient of bycatch assessment and mitigation. We discuss a general bycatch estimator and how to obtain its requisite bycatch-rate and fisheries-effort data. Scientific observer programs provide the most robust bycatch estimates and consequently are discussed at length, including characteristics such as study design, data collection, statistical analysis, and common sources of estimation bias. We also discuss alternative approaches and data types, such as those based on self-reporting and electronic vessel-monitoring systems. This guide is intended to be useful to managers and scientists in countries having or establishing programs aimed at managing marine mammal bycatch, especially those conducting first-time assessments of fisheries impacts on marine mammal populations.Publisher PDFPeer reviewe

Rice Management Decisions Using Process-Based Models With Climate-Smart Indicators

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011