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.bitstream/item/136525/1/abh.pd

Moho Depth of Northern Baja California, Mexico, From Teleseismic Receiver Functions

We estimated Moho depths from data recorded by permanent and temporary broadband seismic stations deployed in northern Baja California, Mexico, using the receiver function technique. This region is composed of two subregions of contrasting geological and topographical characteristics: The Peninsular Ranges of Baja California (PRBC), a batholith with high elevations (up to 2600 m); and the Mexicali Valley (MV) region, a sedimentary environment close to sea level. Crustal thickness derived from the P‐to‐S converted phases at 29 seismic stations were analyzed in 3 profiles: two that cross the two subregions, in ∼W‐E direction, and the third one that runs over the PRBC in a N‐S direction. For the PRBC, Moho depths vary from 35 to 45‐km, from 33ºN to 32ºN; and from 30 to 46‐km depth from 32ºN to 30.5ºN. From a profile that crosses the subregions in the W‐E direction; Moho depths vary from 45 to ∼34‐km under western and eastern PRBC, respectively; with an abrupt change of depth under the Main Gulf Escarpment (30º), from ∼32 to 30‐km; and depths of 17‐20‐km under the MV. Moho depths of the profile in an ∼W‐E direction at ∼31.5ºN from ∼30 to 40‐km, under topography that increase from 0 to 2600 m; and became shallower (16‐km depth) as the profile reaches the Gulf of California. These results show that deeper Moho is related to higher elevations with an abrupt change under the Main Gulf Escarpment, except for western PRBC were the Moho depth is not simply reflect isostatic compensation

A low-voltage low-power front-end for wearable EEG systems

A low-voltage and low-power front-end for miniaturized, wearable EEG systems is presented. The instrumentation amplifier, which removes the electrode drift and conditions the signal for a 10-bit A/D converter, combines a chopping strategy with quasi-FGMOS (QFG) transistors to minimize low frequency noise whilst enabling operation at 1 V supply. QFG devices are also key to the A/D converter operating at 1.2 V with 70dB of SNR and an oversampling ratio of 64. The whole system consumes less than 2uW at 1.2V.Published versio

Moho Depth of Northern Baja California, Mexico, From Teleseismic Receiver Functions

We estimated Moho depth from data recorded by permanent and temporary broadband seismic stations deployed in northern Baja California, Mexico using the receiver function technique. This region is composed, mainly, of two subregions of contrasting geological and topographical characteristics: The Peninsular Ranges of Baja California (PRBC), a batholith with high elevations (up to 2600 m above mean sea level); and the Mexicali Valley (MV) region, a sedimentary environment at around the mean sea level. Crustal thickness derived from the P-to-S converted phases at 29 seismic stations were analyzed in 3 profiles: two that cross the two subregions, in a ~W-E direction, and the third one that runs over the PRBC in a N-S direction. For the PRBC region, Moho depths vary from 35 to 45 km, from 33°;N to 32°;N; and from 30 to 46 km depth from 32°;N to 30.5°;N. From a profile that crosses the subregions in the W-E direction; Moho depths vary from 45 to ~34 km under the PRBC; with an abrupt change of depth under the Main Gulf Escarpment, from ~32 to 30 km; and depths of 17-20 km under the MV region. Moho depths of the profile that runs, of an almost W-E direction at ~31.5°; N, follow the eltimetry from 0 to 2600 m: from ~30 to 40 km; and became shallower (16 km depth) as the profile reaches the Gulf of California. These results show that deeper Moho is related to higher elevations with an abrupt change under the Main Gulf Escarpment

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

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production

Increasing global CO₂ emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO₂ world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production

Design of a Soil-based Climate-Smartness Index (SCSI) using the trend and variability of yields and soil organic carbon

Climate-Smart Agriculture (CSA) has had an increasing role in the agricultural policy arena, as it aims to address climate change mitigation, adaptation and food security goals in an integrated way. In regions where agriculture has been constrained because of soil degradation and climate change, CSA aims to implement soil-based strategies that restore soil function and increase carbon storage. The extent to which such strategies succeed in achieving mitigation, adaptation and productivity goals is referred to as climate-smartness. The co-evolution of yield and Soil Organic Carbon (SOC) over the years presents a proxy for the trade-off between productivity, soil fertility and carbon sequestration. Yield and SOC are widely monitored, analysed and used to inform CSA planning. Yet their analysis is often conducted separately and for a small number of years, which neglects long-term soil fertility dynamics and their impact on crops. Given the absence of integrated climate-smartness metrics to capture the trade-offs and synergies between SOC and yield, we present a soil-based Climate-Smartness Index (SCSI). The SCSI is computed using normalized indicators of trend and variability of annual changes on yield and SOC data. The SCSI was calculated for a set of published experiments that compared Conservation Agriculture (CA) practices with conventional management. The CA treatments scored higher SCSI during the first 5 years of evaluation as compared to conventional management. Analysis of the temporal dynamics of climate-smartness indicated that minimum SCSI values typically occurred before 5 years after the start of the experiment, indicating potential trade-offs between SOC and yield. Conversely, SCSI values peaked between 5 and 10 years. After 20 years, the SCSI tended towards zero, as substantial changes in either SOC or yield are no longer evidenced. The SCSI can be calculated for annual crops under any soil management and at different time periods, providing a consistent metric for climate-smartness across both practices and time

Determination of the angular momentum of the Kerr black hole from equatorial geodesic motion

We present a method to determine the angular momentum of a black hole, based on observations of the trajectories of the bodies in the Kerr space-time. We use the Hamilton equations to describe the dynamics of a particle and present results for equatorial trajectories, obtaining an algebraic equation for the magnitude of the black hole's angular momentum. We tailor a numerical code to solve the dynamical equations and use it to generate synthetic data. We apply the method in some representative examples, obtaining the parameters of the trajectories as well as the black hole's angular momentum in good agreement with the input data.Comment: 24 page

A new framework for predicting and understanding flowering time for crop breeding

As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation.Summary•There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments.•Meeting this challenge will require a clear understanding of how existing germ-plasm behave across Genotype∗Environment (G∗E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accu-rately simulate genotypic variation in key traits across target population of environments.•Accordingly, we develop a simple machine learning framework for modelling time to flowering across G∗E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learn-ing models and find that the best performing models display high levels of accu-racy across G∗E.•We advance understanding of the environmental drivers of flowering time inequatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models

Genetic diversity of the rain tree (Albizia saman) in Colombian seasonally dry tropical forest for informing conservation and restoration interventions

Albizia saman is a multipurpose tree species of seasonally dry tropical forests (SDTFs) of Mesoamerica and northern South America typically cultivated in silvopastoral and other agroforestry systems around the world, a trend that is bound to increase in light of multimillion hectare commitments for forest and landscape restoration. The effective conservation and sustainable use of A. saman requires detailed knowledge of its genetic diversity across its native distribution range of which surprisingly little is known to date. We assessed the genetic diversity and structure of A.saman across twelve representative locations of SDTF in Colombia, and how they may have been shaped by past climatic changes and human influence. We found four different genetic groups which may be the result of differentiation due to isolation of populations in preglacial times. The current distribution and mixture of genetic groups across STDF fragments we observed might be the result of range expansion of SDTFs during the last glacial period followed by range contraction during the Holocene and human‐influenced movement of germplasm associated with cattle ranching. Despite the fragmented state of the presumed natural A. saman stands we sampled, we did not find any signs of inbreeding, suggesting that gene flow is not jeopardized in humanized landscapes. However, further research is needed to assess potential deleterious effects of fragmentation on progeny. Climate change is not expected to seriously threaten the in situ persistence of A. saman populations and might present opportunities for future range expansion. However, the sourcing of germplasm for tree planting activities needs to be aligned with the genetic affinity of reference populations across the distribution of Colombian SDTFs. We identify priority source populations for in situ conservation based on their high genetic diversity, lack or limited signs of admixture, and/or genetic uniqueness