Effects of diurnal temperature range and drought on wheat yield in Spain

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies

Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change

Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO2 (171 720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters. Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO2 environment may not exist. In conjunction with analyses of germplasm and local management practices, results such as this can identify the genetic resources needed to adapt to climate change

Estimating sowing and harvest dates based on the Asian summer monsoon

Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments. We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled general circulation models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. The application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate

Mapping vulnerability to multiple hazards in the Savanna Ecosystem in Ghana

The interior savannah ecosystem in Ghana is subjected to a number of hazards, including droughts, windstorms, high temperatures and heavy rainfall, the frequency and intensity of which are projected to increase during the 21st century as a result of climate variability and change. Vulnerabilities to these hazards vary, both spatially and temporally, due to differences in susceptibilities and adaptive capacities. Many mapping exercises in Ghana have considered the impacts of single hazards on single sectors, particularly agriculture. But the hazards often occur concurrently or alternately, and have varying degrees of impacts on different sectors. The impacts also interact. These interactions make mapping of the vulnerabilities of multiple sectors to multiple hazards imperative. This paper presents an analysis of the spatial dimension of vulnerabilities by mapping vulnerability of sectors that support livelihood activities at a single point in time, using the Upper East Region of Ghana as a case study. Data colected to develop the maps were largely quantitative and from secondary sources. Other data drew on fieldwork undertaken in the region from July - September 2013. Quantitative values were assigned to qualitative categorical data as the mapping process is necessarily quantitative. Data were divided into susceptibility and adaptive capacity indicators and mapped in ArcGIS 10.2 using weighted linear sum aggregation. Agriculture was found to be the most vulnerable sector in all districts of the Upper East Region and experienced the greatest shocks from all hazards. Although all districts were vulnerable, the Talensi, Nabdam, Garu-Temapane and Kassena-Nankana West Districts were most vulnerable. Findings highlight the need for more targeted interventions to build adaptive capacity in light of the spatial distributions of vulnerabilities to hazards across sectors

New modelling technique for improving crop model performance - Application to the GLAM model

Crop models simulate growth and development and they are often used for climate change applications. However, they have a variable skill in the simulation of crop responses to extreme climatic events. Here, we present a new dynamic crop modelling method for simulating the impact of abiotic stresses. The Simultaneous Equation Modelling for Annual Crops (SEMAC) uses simultaneous solution of the model equations to ensure internal model consistency within daily time steps; something that is not always guaranteed in the usual sequential method. The SEMAC approach is implemented in GLAM, resulting in a new model version (GLAM-Parti). The new model shows a clear improvement in skill under water stress conditions and it successfully simulates the acceleration of leaf senescence in response to drought. We conclude that SEMAC is a promising crop modelling technique that might be applied to a range of models

Enhanced Leaf Cooling Is a Pathway to Heat Tolerance in Common Bean

Common bean is the most consumed legume in the world and an important source of protein in Latin America, Eastern, and Southern Africa. It is grown in a variety of environments with mean air temperatures of between 14°C and 35°C and is more sensitive to high temperatures than other legumes. As global heating continues, breeding for heat tolerance in common bean is an urgent priority. Transpirational cooling has been shown to be an important mechanism for heat avoidance in many crops, and leaf cooling traits have been used to breed for both drought and heat tolerance. As yet, little is known about the magnitude of leaf cooling in common bean, nor whether this trait is functionally linked to heat tolerance. Accordingly, we explore the extent and genotypic variation of transpirational cooling in common bean. Our results show that leaf cooling is an important heat avoidance mechanism in common bean. On average, leaf temperatures are 5°C cooler than air temperatures, and can range from between 13°C cooler and 2°C warmer. We show that the magnitude of leaf cooling keeps leaf temperatures within a photosynthetically functional range. Heat tolerant genotypes cool more than heat sensitive genotypes and the magnitude of this difference increases at elevated temperatures. Furthermore, we find that differences in leaf cooling are largest at the top of the canopy where determinate bush beans are most sensitive to the impact of high temperatures during the flowering period. Our results suggest that heat tolerant genotypes cool more than heat sensitive genotypes as a result of higher stomatal conductance and enhanced transpirational cooling. We demonstrate that it is possible to accurately simulate the temperature of the leaf by genotype using only air temperature and relative humidity. Our work suggests that greater leaf cooling is a pathway to heat tolerance. Bean breeders can use the difference between air and leaf temperature to screen for genotypes with enhanced capacity for heat avoidance. Once evaluated for a particular target population of environments, breeders can use our model for modeling leaf temperatures by genotype to assess the value of selecting for cooler beans

South Asia river-flow projections and their implications for water resources

South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections

Invited Review: IPCC, Agriculture and Food - A Case of Shifting Cultivation and History.

Since 1990 the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed data base of the ca. 2,100 cited experiments and simulations in the five ARs were analysed with respect to impacts on yields via crop type, region and whether or not adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and non-production aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical and imminent issues in climate change, have been dealt with in an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modelling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and non-production aspects of food security in future roles for integrated assessment models

A Climate Smartness Index (CSI) Based on Greenhouse Gas Intensity and Water Productivity: Application to Irrigated Rice

Efforts to increase agricultural productivity, adapt to climate change, and reduce the carbon footprint of agriculture are reflected in a growing interest in climate-smart agriculture (CSA). Specific indicators of productivity, adaptation and mitigation are commonly used in support of claims about the climate smartness of practices. However, it is rare that these three objectives can be optimized simultaneously by any one strategy. In evaluating the relative climate smartness of different agricultural practices, plans and policies, there is a need for metrics that can simultaneously represent all three objectives and therefore be used in comparing strategies that have different benefits and trade-offs across this triad of objectives. In this context, a method for developing a Climate Smartness Index (CSI) is presented. The process of developing the index follows four steps: (1) defining system specific climate smartness; (2) selecting relevant indicators; (3) normalizing against reference values from a systematic literature review; and (4) aggregating and weighting. The CSI presented here has been developed for application in a systematic review of rice irrigation strategies and it combines normalized water productivity (WP) and greenhouse gas intensity (GHGI) The CSI was developed for application to data from published field experiments that assessed the impact of water management practices in irrigated rice, focusing on practices heralded as climate-smart strategies, such as Alternate Wetting and Drying (AWD). The analysis shows that the CSI can provide a consistent judgment of the treatments based on the evidence of water efficiency and reduced GHGI reported in such studies. Using a measurable and replicable index supports the aim of generating a reliable quantification of the climate smartness of agricultural practices. The same four step process can be used to build metrics for a broad range of CSA practice, policy and planning