Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation

Increased understanding of the substantial threat climate change poses to agriculture has not been met with a similarly improved understanding of how best to respond. Here we examine likely shifts in crop climates in Sub-Saharan Africa under climate change to 2050, and explore the implications for agricultural adaptation, with particular focus on identifying priorities in crop breeding and the conservation of crop genetic resources. We find that for three of Africa's primary cereal crops – maize, millet, and sorghum – expected changes in growing season temperature are considerable and dwarf changes projected for precipitation, with the warmest recent temperatures on average cooler than almost 9 out of 10 expected observations by 2050. For the “novel” crop climates currently unrepresented in each country but likely extant there in 2050, we identify current analogs across the continent. The majority of African countries will have novel climates over at least half of their current crop area by 2050. Of these countries, 75% will have novel climates with analogs in the current climate of at least five other countries, suggesting that international movement of germplasm will be necessary for adaptation. A more troubling set of countries – largely the hotter Sahelian countries – will have climates with few analogs for any crop. Finally, we identify countries, such as Sudan, Cameroon, and Nigeria, whose current crop areas are analogs to many future climates but that are poorly represented in major genebanks – promising locations in which to focus future genetic resource conservation efforts.     

Climate change adaptation advantage for African road infrastructure

The African continent is facing the potential of a $183.6 billion USD liability to repair and maintain roads damaged from temperature and precipitation changes directly related to predicted climate change through 2100. This cost is strictly to retain the current road inventory. This cost does not include costs associated with impacts to critically needed new roads. In many African countries, limited or non-existent funds for adaptation and mitigation are challenging these countries to identify the threats that are posed by climate change, develop adaptation approaches to the predicted changes, incorporate changes into mid-range and long-term development plans, and secure funding for the proposed and necessary adaptations. Existing studies have attempted to quantify the impact of climate change on infrastructure assets that will be affected by climate change in the coming decades. The current study extends these efforts by specifically addressing the effect of climate change on the African road infrastructure. The study identifies both total costs and opportunity costs of repairing and maintaining infrastructure due to increased stressors from climate change. Proactive and reactive costs are examined for six climate scenarios, with costs ranging, respectively, from an average of $22 million USD to $54 million USD annually per country. A regional analysis shows contrast between impacts in five areas of the continent, with impacts ranging from 22 % opportunity cost to 168 %. These costs have the potential to delay critical infrastructure development on the continent and present a challenge to policy makers balancing short-term needs with long-term planning.     

Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

Informing national adaptation for sustainable development through spatial systems modelling

Acute climate-change hazards, such as floods or storm surges, can affect a nation’s built and natural environment assets that are critical for development and achievement of the Sustainable Development Goals (SDGs). To reduce the impacts of such acute climate-change hazards and safeguard development, national decision-makers require evidence on where and how hazards affect SDG achievement to better inform adaptation. Here, we develop a systems methodology that spatially models the impacts of climate-change hazards across a nation’s entire built and natural environment assets and its interdependent influences on the SDG targets to inform national adaptation. We apply our methodology in Saint Lucia through a participatory approach with decision-makers across 18 government ministries, academia, and the private sector. Results reveal that acute climate-change hazards can affect half of Saint Lucia’s assets across 22 sectors, which can influence 89% of all SDG targets. Application of our methodology provided evidence on where and how to prioritise adaptation, thereby helping to add spatial granularity to 52 measures under Saint Lucia’s National Adaptation Plan (NAP) as well as specificity on how limited capacity for cross-sectoral coordination can be directed to safeguard SDG targets. Adaptation does not necessarily imply investing in physical asset protection: results show the need to protect critical natural environments which provide important adaptation services to the built environment. As more nations develop and revise their NAPs and Nationally Determined Contributions under the Paris Agreement, strategic planning across sectors – as demonstrated in Saint Lucia – will be critical to facilitate adaptation that safeguards SDG achievement.     

Crop modelling: towards locally relevant and climate-informed adaptation

A gap between the potential and practical realisation of adaptation exists: adaptation strategies need to be both climate-informed and locally relevant to be viable. Place-based approaches study local and contemporary dynamics of the agricultural system, whereas climate impact modelling simulates climate-crop interactions across temporal and spatial scales. Crop-climate modelling and place-based research on adaptation were strategically reviewed and analysed to identify areas of commonality, differences, and potential learning opportunities to enhance the relevance of both disciplines through interdisciplinary approaches. Crop-modelling studies have projected a 7–15% mean yield change with adaptation compared to a non-adaptation baseline (Nature Climate Change 4:1–5, 2014). Of the 17 types of adaptation strategy identified in this study as place-based adaptations occurring within Central America, only five were represented in crop-climate modelling literature, and these were as follows: fertiliser, irrigation, change in planting date, change in cultivar and area cultivated. The breath and agency of real-life adaptation compared to its representation in modelling studies is a source of error in climate impact simulations. Conversely, adaptation research that omits assessment of future climate variability and impact does not enable to provide sustainable adaptation strategies to local communities so risk maladaptation. Integrated and participatory methods can identify and reduce these sources of uncertainty, for example, stakeholder’s engagement can identify locally relevant adaptation pathways. We propose a research agenda that uses methodological approaches from both the modelling and place-based approaches to work towards climate-informed locally relevant adaptation.     

Limitations of temperature-based methods in estimating crop evapotranspiration in arid-zone agricultural development projects

The methods of Blaney-Criddle and Thornthwaite were used to estimate evapo-transpiration and the estimates were compared with similar estimates provided by the Doorenbos and Pruitt method as well as with the measured values of Class A pan evaporation. It was concluded that Thornthwaite underestimates under the arid conditions. Blaney-Criddle method, though developed in arid areas and can provide good estimates of the mean values in an arid area, has an insufficient sensitivity to the inter-annual variation of the evaporative demand of the atmosphere. The development of a vast irrigation project in a previously barren arid land allowed the examination of the effect of the release of latent heat in the estimates of evapotranspiration.     

South African crop farming and climate change: An economic assessment of impacts

This paper assesses the economic impact of the expected adverse changes in the climate on crop farming in South Africa using a revised Ricardian model and data from farm household surveys, long-term climate data, major soils and runoffs. Mean annual estimates indicate that a 1% increase in temperature will lead to about US$ 80.00 increase in net crop revenue while a 1 mm/month fall in precipitation leads to US$ 2.00 fall, but with significant seasonal differences in impacts. There are also significant spatial differences and across the different farming systems. Using selected climate scenarios, the study predicts that crop net revenues are expected to fall by as much as 90% by 2100 with small-scale farmers been most affected. Policies therefore need to be fine-tuned and more focused to take advantage of the relative benefits across seasons, farming systems and spatially, and by so doing climate change may be beneficial rather than harmful.     

Sources of knowledge and ignorance in climate research

Ignorance is an inevitable component of climate change research, and yet it has not been specifically catered for in standard uncertainty guidance documents for climate assessments. Reports of ignorance in understanding require context to explain how such ignorance does and does not affect understanding more generally. The focus of this article is on dynamical sources of ignorance in regional climate change projections. A key source of ignorance in the projections is the resolution-limited treatment of dynamical instabilities in the ocean component of coupled climate models. A consequence of this limitation is that it is very difficult to quantify uncertainty in regional projections of climate variables that depend critically upon the details of the atmospheric flow. The standard methods for quantifying or reducing uncertainty in regional projections are predicated on the models capturing and representing the key dynamical instabilities, which is doubtful for present coupled models. This limitation does not apply to all regional projections, nor does it apply to the fundamental findings of greenhouse climate change. Much of what is known is not highly flow-dependent and follows from well grounded radiative physics and thermodynamic principles. The growing field of applications of regional climate projections would benefit from a more critical appraisal of ignorance in these projections.     

Eemian tropical and subtropical African moisture transport: an isotope modelling study

During the last interglacial insolation maximum (Eemian, MIS 5e) the tropical and subtropical African hydrological cycle was enhanced during boreal summer months. The climate anomalies are examined with a General Circulation Model (ECHAM4) that is equipped with a module for the direct simulation of ¹⁸O and deuterium (H ₂ ¹⁸ O and HDO, respectively) in all components of the hydrological cycle. A mechanism is proposed to explain the physical processes that lead to the modelled anomalies. Differential surface heating due to anomalies in orbital insolation forcing induce a zonal flow which results in enhanced moisture advection and precipitation. Increased cloud cover reduces incoming short wave radiation and induces a cooling between 10°N and 20°N. The isotopic composition of rainfall at these latitudes is therefore significantly altered. Increased amount of precipitation and stronger advection of moisture from the Atlantic result in isotopically more depleted rainfall in the Eemian East African subtropics compared to pre-industrial climate. The East–West gradient of the isotopic rainfall composition reverses in the Eemian simulation towards depleted values in the east, compared to more depleted western African rainfall in the pre-industrial simulation. The modelled re-distribution of δ¹⁸O and δD is the result of a change in the forcing of the zonal flow anomaly. We conclude that the orbitally induced forcing for African monsoon maxima extends further eastward over the continent and leaves a distinct isotopic signal that can be tested against proxy archives, such as lake sediment cores from the Ethiopian region.     

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.     

Perspective of agricultural water safety under combined future changes in crop water requirements and climate conditions in China

Theoretical and Applied Climatology - Agricultural water safety is foreseen to have more turmoil in the future, and this concern will be driven by agricultural production growth, population growth,...     

Divergent adaptation to climate variability: A case study of pastoral and agricultural societies in Niger

Adaptation is a complex, dynamic, and sometimes unequal process. Stemming from social ecological systems theories of climate change adaptation and adaptive capacity, this case study introduces the concept of ‘divergent’ adaptation. Adaptation is divergent when one user or group's adaptation causes a subsequent reduction in another user or group's adaptive capacity in the same ecosystem. Using the example of pastoral and agricultural groups in northern and southern rainfall zones of Niger, this study illustrates the concept of divergent adaptation by identifying changes to the adaptive capacity of users who are currently engaged in conflicts over access to natural resources. Similar to other studies, we find that expansion of farmland and the consequent loss of pastoral space are restricting pastoral adaptation. Divergent adaptations favoring agricultural livelihoods include cultivating near or around pastoral wells or within pastoral corridors, both of which limit the mobility and entitlements of pastoralists. Institutions rarely secure pastoral routes and access to water points, a problem that is compounded by conflicting modes of governance, low accountability, and corruption. The case study illustrates the need to enhance the adaptive capacity of multiple user groups to reduce conflict, enhance human security, and promote overall resilience.     

Quantifying the value of historical climate knowledge and climate forecasts using agricultural systems modelling

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts.     

Uncertainty of climate change impact on crop characteristics: a case study of Moghan plain in Iran

Theoretical and Applied Climatology - Crop yield is one of the most critical factors in the food security chain. Climate plays a crucial role in crop water productivity in rainfed and irrigated...     

Single and dual crop coefficients and crop evapotranspiration for wheat and maize in a semi-arid region

In this study, weighing lysimeters were used to investigate the daily crop coefficient and evapotranspiration of wheat and maize in the Fars province, Iran. The locally calibrated Food and Agriculture Organization (FAO) Penman–Monteith equation was used to calculate the reference crop evapotranspiration (ET_o). Micro-lysimetry was used to measure soil evaporation (E). Transpiration (T) was estimated by the difference between crop evapotranspiration (ET_c) and E. The single crop coefficient (K _c) was calculated by the ratio of ET_c to ET_o. Furthermore, the dual crop coefficient is composed of the soil evaporation coefficient (K _e) and the basal crop coefficients (K _cb) calculated from the ratio of E and T to ET_o, respectively. The maximum measured evapotranspiration rate for wheat was 9.9 mm?day^?1 and for maize was 10 mm?day^?1. The total evaporation from the soil surface was about 30 % of the total wheat ET_c and 29.8 % of total maize ET_c. The single crop coefficient (K _c) values for the initial, mid-, and end-season growth stages of maize were 0.48, 1.40, and 0.31 and those of wheat were 0.77, 1.35, and 0.26, respectively. The measured K _c values for the initial and mid-season stages were different from the FAO recommended values. Therefore, the FAO standard equation for K _c-_mid was calibrated locally for wheat and maize. The K _cb values for the initial, mid-, and end-season growth stages were 0.23, 1.14, and 0.13 for wheat and 0.10, 1.07, and 0.06 for maize, respectively. Furthermore, the FAO procedure for single crop coefficient showed better predictions on a daily basis, although the dual crop coefficient method was more accurate on seasonal scale.     

Classifying knowledge on climate change impacts, adaptation, and vulnerability in Europe for informing adaptation research and decision-making: A conceptual meta-analysis

Knowledge on climate change impacts, adaptation, and vulnerability is fragmented, because it is found in disparate case studies which use inconsistent terminology and focus on distinct aspects relevant to adaptation. While large-scale syntheses such as the Assessment Reports of the Intergovernmental Panel on Climate Change provide a high level overview and are useful for international decision-making, there is a need for systematic and flexible access to this research-based knowledge in order to aid future adaptation research and decision-making. Against this background, we present a ‘conceptual’ meta-analysis, a novel approach to meta-analyse studies on climate change impacts, adaptation, and vulnerability in Europe. The meta-analysis results in a classification scheme for relating the diverse studies. This scheme consists of (i) a classification of studies according to the type of adaptation-relevant results they produce and (ii) a hierarchical classification of the regional and thematic context of studies. The implementation of this scheme, for example in the form of a database, overcomes some of the identified gaps of current adaptation knowledge representation. We furthermore present a quantitative analysis of the classified studies that exemplifies how the developed classification scheme can be applied to get a systematic and quantitative overview of the knowledge they contain. Thus, the conceptual meta-analysis and the classification scheme represent a first step towards a systematisation of knowledge on climate change impacts, adaptation, and vulnerability and may be seen as a useful complement to qualitative literature reviews.