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To achieve food security for many in low-income and middle-income countries for whom this is already a challenge, especially with the additional complications of climate change, will require early investment to support smallholder farming systems and the associated food systems that supply poor consumers. We need both local and global policy-linked research to accelerate sharing of lessons on institutions, practices and technologies for adaptation and mitigation. This strategy paper briefly outlines how the Research Program on Climate Change, Agriculture and Food Security (CCAFS) of the Consortium of International Agricultural Research Centres (CGIAR) is working across research disciplines, organisational mandates, and spatial and temporal levels to assist immediate and longer-term policy actions.

Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems.

Les modèles climatiques mondiaux (MCG) sont devenus de plus en plus importants pour la science du changement climatique et constituent la base de la plupart des études d'impact. Étant donné que les modèles d'impact sont très sensibles aux données climatiques d'entrée, les compétences en GCM sont cruciales pour obtenir de meilleures perspectives à court, moyen et long terme pour la production agricole et la sécurité alimentaire. L'ensemble de la phase 5 du projet d'intercomparaison de modèles couplés (CMIP) est susceptible de sous-tendre la majorité des évaluations d'impact climatique au cours des prochaines années. Nous évaluons 24 simulations CMIP3 et 26 CMIP5 du climat actuel par rapport aux observations climatiques pour cinq régions tropicales, ainsi que les améliorations régionales des compétences des modèles et, par le biais d'une revue de la littérature, les sensibilités des estimations d'impact aux erreurs des modèles. Les moyennes climatologiques des températures moyennes saisonnières représentent des erreurs moyennes entre 1 et 18 ° C (2-130% par rapport à la moyenne), tandis que les précipitations saisonnières et la fréquence des jours humides représentent des erreurs plus importantes, compensant souvent les moyennes observées et la variabilité au-delà de 100%. La variabilité climatique interannuelle simulée dans les MCG mérite une attention particulière, étant donné qu'aucun MCG ne correspond aux observations dans plus de 30 % des zones pour les précipitations mensuelles et la fréquence des jours humides, 50 % pour la plage diurne et 70 % pour les températures moyennes. Nous rapportons des améliorations des compétences climatiques moyennes de 5 à 15 % pour les températures moyennes climatologiques, de 3 à 5 % pour la plage diurne et de 1 à 2 % pour les précipitations. À ces taux d'amélioration, nous estimons qu'au moins 5 à 30 ans de travail du CMIP sont nécessaires pour améliorer les simulations régionales de température et au moins 30 à 50 ans pour les simulations de précipitations, pour que celles-ci soient directement entrées dans les modèles d'impact. Nous concluons avec quelques recommandations pour l'utilisation de CMIP5 dans les études d'impact agricole. Los modelos climáticos globales (GCM) se han vuelto cada vez más importantes para la ciencia del cambio climático y proporcionan la base para la mayoría de los estudios de impacto. Dado que los modelos de impacto son muy sensibles a los datos climáticos de entrada, la habilidad del GCM es crucial para obtener mejores perspectivas a corto, mediano y largo plazo para la producción agrícola y la seguridad alimentaria. Es probable que el conjunto de fase 5 del Proyecto de Intercomparación de Modelos Acoplados (CMIP) sustente la mayoría de las evaluaciones de impacto climático en los próximos años. Evaluamos 24 simulaciones CMIP3 y 26 CMIP5 del clima actual contra las observaciones climáticas para cinco regiones tropicales, así como las mejoras regionales en la habilidad del modelo y, a través de la revisión de la literatura, las sensibilidades de las estimaciones de impacto al error del modelo. Las medias climatológicas de las temperaturas medias estacionales muestran errores medios entre 1 y 18 °C (2-130% con respecto a la media), mientras que las precipitaciones estacionales y la frecuencia de los días húmedos muestran errores mayores, que a menudo compensan las medias observadas y la variabilidad más allá del 100%. La variabilidad climática interanual simulada en los mcg merece especial atención, dado que ningún mcg coincide con lo observado en más del 30% de las áreas para la precipitación mensual y la frecuencia de los días húmedos, el 50% para el rango diurno y el 70% para las temperaturas medias. Reportamos mejoras en la habilidad climática media de 5–15% para las temperaturas medias climatológicas, 3–5% para el rango diurno y 1–2% en la precipitación. A estas tasas de mejora, estimamos que se requieren al menos 5–30 años de trabajo de CMIP para mejorar las simulaciones de temperatura regionales y al menos 30–50 años para las simulaciones de precipitación, para que estas se ingresen directamente en los modelos de impacto. Concluimos con algunas recomendaciones para el uso de CMIP5 en estudios de impacto agrícola. Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18 ° C (2–130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5–15% for climatological mean temperatures, 3–5% for diurnal range and 1–2% in precipitation. At these improvement rates, we estimate that at least 5–30 years of CMIP work is required to improve regional temperature simulations and at least 30–50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies. أصبحت نماذج المناخ العالمي (GCMs) ذات أهمية متزايدة لعلوم تغير المناخ وتوفر الأساس لمعظم دراسات التأثير. نظرًا لأن نماذج التأثير حساسة للغاية لإدخال البيانات المناخية، فإن مهارة GCM ضرورية للحصول على توقعات أفضل على المدى القصير والمتوسط والطويل للإنتاج الزراعي والأمن الغذائي. من المرجح أن تدعم مجموعة المرحلة الخامسة من مشروع المقارنة بين النماذج المقترنة (CMIP) غالبية تقييمات التأثير المناخي على مدى السنوات القليلة المقبلة. نقوم بتقييم 24 محاكاة CMIP3 و 26 CMIP5 للمناخ الحالي مقابل ملاحظات المناخ لخمس مناطق استوائية، بالإضافة إلى التحسينات الإقليمية في مهارة النموذج، ومن خلال مراجعة الأدبيات، وحساسيات تقديرات التأثير لنمذجة الخطأ. تصور المتوسطات المناخية لمتوسط درجات الحرارة الموسمية أخطاء متوسطة تتراوح بين 1 و 18 درجة مئوية (2-130 ٪ فيما يتعلق بالمتوسط)، في حين أن هطول الأمطار الموسمي وتردد اليوم الرطب يصور أخطاء أكبر، وغالبًا ما تعوض الوسائل المرصودة والتباين الذي يتجاوز 100 ٪. تستدعي محاكاة تقلبات المناخ بين السنوات في GCMs اهتمامًا خاصًا، نظرًا لأنه لا يوجد GCM واحد يطابق الملاحظات في أكثر من 30 ٪ من المناطق لهطول الأمطار الشهرية وتردد اليوم الرطب، و 50 ٪ للنطاق النهاري و 70 ٪ لدرجات الحرارة المتوسطة. نبلغ عن تحسينات في متوسط المهارة المناخية بنسبة 5-15 ٪ لدرجات الحرارة المتوسطة المناخية، و 3-5 ٪ للنطاق النهاري و 1-2 ٪ في هطول الأمطار. بمعدلات التحسن هذه، نقدر أن هناك حاجة إلى ما لا يقل عن 5–30 عامًا من العمل في CMIP لتحسين محاكاة درجة الحرارة الإقليمية وما لا يقل عن 30–50 عامًا لمحاكاة هطول الأمطار، حتى يتم إدخالها مباشرة في نماذج التأثير. نختتم ببعض التوصيات لاستخدام CMIP5 في دراسات الأثر الزراعي.

Achieving food security in a ‘perfect storm’ scenario is a grand challenge for society. Climate change and an expanding global population act in concert to make global food security even more complex and demanding. As achieving food security and the millennium development goal (MDG) to eradicate hunger influences the attainment of other MDGs, it is imperative that we offer solutions which are complementary and do not oppose one another. Sustainable intensification of agriculture has been proposed as a way to address hunger while also minimizing further environmental impact. However, the desire to raise productivity and yields has historically led to a degraded environment, reduced biodiversity and a reduction in ecosystem services (ES), with the greatest impacts affecting the poor. This paper proposes that the ES framework coupled with a policy response framework, for example Driver-Pressure-State-Impact-Response (DPSIR), can allow food security to be delivered alongside healthy ecosystems, which provide many other valuable services to humankind. Too often, agro-ecosystems have been considered as separate from other natural ecosystems and insufficient attention has been paid to the way in which services can flow to and from the agro-ecosystem to surrounding ecosystems. Highlighting recent research in a large multi-disciplinary project (ASSETS), we illustrate the ES approach to food security using a case study from the Zomba district of Malawi.

Pinus patula and Pinus tecunumanii play an important role in the forestry sector in the tropics and subtropics and, in recent decades, members of the International Tree Breeding and Conservation Program (Camcore) at North Carolina State University have established large, multi-site provenance trials for these pine species. The data collected in these trials provide valuable information about species and provenance choice for plantation establishment in many regions with different climates. Since climate is changing rapidly, it may become increasingly difficult to choose the right species and provenance to plant. In this study, growth performance of plantings in Colombia, Brazil and South Africa was correlated to the degree of climatic dissimilarity between planting sites. Results are used to assess the suitability of seed material under a changing climate for four P. patula provenances and six P. tecunumanii provenances. For each provenance, climate dissimilarities based on standardized Euclidean distances were calculated and statistically related to growth performances. We evaluated the two methods of quantifying climate dissimilarity with extensive field data based on the goodness of fit and statistical significance of the climate distance relation to differences in height growth. The best method was then used as a predictor of a provenance change in height growth. The provenance-specific models were used to predict provenance performance under different climate change scenarios. The developed provenance-specific models were able to significantly relate climate similarity to different growth performances for five out of six P. tecunumanii provenances. For P. patula provenances, we did not find any correlation. Results point towards the importance of the identification of sites with stable climates where high yields are achievable. In such sites, fast-growing P. tecunumanii provenances with a high but narrow growth optimum can be planted. At sites with climate change of uncertain direction and magnitude, the choice of P. patula provenances, with greater tolerance towards different temperature and precipitation regimes, is recommended. Our results indicate that the analysis of provenance trial data with climate similarity models helps us to (1) maintain plantation productivity in a rapidly changing environment; and (2) improve our understanding of tree species’ adaptation to a changing climate.

Coffee is grown in more than 60 tropical countries on over 11 million ha by an estimated 25 million farmers, most of whom are smallholders. Several regional studies demonstrate the climate sensitivity of coffee (Coffea arabica) and the likely impact of climate change on coffee suitability, yield, increased pest and disease pressure and farmers’ livelihoods. The objectives of this paper are (i) to quantify the impact of progressive climate change to grow coffee and to produce high quality coffee in Nicaragua and (ii) to develop an adaptation framework across time and space to guide adaptation planning. We used coffee location and cup quality data from Nicaragua in combination with the Maxent and CaNaSTA crop suitability models, the WorldClim historical data and the CMIP3 global circulation models to predict the likely impact of climate change on coffee suitability and quality. We distinguished four different impact scenarios: Very high (coffee disappears), high (large negative changes), medium (little negative changes) and increase (positive changes) in climate suitability. During the Nicaraguan coffee roundtable, most promising adaptation strategies were identified, which we then used to develop a two-dimensional adaptation framework for coffee in time and space. Our analysis indicates that incremental adaptation may occur over short-term horizons at lower altitudes, whereas the same areas may undergo transformative adaptation in the longer term. At higher elevations incremental adaptation may be needed in the long term. The same principle and framework is applicable across coffee growing regions around the world.

Climate change is expected to have significant influences on terrestrial biodiversity at all system levels, including species-level reductions in range size and abundance, especially amongst endemic species. However, little is known about how mitigation of greenhouse gas emissions could reduce biodiversity impacts, particularly amongst common and widespread species. Our global analysis of future climatic range change of common and widespread species shows that without mitigation, 57±6% of plants and 34±7% of animals are likely to lose ≥50% of their present climatic range by the 2080s. With mitigation, however, losses are reduced by 60% if emissions peak in 2016 or 40% if emissions peak in 2030. Thus, our analyses indicate that without mitigation, large range contractions can be expected even amongst common and widespread species, amounting to a substantial global reduction in biodiversity and ecosystem services by the end of this century. Prompt and stringent mitigation, on the other hand, could substantially reduce range losses and buy up to four decades for climate change adaptation.