• Energy Research

Le cacao (Theobroma cacao L.) est l'une des cultures de base agricoles les plus importantes au monde, avec la plus grande part de la production mondiale concentrée en Afrique de l'Ouest. Les rendements actuels à la ferme dans cette région sont faibles et devraient diminuer en réponse au changement climatique, en raison du réchauffement et des changements dans les précipitations. Les interventions visant à améliorer les rendements et l'adaptation au climat nécessitent une compréhension des principaux facteurs de rendement dans les exploitations. À cet égard, nous avons quantifié la mesure dans laquelle les conditions environnementales (c.-à-d. le climat et les sols) déterminent les rendements de cacao et comment cela diffère pour les exploitations atteignant en moyenne des niveaux de production moyens faibles et élevés sur la base d'un ensemble de données sans précédent de 3 827 exploitations de cacao couvrant les gradients environnementaux du Ghana. Nous avons en outre quantifié l'importance relative des pratiques de gestion sur la base d'un sous-ensemble de 134 exploitations pour lesquelles des informations de gestion étaient disponibles. Nous avons modélisé le rendement annuel du cacao à la ferme en fonction des variables environnementales pour le grand ensemble de données et le rendement du cacao par arbre en fonction des variables environnementales et de gestion pour les exploitations du sous-ensemble à l'aide de modèles à effets mixtes. Les différences d'effets sur le rendement entre les exploitations ayant des niveaux de production moyens faibles et élevés ont été évaluées à l'aide de modèles à effets mixtes quantiles. Il y avait une variabilité considérable des rendements entre les exploitations, allant de ~100 à >1000 kg ha−1 (moyenne = 554 kg ha−1). Les modèles à effets mixtes ont montré que les effets fixes (c.-à-d. les variables environnementales) n'expliquaient que 7% de la variabilité des rendements tandis que les effets fixes et aléatoires expliquaient ensemble 80%, ce qui suggère que la variation d'une ferme à l'autre a joué un rôle important. La variation expliquée des rendements en cacao par arbre de 134 exploitations du sous-ensemble est passée de 10 % à 25 % en incluant des variables de gestion en plus des variables environnementales. Dans les deux modèles, les facteurs liés au climat ont eu un effet plus important sur les rendements que les facteurs édaphiques, le rayonnement de la principale saison sèche et celui de l'année précédente ayant les effets les plus forts sur les rendements à la ferme et sur les rendements des arbres, respectivement. Les analyses de régression quantile ont montré que la productivité dans les exploitations à haut rendement (90e percentile) était plus fortement influencée par des facteurs environnementaux que dans les exploitations à faible rendement (10e percentile). En conclusion, la gestion agronomique est le déterminant dominant des rendements de cacao à la ferme au Ghana, plus que les conditions environnementales. En outre, les exploitations cacaoyères à haut rendement sont plus sensibles aux conditions environnementales que celles à faible rendement. Nos résultats suggèrent que de bonnes pratiques agricoles doivent être en place avant d'investir dans des pratiques supplémentaires d'adaptation au climat. El cacao (Theobroma cacao L.) es uno de los cultivos agrícolas básicos más importantes del mundo, con la mayor parte de la producción mundial concentrada en África Occidental. Los rendimientos actuales en las granjas en esta región son bajos y se espera que disminuyan en respuesta al cambio climático, a través del calentamiento y los cambios en las precipitaciones. Las intervenciones destinadas a mejorar los rendimientos y la adaptación al clima requieren una comprensión de los principales impulsores de los rendimientos en todas las granjas. En este sentido, cuantificamos la medida en que las condiciones ambientales (es decir, el clima y el suelo) impulsan los rendimientos de cacao y cómo esto difiere para las granjas que logran en promedio niveles medios de producción bajos y altos en base a un conjunto de datos sin precedentes de 3827 granjas de cacao que abarcan los gradientes ambientales de Ghana. Además, cuantificamos la importancia relativa de las prácticas de gestión basadas en un subconjunto de 134 granjas para las que se disponía de información de gestión. Modelamos el rendimiento anual de cacao en la granja en función de las variables ambientales para el gran conjunto de datos y el rendimiento de cacao por árbol en función de las variables ambientales y de gestión para las granjas del subconjunto utilizando modelos de efectos mixtos. Las diferencias en los efectos sobre el rendimiento entre las granjas con niveles medios de producción bajos y altos se evaluaron utilizando modelos cuantil de efectos mixtos. Hubo una variabilidad considerable en los rendimientos entre granjas, que oscilaron entre ~100 y >1000 kg ha−1 (media = 554 kg ha−1). Los modelos de efectos mixtos mostraron que los efectos fijos (es decir, las variables ambientales) solo explicaban el 7% de la variabilidad en los rendimientos, mientras que los efectos fijos y aleatorios juntos explicaban el 80%, lo que sugiere que la variación de una granja a otra desempeñó un papel importante. La variación explicada en los rendimientos de cacao por árbol de 134 fincas en el subconjunto aumentó del 10% al 25% al incluir variables de manejo además de variables ambientales. En ambos modelos, los factores relacionados con el clima tuvieron un mayor efecto en los rendimientos que los factores edáficos, y la radiación de la estación seca principal y la del año anterior tuvieron los efectos más fuertes en los rendimientos de la granja y de los árboles, respectivamente. Los análisis de regresión cuantil mostraron que la productividad en las granjas de alto rendimiento (percentil 90) fue impulsada más fuertemente por factores ambientales que en las granjas de bajo rendimiento (percentil 10). En conclusión, la gestión agronómica es el determinante dominante de los rendimientos de cacao en la granja en Ghana, más que las condiciones ambientales. Además, las fincas de cacao de alto rendimiento son más sensibles a las condiciones ambientales que las de bajo rendimiento. Nuestros hallazgos sugieren que deben existir buenas prácticas agrícolas antes de invertir en prácticas adicionales de adaptación al clima. Cocoa (Theobroma cacao L.) is one of the world's most important agricultural commodity crops with the largest share of global production concentrated in West Africa. Current on-farm yields in this region are low and are expected to decrease in response to climate change, through warming and shifts in rainfall. Interventions intended to improve yields and climate adaptation require an understanding of the main drivers of yields across farms. In this regard, we quantified the extent to which environmental (i.e., climate and soil) conditions drive cocoa yields and how this differs for farms achieving on average low- and high mean production levels based on an unprecedented dataset of 3827 cocoa farms spanning the environmental gradients of Ghana. We further quantified the relative importance of management practices based on a subset of 134 farms for which management information was available. We modelled on-farm annual cocoa yield as a function of environmental variables for the large dataset and cocoa yield per tree as a function of environmental and management variables for the subset farms using mixed-effects models. Differences in effects on yield between farms with low and high mean production levels were evaluated using quantile mixed-effects models. There was considerable variability in yields across farms, ranging from ~100 to >1000 kg ha−1 (mean = 554 kg ha−1). Mixed-effects models showed that the fixed effects (i.e., environmental variables) only explained 7% of the variability in yields whilst fixed and random effects together explained 80%, suggesting that farm-to-farm variation played a large role. Explained variation in cocoa yields per tree of 134 farms in the subset increased from 10% to 25% when including management variables in addition to environmental variables. In both models, climate-related factors had a larger effect on yields than edaphic factors, with radiation of the main dry season and that of the previous year having the strongest effects on on-farm- and tree yields, respectively. The quantile regression analyses showed that productivity in high-yielding farms (90th percentile) was more strongly driven by environmental factors than in low-yielding farms (10th percentile). In conclusion, agronomic management is the dominant determinant of on-farm cocoa yields in Ghana, more so than environmental conditions. Furthermore, high-yielding cocoa farms are more sensitive to environmental conditions than low-yielding ones. Our findings suggests that good agricultural practices need to be in place before investing in additional climate adaptation practices. الكاكاو (Theobroma cacao L.) هو واحد من أهم محاصيل السلع الزراعية في العالم مع أكبر حصة من الإنتاج العالمي تتركز في غرب أفريقيا. الغلة الحالية في المزارع في هذه المنطقة منخفضة ومن المتوقع أن تنخفض استجابة لتغير المناخ، من خلال الاحترار والتغيرات في هطول الأمطار. تتطلب التدخلات التي تهدف إلى تحسين الغلة والتكيف مع المناخ فهم الدوافع الرئيسية للغلة عبر المزارع. في هذا الصدد، قمنا بقياس مدى تأثير الظروف البيئية (أي المناخ والتربة) على غلة الكاكاو وكيف يختلف ذلك بالنسبة للمزارع التي تحقق في المتوسط مستويات إنتاج منخفضة وعالية بناءً على مجموعة بيانات غير مسبوقة من 3827 مزرعة كاكاو تغطي التدرجات البيئية لغانا. كما قمنا بقياس الأهمية النسبية لممارسات الإدارة بناءً على مجموعة فرعية من 134 مزرعة تتوفر لها معلومات إدارية. قمنا بنمذجة محصول الكاكاو السنوي في المزرعة كدالة للمتغيرات البيئية لمجموعة البيانات الكبيرة ومحصول الكاكاو لكل شجرة كدالة للمتغيرات البيئية والإدارية لمزارع المجموعات الفرعية باستخدام نماذج التأثيرات المختلطة. تم تقييم الاختلافات في التأثيرات على الغلة بين المزارع ذات مستويات الإنتاج المنخفضة والمرتفعة باستخدام نماذج التأثيرات المختلطة الكمية. كان هناك تباين كبير في الغلة عبر المزارع، تتراوح من ~100 إلى >1000 كجم هكتار-1 (المتوسط = 554 كجم هكتار-1). أظهرت نماذج التأثيرات المختلطة أن التأثيرات الثابتة (أي المتغيرات البيئية) أوضحت فقط 7 ٪ من التباين في الغلة بينما أوضحت التأثيرات الثابتة والعشوائية معًا 80 ٪، مما يشير إلى أن التباين من المزرعة إلى المزرعة لعب دورًا كبيرًا. زاد التباين الموضح في غلة الكاكاو لكل شجرة من 134 مزرعة في المجموعة الفرعية من 10 ٪ إلى 25 ٪ عند تضمين متغيرات الإدارة بالإضافة إلى المتغيرات البيئية. في كلا النموذجين، كان للعوامل المتعلقة بالمناخ تأثير أكبر على الغلة من العوامل الوعائية، حيث كان لإشعاع موسم الجفاف الرئيسي وإشعاع العام السابق أقوى التأثيرات على غلة المزارع والأشجار، على التوالي. أظهرت تحليلات الانحدار الكمي أن الإنتاجية في المزارع ذات العائد المرتفع (الشريحة المئوية التسعين) كانت مدفوعة بقوة بالعوامل البيئية أكثر من المزارع ذات العائد المنخفض (الشريحة المئوية العاشرة). في الختام، فإن الإدارة الزراعية هي المحدد المهيمن لمحصول الكاكاو في المزرعة في غانا، أكثر من الظروف البيئية. علاوة على ذلك، فإن مزارع الكاكاو عالية الغلة أكثر حساسية للظروف البيئية من المزارع منخفضة الغلة. تشير النتائج التي توصلنا إليها إلى ضرورة وجود ممارسات زراعية جيدة قبل الاستثمار في ممارسات إضافية للتكيف مع المناخ.

AbstractAmazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree‐level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat‐derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha−1 year−1 during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest‐based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.

Trees forage for light through optimal leaf display. Effective leaf display is determined by metamer traits (i.e., the internode, petiole, and corresponding leaf), and thus these traits strongly co-determine carbon gain and as a result competitive advantage in a light-limited environment. We examined 11 metamer traits of sun and shade trees of 38 coexisting moist forest tree species and determined the relative strengths of intra- and interspecific variation. Species-specific metamer traits were related to two variables that represent important life history variation; the regeneration light requirements and average leaf size of the species. Metamer traits varied strongly across species and, in contrast to our expectation, showed only modest changes in response to light. Intra- and interspecific responses to light were only congruent for a third of the traits evaluated. Four traits, amongst which leaf size, specific leaf area (SLA), and leaf area ratio at the metamer level (LAR) showed even opposite intra- and interspecific responses to light. Strikingly, these are classic traits that are thought to be of paramount importance for plant performance but that have completely different consequences within and across species. Sun trees of a given species had small leaves to reduce the heat load, but light-demanding species had large leaves compared to shade-tolerants, probably to outcompete their neighbors. Shade trees of a given species had a high SLA and LAR to capture more light in a light-limited environment, whereas shade-tolerant species have well-protected leaves with a low SLA compared to light-demanding species, probably to deter herbivores and enhance leaf lifespan. There was a leaf-size-mediated trade-off between biomechanical and hydraulic safety, and the efficiency with which species can space their leaves and forage for light. Unexpectedly, metamer traits were more closely linked to leaf size than to regeneration light requirements, probably because leaf-size-related biomechanical and vascular constraints limit the trait combinations that are physically possible. This suggests that the leaf size spectrum overrules more subtle variation caused by the leaf economics spectrum, and that leaf size represents a more important strategy axis than previously thought.

Land-use change occurs nowhere more rapidly than in the tropics, where the imbalance between deforestation and forest regrowth has large consequences for the global carbon cycle1. However, considerable uncertainty remains about the rate of biomass recovery in secondary forests, and how these rates are influenced by climate, landscape, and prior land use2, 3, 4. Here we analyse aboveground biomass recovery during secondary succession in 45 forest sites and about 1,500 forest plots covering the major environmental gradients in the Neotropics. The studied secondary forests are highly productive and resilient. Aboveground biomass recovery after 20 years was on average 122 megagrams per hectare (Mg ha−1), corresponding to a net carbon uptake of 3.05 Mg C ha−1 yr−1, 11 times the uptake rate of old-growth forests. Aboveground biomass stocks took a median time of 66 years to recover to 90% of old-growth values. Aboveground biomass recovery after 20 years varied 11.3-fold (from 20 to 225 Mg ha−1) across sites, and this recovery increased with water availability (higher local rainfall and lower climatic water deficit). We present a biomass recovery map of Latin America, which illustrates geographical and climatic variation in carbon sequestration potential during forest regrowth. The map will support policies to minimize forest loss in areas where biomass resilience is naturally low (such as seasonally dry forest regions) and promote forest regeneration and restoration in humid tropical lowland areas with high biomass resilience.

Abstract For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (⩽20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0–7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps.