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Landscape mapping has the potential to address some of the most pressing research issues of our time, including climate change, sustainable development, and human well-being. In this paper, we propose an original method that lays the foundations for landscape mapping and overcomes some of the major limitations of existing biophysical methods. Based on the assumption that the primary components of the landscape can be extracted directly from the radiometric information of satellite image time series, this paper presents a new approach to landscape characterization and mapping based solely on remote sensing data. The approach relies on a conceptual model, which links the description, characteristics, structure and functions of the landscape to a set of Remote Sensing-based Essential Landscape Variables (RS- ELVs). The RS-ELVs are then processed according to geographic object-based image analysis (GEOBIA) approach to produce a radiometric landscape map. The model and the remote sensing data processing chain are tested on a case study in central Madagascar (about 13 000 km2) composed of contrasting landscapes resulting from different climatic conditions and agricultural practices. The RS-ELVs are extracted from MODIS image time series for the temporal and spectral variables, and from MODIS and Sentinel-2 images for the texture variables. The parameterization of the segmentation and clustering algorithms is determined by statistical optimization. The final result is a radiometric landscape map in six classes. The landscape classes are then characterized using an independent set of remote sensing variables, a global land cover map and ground observations. The approach successfully identifies and delineates the gradient and major landscape types of the complex region of central Madagascar, confirming our initial hypothesis. The production of such radiometric landscape maps opens the way for integrated territorial development, including the planning and protection of the li

Societal Impact Statement: Despite strong historical declines, Guadeloupe and Haiti's coffee sectors remain important to rural communities' livelihood and resilience. Coffee also holds value as part of the islands' historical legacy and cultural identities. Furthermore, it is often grown in agroforestry systems providing important ecosystem services, which will become more important as these vulnerable islands work to adapt to a changing climate. Current efforts to revitalize coffee farms and target strategically important specialty markets would benefit from understanding existing genetic resources and the historical factors that shaped them. Our study reveals the rich history reflected in current coffee stands on the islands. Summary: The West Indies, particularly former French colonies like Haiti and Guadeloupe, were central to the spread of coffee in the Americas. The histories of these Islands are shared until the 19th century, where they diverged significantly. Still, both Islands experienced a strong decline in their coffee sector. Characterizing the genetic and varietal diversity of their coffee resources and understanding historical factors shaping them can help support revitalization efforts. To that end, we performed Kompetitve Allele-Specific PCR (KASP) genotyping of 80 informative single nucleotide polymorphism (SNP) markers on field samples from across main coffee-growing region of Guadeloupe, and two historically important ones in Haiti, as well as 146 reference accessions from international collections. We also compared bioclimatic variables from sampled geographic areas and searched for historical determinants of present coffee resources. At least five Coffea arabica varietal groups were found in Haiti, versus two in Guadeloupe, with admixed individuals in both. The traditional Typica variety is still present in both islands, growing across a variety of climatic environments. We also found Coffea canephora on both islands, with multiple likely origi

Lodging is a critical factor in reducing sugarcane yields worldwide, mainly due to the selection of highly productive varieties. Understanding the response of yield and lodging to the combined effects of climate, sugarcane traits, and varieties has become a priority under climate change. The aim of this study was to better understand the influence of plant characteristics, climate, and soil conditions on the trade-off between sugarcane yield and lodging on the tropical Reunion Island. Data from a 14-year experimental network run by the eRcane breeding institute were used to build random-forest models to predict sugarcane yield and lodging classes, i.e. <10 %, 10–50 %, >50 % of lodging. Yield and lodging probability were then predicted across the island using climate change projections from 2015 to 2035. Both yield and lodging were highly influenced by the variety and characteristics (height and tillering) and climatic conditions. Areas on the island at high altitudes were subject to high probability of lodging (>50 %), while in areas with high wind speed, the risk of moderate lodging (10–50 %) increased. Overall, conditions or plant characteristics that favor higher yields increased lodging probability. Nevertheless, the correlation between yield and lodging probability varied considerably depending on the variety, highlighting the importance of sugarcane characteristics in resistance to lodging. This study highlights the fact that promoting more productive varieties in recent decades has led to an increase in lodging and identified critical environments on the island prone to increased risk of lodging.

Crops need adequate mineral nutrition to ensure optimal growth and yield. Nitrogen (N) and phosphorus (P) are two major elements that are essential for crop growth. However, situations of N-P colimitation are frequent in agroecosystems. Hence, our ability to optimize crop production under these conditions depends on our ability to analyze and predict the response of crops to colimitation. Traditionally, agronomists rely on the law of the minimum (LM) to manage colimitation situations. This law states that crop growth is constrained by the most limiting element. In contrast, the multiple limitation hypothesis (MLH) argues that crops can adapt by balancing their resource allocation with the best compromise to maximize their growth. These two hypotheses result in contrasting growth response patterns. The aim of the present review is to identify the crop response pattern to N-P colimitation through an assessment of experimental results against a conceptual framework and to report the main mechanism involved in this interaction. Finally, an inventory of existing crop models handling N-P colimitation is presented and possible ways of improvement are proposed. This review allowed us to (1) remind of the published theories used to classify colimitation types, (2) highlight the fact that a large range of crops mostly showed MLH-response patterns, (3) report that the variability in crop response patterns is linked to pedoclimatic variation, (4) identify multiple mechanisms that may be involved in plant adaptation to N-P colimitation, (5) suggest that the interplay between the different mechanisms results in complex responses that are difficult to understand experimentally, (6) report that few models handle N-P colimitation and that most of them rely on the law of the minimum, and (7) recommend possible ways to improve model formalization for a better simulation of crop responses under N-P colimitation.

The resilience capacity of smallholder households is one of the main drivers of their ability to continue to farm and make investments in the fragile dryland regions. This paper aims to assess the resilience profile of smallholder farmers in the face of climate change and the factors influencing it in three dryland sub-regions of Senegal, namely, Louga, Kaffrine, and Thies. We developed a composite index of climate resilience (CICR) using data on farmers' perceptions of climate variability and their perceived ability to withstand, adapt, and bounce back in the event of climatic shocks. Drought, strong winds, and soil fertility decline because of climate change emerged as the main climate hazards impacting smallholder farming systems. The CICR value ranged from −2 for the most vulnerable households to +2 for the most resilient households. On average, all the households were found to be vulnerable, with an average CICR value of −0.2. The LOUGA region was the most vulnerable, with an average CICR value of −0.36, followed by THIES (-0.2). The KAFFRINE region was relatively less vulnerable, with a CICR value of −0.1. Ordered logit model estimates show that the chances of improving CICR decrease with the increase of the household head's age until 59 years. Access to training on climate-smart agricultural (CSA) practices and climate information appeared to have the potential to increase by 171% the chance of the household improving its resilience status. Analysis also shows that one more woman working off-farm or in-home gardening has the potential to multiply by four times the chances of households being more resilient. This highlights the importance of empowering women to enhance household resilience to climate change. The off-farm revenue increased the chance to improve the resilience status of the farm household by 62% and the receipt of transfer revenue by 50%. This study provides a robust method for quantifying resilience or wellbeing and its drivers and enriches our

Background: Antananarivo, the capital city of Madagascar, is experiencing a steady increase in population growth. Due to the abundance of mosquito vectors in this locality, the population exposed to mosquito-borne diseases is therefore also increasing, as is the risk of epidemic episodes. The aim of the present study was to assess, in a resource-limited setting, the information on mosquito population dynamics and disease transmission risk that can be provided through a longitudinal entomological study carried out in a multi-host single site. Methods: Mosquitoes were collected every 15 days over 16 months (from January 2017 to April 2018) using six CDC-light traps in a peri-urban area of Antananarivo. Multivariable generalised linear models were developed using indoor and outdoor densities of the predominant mosquito species as response variables and moon illumination, environmental data and climatic data as the explanatory variables. Results: Overall, 46,737 mosquitoes belonging to at least 20 species were collected, of which Culex antennatus (68.9%), Culex quinquefasciatus (19.8%), Culex poicilipes (3.7%) and Anopheles gambiae sensu lato (2.3%) were the most abundant species. Mosquito densities were observed to be driven by moon illumination and climatic factors interacting at different lag periods. The outdoor models demonstrated biweekly and seasonal patterns of mosquito densities, while the indoor models demonstrated only a seasonal pattern. Conclusions: An important diversity of mosquitoes exists in the peri-urban area of Antananarivo. Some well-known vector species, such as Cx. antennatus, a major vector of West Nile virus (WNV) and Rift-Valley fever virus (RVFV), Cx. quinquefasciatus, a major vector of WNV, Cx. poicilipes, a candidate vector of RVFV and An. gambiae sensu lato, a major vector of Plasmodium spp., are abundant. Importantly, these four mosquito species are present all year round, even though their abundance declines during the cold dry season, wi

Theobroma cacao, a tropical cauliflorous fruit tree, typically produces flowers and fruits twice a year and exhibits alternate harvesting patterns over consecutive six-monthly seasons in regions with bi-modal rainfall distribution, such as Côte d'Ivoire. This study investigated seasonal variations in flowering and fruiting among trees in populations of mixed cacao genotypes. The intensities of crown and trunk flowering and pod production were monitored for eight consecutive six-monthly seasons on 114 adult cacao trees grown from seedlings. We investigated distributions of seasonal flowering and fruiting values, relationships between flowering and pod production, and the effects of seasonal cumulative rainfall. The patterns of seasonal flowering and fruiting series were analyzed using two descriptors: the first distinguishing between regular and variable patterns, and the second analyzing the structure of such variability, classifying it as either irregular or alternating. Despite being subjected to similar climate and agronomic management, individual trees exhibited highly variable flowering and fruiting behaviors within each season, as well as variable patterns of flowering and fruiting across seasons. Seasonal alternate fruiting on a population scale masked highly variable patterns among trees, and only 19 % of the trees exhibited marked alternate fruiting patterns. Variations in pod production on a tree scale were mainly related to variations in trunk flowering. Endogenous factors seemed to control seasonal variations in flowering and fruiting, even though exogenous factors, both climatic and agronomic, could structure flowering and fruiting patterns at the orchard scale.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%–63% of spatial variations, followed by vegetation characteristics (25%–31%) and edaphic properties (11%–16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Short-term adaptive evolution represents one of the primary mechanisms allowing species to persist in the face of global change. Predicting the adaptive response at the species level requires reliable estimates of the evolutionary potential of traits involved in adaptive responses, as well as understanding how evolutionary potential varies across a species' range. Theory suggests that spatial variation in the fitness landscape due to environmental variation will directly impact the evolutionary potential of traits. However, empirical evidence on the link between environmental variation and evolutionary potential across a species range in the wild is lacking. In this study, we estimate multivariate evolutionary potential (via the genetic variance–covariance matrix, or G-matrix) for six morphological and life history traits in 10 wild populations of great tits (Parus major) distributed across Europe. The G-matrix significantly varies in size, shape, and orientation across populations for both types of traits. For life history traits, the differences in G-matrix are larger when populations are more distant in their climatic niche. This suggests that local climates contribute to shaping the evolutionary potential of phenotypic traits that are strongly related to fitness. However, we found no difference in the overall evolutionary potential (i.e., G-matrix size) between populations closer to the core or the edge of the distribution area. This large-scale comparison of G-matrices across wild populations emphasizes that integrating variation in multivariate evolutionary potential is important to understand and predict species' adaptive responses to new selective pressures.

For countries' emission-reduction efforts under the Paris Agreement to be effective, baseline emission/removals levels and reporting must be as transparent and accurate as possible. For Indonesia, which holds among the largest area of tropical peatlands and mangrove forest in the world, it is particularly important for these high-carbon ecosystems to produce high-accuracy greenhouse gas inventory and to improve national forest reference emissions level/forest reference level. Here, we highlight the opportunity for refining greenhouse gas emission factors (EF) of peatlands and mangroves and describe scientific challenges to support climate policy processes in Indonesia, where 55 to 59% of national emission reduction targets by 2030 depend on mitigation in Forestry and Other Land Use. Based on the stock-difference and flux change approaches, we examine higher-tier EF for drained and rewetted peatland, peatland fires, mangrove conversions, and mangrove on peatland to improve future greenhouse gas flux reporting in Indonesia. We suggest that these refinements will be essential to support Indonesia in achieving Forest and Other Land Use net sink by 2030 and net zero emissions targets by 2060 or earlier.

Synthesis of crop trial data can generate insights that are not available from the analysis of individual studies, but such synthesis is often constrained by the heterogeneity of data among studies. Rank-based data synthesis provides the flexibility to combine data of heterogeneous types and from different sources. We demonstrate the application of rank-based data synthesis of heterogeneous trial data to assess the effect of climatic factors on the reaction of several Musa genotypes to black leaf streak disease (BLSD; caused by Pseudocercospora fijiensis [sexual morph: Mycosphaerella fijiensis]). We aggregated data from the main public repositories of Musa trial data. We applied model-based recursive partitioning with the Plackett–Luce model, using climatic data as covariates. The model identified the maximum length of the dry spell as the main variable influencing differences in genotypic response to BLSD, dividing the aggregated trial dataset into humid and dry environments. We found differences in the reaction of genotypes to BLSD between these environments. In humid environments, NARITA 8 was found to be the most resistant genotype, while in dry environments FHIA-01 was the best performing improved genotype. We also assessed reliability, which is the probability of outperforming the reference genotype (Calcutta 4). In humid environments, NARITA 2, NARITA 8, and FHIA-01 had the highest reliability, while in dry environments only the landrace Saba surpassed 50% reliability. The information generated by our data synthesis approach supports selecting Musa genotypes for further evaluations at new locations.

Background: Arthropod vectors such as ticks, mosquitoes, sandflies and biting midges are of public and veterinary health significance because of the pathogens they can transmit. Understanding their distributions is a key means of assessing risk. VectorNet maps their distribution in the EU and surrounding areas. Aim: We aim to describe the methodology underlying VectorNet maps, encourage standardisation and evaluate output. Methods: Vector distribution and surveillance activity data have been collected since 2010 from a combination of literature searches, field-survey data by entomologist volunteers via a network facilitated for each participating country and expert validation. Data were collated by VectorNet members and extensively validated during data entry and mapping processes. Results: As of 2021, the VectorNet archive consisted of ca 475,000 records relating to > 330 species. Maps for 42 species are routinely produced online at subnational administrative unit resolution. On VectorNet maps, there are relatively few areas where surveillance has been recorded but there are no distribution data. Comparison with other continental databases, namely the Global Biodiversity Information Facility and VectorBase show that VectorNet has 5–10 times as many records overall, although three species are better represented in the other databases. In addition, VectorNet maps show where species are absent. VectorNet's impact as assessed by citations (ca 60 per year) and web statistics (58,000 views) is substantial and its maps are widely used as reference material by professionals and the public. Conclusion: VectorNet maps are the pre-eminent source of rigorously validated arthropod vector maps for Europe and its surrounding areas.

The recent expansion of Aedes albopictus across continents in both tropical and temperate regions and the exponential growth of dengue cases over the past 50 years represent a significant risk to human health. Although climate change is not the only factor responsible for the increase and spread of dengue cases worldwide, it might increase the risk of disease transmission at global and regional scale. Here we show that regional and local variations in climate can induce differential impacts on the abundance of Ae. albopictus. We use the instructive example of Réunion Island with its varied climatic and environmental conditions and benefiting from the availability of meteorological, climatic, entomological and epidemiological data. Temperature and precipitation data based on regional climate model simulations (3 km × 3 km) are used as inputs to a mosquito population model for three different climate emission scenarios. Our objective is to study the impact of climate change on the life cycle dynamics of Ae. albopictus in the 2070–2100 time horizon. Our results show the joint influence of temperature and precipitation on Ae. albopictus abundance as a function of elevation and geographical subregion. At low-elevations areas, decreasing precipitation is expected to have a negative impact on environmental carrying capacity and, consequently, on Ae. albopictus abundance. At mid- and high-elevations, decreasing precipitation is expected to be counterbalanced by a significant warming, leading to faster development rates at all life stages, and consequently increasing the abundance of this important dengue vector in 2070–2100.

The assumption that climatic growing requirements of invasive species are conserved between their native and non-native environment is a key ecological issue in the evaluation of invasion risk. We conducted a growth chamber experiment to compare the effect of water regime and temperature on the growth and mortality of native and invasive populations of common gorse seedlings (Ulex europaeus L.). Seeds were sampled from 20 populations of five areas from both native (continental France and Spain) and non-native areas (New Zealand, Canary and Reunion islands). The seedlings were grown over 36 days in two temperature treatments (ambient and elevated) combined with two water treatments (irrigated or droughted). The elevated temperature (ET) was defined as the highest temperature observed at the niche margin in the different countries. While ET increased seedlings growth, the drought treatment increased mortality rate and limited seedlings growth. Under ET and drought, native populations showed a greater mortality rate (53%) than invasive populations (16%). Invasive seedlings also showed higher above- and belowground development than native ones under these constrained climatic conditions. While phenotypic plasticity did not differ between native and invasive populations, the difference between populations in terms of total dry mass could be related to differences in the climate of origin (precipitation in particular). Assessing the importance of phenotypic changes between populations within invasive species is crucial to identify the margins of their climatic distribution range and to highlight areas where management efforts should be concentrated in order to limit its spread.

Malaria is a permanent threat to health in western Burkina Faso. Research has shown that geographical variables contribute to the spatial distribution in its transmission. The objective of this study is to assess the relationship between malaria prevalence and potential explanatory geographical variables in the Houet province in Burkina Faso. Statistics on malaria prevalence registered by health centres in the Houet province in 2017 and potential geographical variables identified through a literature review were collected. An Ordinary Least Squares (OLS) regression was used to identify key geographical variables and to measure their association with malaria while the Getis Ord Gi* index was used to locate malaria hotspots. The results showed that average annual temperature, vegetation density, percentage of clay in the soil, total annual rainfall and distance to the nearest waterbody are the main variables associated with malaria prevalence. These variables account for two-thirds of the spatial variability of malaria prevalence observed in Houet province. The intensity and direction of the relationship between malaria prevalence and geographical factors vary according to the variable. Hence, only vegetation density is positively correlated with malaria prevalence. Average temperature, for soil clay content, annual rainfall and for distance to the nearest water body are negatively correlated with the disease prevalence. These results show that even in an endemic area, malaria prevalence has significant spatial variation. The results could contribute to the choice of intervention sites, as this choice is crucial for reducing the malaria burden.

Introduction: Rice plays a critical role in human livelihoods and food security. However, its cultivation requires inputs that are not accessible to all farming communities and can have negative effects on ecosystems. simultaneously, ecological research demonstrates that biodiversity management within fields contributes to ecosystem functioning. Methods: This study aims to evaluate the mixture effect of four functionally distinct rice varieties in terms of characteristics and agronomic performance and their spatial arrangement on the upland rice performance in the highlands of Madagascar. The study was conducted during the 2021-2022 rainfall season at two close sites in Madagascar. Both site differ from each other's in soil properties and soil fertility management. The experimental design at each site included three modalities: i) plot composition, i.e., pure stand or binary mixture; ii) the balance between the varieties within a mixture; iii) and for the balanced mixture (50% of each variety), the spatial arrangement, i.e., row or checkerboard patterns. Data were collected on yields (grain and biomass), and resistance to Striga asiatica infestation, Pyricularia oryzea and bacterial leaf blight (BLB) caused by Xanthomonas oryzae-pv from each plot. Results and discussion: Varietal mixtures produced significantly higher grain and biomass yields, and significantly lower incidence of Pyricularia oryzea compared to pure stands. No significant differences were observed for BLB and striga infestation. These effects were influenced by site fertility, the less fertilized site showed stronger mixture effects with greater gains in grain yield (60%) and biomass yield (42%). The most unbalanced repartition (75% and 25% of each variety) showed the greatest mixture effect for grain yield at both sites, with a strong impact of the varietal identity within the plot. The mixture was most effective when EARLY_MUTANT_IAC_165 constituted 75% of the density associated with other varietie

Malaria is a permanent threat to health in western Burkina Faso. Research has shown that geographical variables contribute to the spatial distribution in its transmission. The objective of this study is to assess the relationship between malaria prevalence and potential explanatory geographical variables in the Houet province in Burkina Faso. Statistics on malaria prevalence registered by health centres in the Houet province in 2017 and potential geographical variables identified through a literature review were collected. An Ordinary Least Squares (OLS) regression was used to identify key geographical variables and to measure their association with malaria while the Getis Ord Gi* index was used to locate malaria hotspots. The results showed that average annual temperature, vegetation density, percentage of clay in the soil, total annual rainfall and distance to the nearest waterbody are the main variables associated with malaria prevalence. These variables account for two-thirds of the spatial variability of malaria prevalence observed in Houet province. The intensity and direction of the relationship between malaria prevalence and geographical factors vary according to the variable. Hence, only vegetation density is positively correlated with malaria prevalence. Average temperature, for soil clay content, annual rainfall and for distance to the nearest water body are negatively correlated with the disease prevalence. These results show that even in an endemic area, malaria prevalence has significant spatial variation. The results could contribute to the choice of intervention sites, as this choice is crucial for reducing the malaria burden.

The susceptibility of cocoa to harsh climatic conditions is evident in cocoa growing areas in Ghana, and climate distribution models show reduced cocoa suitability to climate change. We assessed how cocoa health and productivity were affected by varying climate conditions for 4 years in 23 cocoa farms along a gradient of low rainfall/high temperature in the north to high rainfall/low temperature in the south of Ghana's cocoa belts. Twenty cocoa trees per farm (in total 460) were observed and scored for their canopy condition, flower intensity, and damaged pods due to mirids, cocoa shield bugs, and black pod disease (BPD). Harvested pods and extracted dried cocoa beans were evaluated to ascertain yield/productivity. Insect pest damages to pods were on average 2.3 ± 0.8, 2.2 ± 1.0, and 3.0 ± 0.7 pods tree−1 year−1 in the south, middle and north, respectively. The healthiest and highest yielding trees were in the rainy south at 0.99 ± 0.02 kg dry beans tree−1 followed by the middle (0.84 ± 0.02 kg) and the north (0.60 ± 0.01 kg). BPD infection was highest in the south at 1.1 ± 1.1 pods tree−1 year−1, followed by the middle (0.7 ± 0.8), and the north (0.4 ± 0.6). Within sites variability in rainfall and temperature was not found to affect yields significantly. The variability in cocoa performance and occurrence of pests and diseases observed within sites may thus be caused by farm management practices that are key to the enhancement of productivity at site level. We recommend regular pruning of cocoa and shade trees to increase aeration and prevent BPD in high rainfall areas, and an increase in shade tree components in dry regions for insect pest management in cocoa systems.

Tree rings provide an invaluable long-term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree-ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3-month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3-month seasonal windows), with concave-down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals

Oil palm is the major source of vegetable oil in the world and Indonesia is the main palm oil producing country. There is limited knowledge on the factors accounting for spatial and temporal variation in fresh fruit bunches (FFB) yield. Here we investigated relationships between weather and endogenous factors with FFB yield and its components (bunch number and individual bunch weight) using data collected from well-managed plantations in Indonesia. The database included many sites and years (total of 136 block-years observations), portraying a wide range of FFB yield and environmental conditions. We used average annual values to detect spatial variations in yield associated with weather, and monthly values to detect temporal yield variations in yield associated with weather and endogenous cycles. We found that water stress was the key factor accounting for the spatial and/or temporal variation in FFB yield. Our analysis also highlights the importance of vapor pressure deficit (VPD) as a stress factor in oil palm, with this study being the first to demonstrate the negative relationship between yield and VPD and yield and water-use efficiency at the block level. Meteorological anomalies during the bunch failure, anthesis, and sex differentiation periods had the largest impact on yield. Besides climate factors, we confirmed the existence of endogenous yield cycles, with high-yield cycles typically followed by low-yield cycles and vice versa. Our findings extend current knowledge about sources of variation in oil palm yield, providing useful information to describe oil palm production environments and improve oil palm modeling and yield forecasting.

Bacterial panicle blight (BPB) caused by Burkholderia glumae is one of the main concerns for rice production in the Americas since bacterial infection can interfere with the grain-filling process and under severe conditions can result in high sterility. B. glumae has been detected in several rice-growing areas of Colombia and other countries of Central and Andean regions in Latin America, although evidence of its involvement in decreasing yield under these conditions is lacking. Analysis of different parameters in trials established in three rice-growing areas showed that, despite BPB presence, severity did not explain the sterility observed in fields. PCR tests for B. glumae confirmed low infection in all sites and genotypes, only 21.4% of the analyzed samples were positive for B. glumae. Climate parameters showed that Monteria and Saldaña registered maximum temperature above 34˚C, minimum temperature above 23˚C, and Relative Humidity above 80%, conditions that favor the invasion model described for this pathogen in Asia. Our study found that in Colombia, minimum temperature above 23˚C during 10 days after flowering is the condition that correlates with disease incidence. Therefore, this correlation, and the fact that Monteria and Saldaña had a higher level of infected samples according to PCR tests, high minimum temperature, but not maximum temperature, seems to be determinant for B. glumae colonization under studied field conditions. This knowledge is a solid base line to design strategies for disease control, and is also a key element for breeders to develop strategies aimed to decrease the effect of B. glumae and high night-temperature on rice yield under tropical conditions.

Consumers, traders and processors consider post‐harvest physiological deterioration (PPD) an important constraint. In Experiment 1, PPD was assessed three consecutive years in roots from five genotypes through seven storage days. PPD, scopoletin and dry matter content (DMC) was recorded during storage. Year, genotype, duration of storage and their interactions were significant. PPD was associated with duration of storage period, DMC and scopoletin contents. Ambient moisture and temperature during storage influenced PPD. In Experiment 2, roots from seven clones were harvested 10 months after planting from 30 consecutive biweekly plantings. PPD was assessed 0, 2 and 7 days after harvest. In 13 harvests, roots from plants pruned six days earlier were also evaluated. Results indicated large seasonal variation across genotypes. Pruning reduced PPD and DMC. Complex and contrasting relationships among the variables analysed were found. There is no uniform model explaining the relationship between PPD and the independent variables considered.

Effective adaptability of plants to new environments can be analysed in terms of survival rate. Analysing the traits that favour adaptation to environmental changes provides a more in-depth understanding of the mechanisms involved. Local adaptation occurs because different environmental factors exert selective pressure across habitats. Understanding the leaf mechanisms underlying plant survival and growth is crucial to determine why local adaptation involves trade-offs. A comparative provenance test on 29 eucalyptus species was conducted to improve our understanding of species adaptation strategies on coastal plains of Pointe-Noire, Republic of the Congo. We studied the different functional traits to determine how plants function and to highlight the different species' adaptive strategies. For each species, survival, growth traits and leaf traits were measured, and the climatic factors of the origin area for each species was taken into account. Cluster analysis was performed on groups of species with a similar growth strategy. The results revealed general trends that explain the physiological mechanisms involved in the species' local adaptation. Indeed, species have survived to current environmental changes by adjusting their specific leaf area plasticity. The 32 provenances of eucalyptus were subdivided into four groups by cluster analysis. The first cluster included two species (E. pilularis and E. peltata) that are totally unsuited to the local conditions in Pointe-Noire, with the slowest growth rate and smallest specific leaf area. The second cluster contained species that showed a wide variety of growing strategies, allowing them to adapt to local conditions. The third cluster included a species that is specialised in obtaining large quantities of resources, while investing very little in growth. The fourth cluster included species that acquired and used resources at a slow rate. Leaf anatomy was quite responsive to climatic conditions. We evaluated the differe

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.

Dacryodes edulis (G. Don) H.J. Lam is an underexploited oil-bearing fruit tree highly appreciated in the Central Africa rainforest. To improve horticultural management and provide relevant data to scientists for physiological studies, the reproductive phenology of D. edulis as well as their fruit (safou) development and ripening indexes has been defined and correlated to climatic conditions. Changes in safou were assessed in four production localities in Cameroon. Fruit-set-to-ripening time was evaluated over 2 consecutive production years. Four fruits developmental and ripening stages and their durations were determined as follows: The putative cell division (1–2 weeks); putative elongation (10–14 weeks); pre-ripening (2–7 weeks); and ripening phases (3–5 weeks). Rainfall, average, and cumulative temperatures were found to be correlated with the chronology of reproductive phenophases. The average temperature during the period ranging from the floral bud emergence to the fruit set can be used to predict the thermal time. Similarly, the average ambient temperature during the period from floral bud emergence to anthesis can help to determine the temperature to be cumulated during the ripening phase. These findings are considerable contributions that could help producers to enhance the industrial management of safou sector, reduce fruit perishability, and better control their harvesting time.

This study set out to determine which environmental factors of growth and silvicultural practices can affect the properties of Eucalyptus robusta coppice wood and also to study variability in those properties depending on the factors. Hundred and thirty-five coppice logs aged 2 to 10 years were collected from five zones in the Highlands of Madagascar. Wood density at 12% moisture content was measured by X-ray microdensitometry. Chemical properties, such as the total extractives, Klason lignin and holocellulose contents were predicted using near infrared spectrometry prediction models. The results significantly showed (p-value<0,001) that wood density (543 – 836 kg/m3), total extractives (3,1 – 9,8%) and Klason lignin content (24,6 – 35,3%) increased with age, with the opposite occurring for holocellulose (63,8 – 69,9%). Wood density also varied significantly (p-value<0,001) depending on the zones, which was not the case for chemical properties. The densest woods were found at the hottest zones with less acid soils. Woods were less dense in zones, characterized by high rainfall and a soil rich in nitrogen and organic carbon. The plantation spacing, elevation of the zone and soil texture did not significantly affect wood properties.

Climate change is already affecting agro-ecosystems and threatening food security by reducing crop productivity and increasing harvest uncertainty. Mobilizing crop diversity could be an efficient way to mitigate its impact. We test this hypothesis in pearl millet, a nutritious staple cereal cultivated in arid and low-fertility soils in sub-Saharan Africa. We analyze the genomic diversity of 173 landraces collected in West Africa together with an extensive climate dataset composed of metrics of agronomic importance. Mapping the pearl millet genomic vulnerability at the 2050 horizon based on the current genomic-climate relationships, we identify the northern edge of the current areas of cultivation of both early and late flowering varieties as being the most vulnerable to climate change. We predict that the most vulnerable areas will benefit from using landraces that already grow in equivalent climate conditions today. However, such seed-exchange scenarios will require long distance and trans-frontier assisted migrations. Leveraging genetic diversity as a climate mitigation strategy in West Africa will thus require regional collaboration.

Fruit abscission facilitates the optimal conditions and timing of seed dispersal. Environmental regulation of tropical fruit abscission has received little attention, even though climate change may have its strongest impacts in tropical regions. In this study, oil palm fruit abscission was monitored during multiple years in the Benin Republic to take advantage of the climatic seasonality and the continuous fruit production by this species. An innovative multivariable statistical method was used to identify the best predictors of fruit abscission among a set of climate and ecophysiological variables, and the stage of inflorescence and fruit bunch development when the variables are perceived. The effects of climate scenarios on fruit abscission were then predicted based on the calibrated model. We found complex regulation takes place at specific stages of inflorescence and bunch development, even long before the fruit abscission zone is competent to execute abscission. Among the predictors selected, temperature variations during inflorescence and fruit bunch development are major determinants of the fruit abscission process. Furthermore, the timing of ripe fruit drop is determined by temperature in combination with the trophic status. Finally, climate simulations revealed that the abscission process is robust and is more affected by seasonal variations than by extreme scenarios. Our investigations highlighted the central function of the abscission zone as the sensor of environmental signals during reproductive development. Coupling ecophysiological and statistical modeling was an efficient approach to disentangle this complex environmental regulation.