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How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland
Abstract
The trees in agroforestry plots create spatial heterogeneity of high interest for adaptation, mitigation, and the provision of ecosystem services. But to what distance, exactly, from the tree? We tested a novel approach, based upon geostatistics and Unmanned Aerial Vehicle (UAV) sensing, to infer the distance at which a single agroforestry tree affects the surrounding under-crop, to map yield, litter (i.e. stover) and compute crop-partial Land Equivalent Ratio (LERcp) at the whole-plot level.
In an agro-silvo-pastoral parkland of semi-arid western Africa dominated by the multi-purpose tree Faidherbia albida, we harvested the pearl-millet under-crop at the whole-plot scale (ca. 1 ha) and also in subplot transects, at three distances from the trunks. We observed that the yield was three times higher below the tree crown (135.6 g m⁻²) than at a distance of five tree-crown radii from the trunk (47.7 g m⁻²). Through geostatistical analysis of multi-spectral, centimetric-resolution images obtained from an UAV overflight of the entire plot, we determined that the ‘Range’ parameter of the semi-variogram (assumed to be the distance of influence of the trees on the Normalized difference vegetation index (NDVI)) was 17 m. We correlated the yield (r² = 0.41; RRMSE = 48 %) and litter production (r² = 0.46; RRMSE = 35 %) in subplots with NDVI, and generated yield and litter maps at the whole-plot scale. The measured whole-plot yield (0.73 t ha⁻¹) differed from the one estimated via the UAV mapping by only 20 %, thereby validating the overall approach. The litter was estimated similarly at 1.05 tC ha⁻¹ yr⁻¹ and mapped. Using a geostatistical proxy for the sole crop, LERcp was estimated 1.16, despite the low tree density.
This new method to handle heterogeneity in agroforestry systems is a first application. We also propose strategies for extension to the landscape level.
... To the knowledge of the authors, only one study on spatial range of tree effects in AFS has utilized UAV-borne images to map crop yield yet, although prediction accuracy was low [47]. Roupsard et al. [47] used vegetation indices (VIs) as a proxy to model millet yield among other variables on field scale to compute the distance of influence of trees through geostatistical methods. ...
... To the knowledge of the authors, only one study on spatial range of tree effects in AFS has utilized UAV-borne images to map crop yield yet, although prediction accuracy was low [47]. Roupsard et al. [47] used vegetation indices (VIs) as a proxy to model millet yield among other variables on field scale to compute the distance of influence of trees through geostatistical methods. Geostatistics make use of the spatial autocorrelation of data and can account for directional variability of random variables. ...
... Detailed spatial analysis of tree effects on yield are of utmost importance for a solid understanding of tree-crop interactions occurring in agroforestry systems (AFS). However, only one study has made use of high-resolution unmanned aerial vehicle (UAV)borne imagery to study the range of tree effects on yield in an African AFS characterized by solitary trees [47]. No study has analyzed spatial variability of yield-related parameters in intensively managed alley cropping systems (ACS) at high spatial resolution utilizing UAV-borne data yet. ...
Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.
... The introduction of trees causes spatial heterogeneity in soil temperature and humidity (Monteith et al., 1991;Rao et al., 1997;Lin 2007) as well as soil microbial biomass abundance and composition (Chander et al., 1998;Guillot et al., 2021;Liu et al., 2019) and soil C stocks (Cardinael 2015). In a recent study performed in the same area as our study site, Roupsard et al. (2020) demonstrated that the whole pearl millet plant dry mass was 2.2 times higher under the Faidherbia tree crown than far from the tree. As a consequence, biomass inputs may be more important near trees, which could induce a modification of the soil chemical and physical properties. ...
... As a consequence, biomass inputs may be more important near trees, which could induce a modification of the soil chemical and physical properties. While the impact of trees on crop yield, climatic conditions and soil C stocks at a local scale was previously investigated in shallow soil horizons (Oelbermann et al., 2004;Oelbermann and Voroney 2007;Lin 2007;Roupsard et al., 2020), to our knowledge, no studies have investigated the impact of trees on deep soil characteristics or on tree and crop root decomposition. ...
In agroforestry systems, fine roots grow at several depths due to the mixture of trees and annual crops. The decomposition of fine roots contributes to soil organic carbon stocks and may impact soil fertility, particularly in poor soils, such as those encountered in sub-Sahelian regions. The aim of our study was to measure the decomposition rate of root litter from annual and perennial species according to soil depth and location under and far from trees in a sub-Sahelian agroforestry parkland.
Soil characteristics under and far from the trees were analysed from topsoil to 200 cm depth. Faidherbia tree, pearl millet and cowpea root litter samples were buried in litterbags for 15 months at 20, 40, 90 and 180 cm depths.
Root litter decomposition was mainly impacted by soil moisture and soil depth. Faidherbia decomposed more slowly (36 ± 12% remaining mass after 15 months) than cowpea and pearl millet roots (23 ± 7% and 29 ± 11% respectively). Pearl millet aboveground biomass, at harvesting time, was twice as high under (992 g m⁻²) than far (433 g m⁻²) from the tree, and belowground biomass (0–200 cm of depth) was 30.9 g m⁻² and 19.6 g m⁻² under and far from the tree, respectively. Faidherbia fine roots contributed slightly (p-value < 0.1) to higher stocks of C under the tree (7761 ± 346 g m⁻²) than far from it (5425 ± 558 g m⁻²) and from 0 cm down to 200 cm depth.
... Previous studies addressing the contribution of agroforestry to food security often relied on a simplified conceptualization of agricultural landscape diversity. Studies addressing the effects of trees on crop productivity mainly dealt with one tree species at a time (Bado et al., 2021;Ndoli et al., 2017;Roupsard et al., 2020;Sanou et al., 2012), whilst seldom considering tree diversity in the surrounding landscape of the field, and often only considering tree density or tree cover (Bado et al., 2021;Duriaux Chavarría et al., 2018;Hadgu et al., 2009;Leroux et al., 2020;Yang et al., 2020). However, it can be assumed that combinations of tree species lead to different effects on crop productivity and hence on food availability. ...
... We found, however, that tree cover was no longer positively associated with millet yield above a tree density of 5 trees/ha (Fig. 5a). Similarly, using a geostatistical approach, Roupsard et al. (2020) showed in a small area in the Groundnut Basin that a tree density of 10 trees/ha optimizes the benefit of trees on millet yield. The observed thresholds of tree density for crop productivity in parkland systems can be interpreted in the context of the balance between facilitation and competition between the trees and the associated crops for plant growth resources, i.e. light, water and nutrients Luedeling et al., 2016). ...
CONTEXT: Fostering diversity within agricultural systems can substantially contribute to improved food security among smallholder farmers. Agroforestry parklands are diverse agricultural landscapes where trees can provide an array of ecosystem services. Previous studies analyzing the agricultural landscape diversity-food security nexus in agroforestry parklands have only considered tree cover.
OBJECTIVE: We propose an original empirical approach that combines the analysis of spatial data on agricultural landscape diversity with agricultural field monitoring and household surveys. These three sources of data were used to scrutinize the direct and indirect contributions of agricultural landscape diversity to food availability and food access.
METHODS: Millet yield was used as a proxy for food availability, and household food access was approximated using the Household Food Insecurity Access Scale (HFIAS) indicator. Two contrasted agroforestry parklands of Central Senegal were chosen as case studies. Firstly, we used a Gradient Boosting Machine (GBM) algorithm to disentangle the relative contribution of landscape diversity, biophysical and crop management variables in
explaining millet yield variability. Secondly, we investigated the pathways linking agricultural landscape diversity to HFIAS using a Correlation Network Analysis (CNA).
RESULTS AND CONCLUSIONS: The GBM model explained 77% and 84% of millet yield variability for the two parklands, respectively, with landscape diversity variables accounting for 53% and 47% of relative influence. Among the landscape diversity variables, tree species richness and tree density were the most important ones. Millet yield was positively associated with tree density in the Nioro site until a threshold of 5 trees/ha, and with tree species richness in the two sites. The CNA showed that greater tree cover and larger tree patches were moderately correlated with HFIAS. This suggests that tree species with large crown, as it the case for most fruit bearing tree species in the region, are the main species contributing directly to food access. Agricultural landscape
diversity contributed mainly indirectly to household food access through an “agroecological pathway”, i.e. by the provision of ecosystem services regulating and supporting crop production.
SIGNIFICANCE: Using an integrated landscape approach relying on up-to-date remote sensing data and recent advances in data analysis methods, our study shows that tree species diversity matters as much as the amount of tree cover for the production of food, and it can contribute to improve food security. We bring a more nuanced picture of the contribution of agricultural landscape diversity to food security suggesting that land management policies supporting food security should consider both tree density and tree species diversity to optimize the cobenefits of trees for the different food security dimensions.
... The tree positively influences the variation of the herbaceous layer up to a minimum distance of 5 meters in August and a maximum of 15 meters from the crown (Figure 1). These results are consistent with those of Roupsard et al. (2020) who used spectral indices (NDVI and MSAVI2) taken by drone and geostatistics to assess the distance of influence of Faidherbia albida on millet crop yield. Their results show that Faidherbia albida no longer has an effect on the millet crop after 17 meters from the crown. ...
Sahelian savannah is composed of an annual herbaceous layer and a sparse tree community. The tree has a strong impact on the biomass and the species composition of the herbaceous layer due to microclimate and increase in fertility. In this work, we evaluated the distance of the tree impact on the herbaceous layers. We used an RGB UAV to produce biomass map and then evaluated the distance of the tree impacts. In 2020 in a rangeland in northern Senegal, three grass measurements were made every second day during the growth season combined with a UAV flight made with a parrot Anafi drone. At each date, we produced a biomass map and evaluated the distance of the tree impact using geostatic method. We obtained a calibration between UAV and field measurement with an R² equal to 0.64. The impacts of the tree were ranging from 5 m in the beginning of the wet season to 15 m at the end of the wet seasons. This work shows the distance of the tree impact on the grass layer in savannah. The evaluation of this impact could be helpful for the management of the tree layers to increase the quantity of grass for the pastoralism.
... [Tscharntke et al., 2011;Santos et al., 2018]). Several studies suggest that shade cover provides services such as climate regulation, erosion control, water supply, and water infiltration, among others (van Oijen et al., 2010;Meylan et al., 2017;Shi et al., 2018;Roupsard et al., 2020). While a common statement is that shade cover (i.e., low to moderate) is more important for ES provisioning than no shade at all (Asbjornsen et al., 2013;Kuyah et al., 2019), limited work has been performed to understand how shade cover and a range of practices contribute to the relationships between yield gaps and ESs across different management types. ...
The productivity of agroforestry systems (AFSs) and the provisioning of associated ecosystem services (ESs) are threatened by increasing cropping intensification and climate change. Compared to full-sun coffee, maintaining shade cover might protect against climate variability, forest degradation, and pests/diseases attack. However, there may be trade-offs between yields and ESs. While the impacts of shade have been reviewed, a global synthesis to understand how shade trees and a range of practices and biophysical factors contribute to yield gaps and ESs across management types is lacking. This research aims at integrating quantitative data on management practices (shade cover percentage, tree/coffee plant densities, and nutrient inputs), coffee/tree characteristics, soil properties, topographic attributes and climate, in order to compare the responses of coffee yields, biodiversity and ES supply across management types. We conducted a meta-analysis with a total of 142 papers that fulfilled the inclusion criteria and analyzed the individual and combined effects of these factors. The results show that factors such as shade cover and tree/plant densities affected more the yields than climatic and biotic factors. AFSs under moderate shade cover levels (35–50%), with tree densities between 100 and 250 trees ha⁻¹, at low altitudes, and with steeper slopes provided as much yields as full-sun systems (4.1 ± 2.88 tons ha⁻¹ yr⁻¹). While increased shade cover (>51%) decreased yields, the results also show that more diversified AFSs can support biodiversity conservation and provide ESs such as stored carbon, infiltration, pollination, and water runoff. The generalized linear models (GLMs) analyses showed that coffee yield variations are highly context-dependent and factors such as the incidence of pest/leaf rust, altitude, soil quality, available water, and biodiversity may play an important role. The novelty of this research is that the effect of shade, coffee plants, and input management practices is taken into account, along with the effects of a range of site-specific biophysical factors; along with the application of a case study to test robustness of the meta-analysis. This study provides an understanding of the effects of management type gradients on coffee yield and whether the low yields in AFSs compared to conventional coffee monocultures, are compensated by the provisioning of ESs.
... The most critical stance may come from conservation biology, where particularly intensively managed agroforestry systems can be seen as harmful to biodiversity (Rolim and Chiarello, 2004;Santos-Heredia et al., 2018). Additionally, some studies claim that some shade-tree species reduce the yields of the crops they shade (Santos et al., 2012); however, other studies show the opposite (Roupsard et al., 2020). By far, the most common point in the literature on agroforestry is the benefits it brings, whether to the producer (farmer, peasant, etc.), to the environment (biodiversity), to the economy (increased returns) or to all of the above (Jose, 2009). ...
This article canvasses the current definitions and framings of “agroforestry” in different academic literature and policies. Three key framings of “agroforestry” are identified in the scholarship and explored for their differences. The findings suggest that the distinct schools of research on “agroforestry” focus on distinct points of departure, and these baseline situations from which transitions to what is called “agroforestry” occur vary in distinct ways from monoculture plantations to primary forests. Political-economic analysis is used to scrutinize three key “agroforestry” transition categories: agroecological, agribusiness, and forest degradation, which the article identifies as agroecoforestry (the good), agrobizforestry (the bad), and agrodeforestry (the ugly) transitions, respectively. Examples of each type are provided based on field research in Brazil, and the results are put into a global perspective. The categories are helpful in identifying the “agroforestry” transitions that are currently marketed as good solutions but might also have negative impacts and in highlighting the agroecological agroforestry transitions that would help simultaneously increase global food production, adapt to and mitigate the climate crisis, and achieve equity and social justice.
... Tamburini et al., 2016Morris et al., 2010;Berner et al., 2008;Holland, 2004;Ö stman et al., 2001 3. Lundgren andFergen, 2011;Poeplau and Don, 2015;Blanco Canqui et al., 2015;Miguez and Bollero, 2005;Dabney et al., 2001;Tiemann et al., 2015 4. Liebman andDyck, 1993;Lacombe et al., 20095. Beuschel et al., 2020Roupsard et al., 2020;Bentrup et al., 2019;Zhou et al., 20116. Bennett et al., 2012Holzschuh et al., 2007;Venter et al., 2016;Rusch et al., 20137. ...
CONTEXT
Ecological intensification (EI) describes farming practices that aim to use ecological processes for producing agricultural yields. While evidence for the ecological benefits of EI is plentiful, the question of how it can be more widely adopted by farmers, and why it has not been so far, remains pertinent, since only approximately 9% of the globally farmed land is currently managed with EI practices. We suggest that considering farmers as central while attending to farm and system level factors can help to identify barriers and facilitators to EI adoption. To do this, we look to diverse, overlapping bodies of literature encompassing EI practice details, systems thinking, and farmer adoption. Innovation characteristics is one framework that has been used to study farmer adoption of new farm management tools and practices.
OBJECTIVE
Our objective is to use innovation characteristics for identifying farmer, farm, and system level barriers and potential solutions to EI adoption. We then aim to synthesize broader lessons for a sustainability transition in agriculture.
METHODS
We treat EI as a suite of innovations, including practices, technologies, and knowledge. We explore how the innovation characteristics of EI - i.e. their relative advantage, compatibility, complexity, trialability and observability – manifest at each of three levels: farmer, farm, and system. We apply our approach to three case studies of EI adoption from different world regions: 1) managing landscape complexity in Germany, 2) installation of riparian buffers in the USA and 3) organic farming in India.
RESULTS AND CONCLUSIONS
An analysis of our case studies using innovation characteristics helped identify barriers to EI adoption and at what level these barriers should be addressed. Barriers included: uncoupled financial and farm-level ecological relative advantages of EI (system level), framing that is not in line with farm values and needs (farmer level), insufficient training for managing complex systems (farmer level), and time constraints for experimentation with and observation of EI effects (farm level). System level solutions could support training and experimentation with EI, empowering farmers by providing them with autonomy to adapt and apply EI as they see fitting.
SIGNIFICANCE
Using innovation characteristics and diverse bodies of knowledge allowed us to identify barriers, but also opportunities at farmer, farm, and system level. Abundant work focuses on convincing farmers of what science says is right, so showing farmers that barriers are not explicit to them is a helpful step forward in the transition towards sustainability.
... A difference in leaf phenology can impact water use efficiency, gaseous exchange, tree growth and productivity of accompanying crops (Muthuri et al. 2009). In exceptional cases, such phases can even occur during periods that feature climatically favourable conditions, as has been widely reported for Faidherbia albida, a species that thrives in semi-arid environments, even though it loses its leaves during the wet season (Roupsard et al. 1999(Roupsard et al. , 2020. What drives such transitions is often unclear, with variation in photoperiod (Heide, 2008), temperature (Guo et al. 2015), water availability, and insolation (Borchert et al. 2015) reported as influential factors in various situations. ...
Agroforestry (AF)-based adaptation to global climate change can consist of (1) reversal of negative trends in diverse tree cover as generic portfolio risk management strategy; (2) targeted, strategic, shift in resource capture (e.g. light, water) to adjust to changing conditions (e.g. lower or more variable rainfall, higher temperatures); (3) vegetation-based influences on rainfall patterns; or (4) adaptive, tactical, management of tree-crop interactions based on weather forecasts for the (next) growing season. Forty years ago, a tree physiological research tradition in aboveground and belowground resource capture was established with questions and methods on climate-tree-soil-crop interactions in space and time that are still relevant for today’s challenges. After summarising early research contributions, we review recent literature to assess current levels of uncertainty in climate adaptation assessments in and through AF. Quantification of microclimate within and around tree canopies showed a gap between standard climate station data (designed to avoid tree influences) and the actual climate in which crop and tree meristems or livestock operates in real-world AF. Where global scenario modelling of ‘macroclimate’ change in mean annual rainfall and temperature extrapolates from climate station conditions in past decades, it ignores microclimate effects of trees. There still is a shortage of long-term phenology records to analyse tree biological responses across a wide range of species to climate variability, especially where flowering and pollination matter. Physiological understanding can complement farmer knowledge and help guide policy decisions that allow AF solutions to emerge and tree germplasm to be adjusted for the growing conditions expected over the lifetime of a tree.
... Parklands are an important agroforestry system widespread across the Sudano-Sahelian zone of West Africa [1,2]. In these landscapes, agriculture and livestock production systems are integrated under a sparse cover of scattered trees. ...
Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Saponé, Burkina Faso, a test site characterised by a low average canopy cover (~15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January–April 2018 are used jointly to create high-resolution (~3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees.
... Although the measured reduction in air temperature was less than 1 • C, the expected reduction in heat stress for livestock is much higher due to the reduction in direct incoming solar radiation. Roupsard et al. (2020) presented a method of spatial analysis to determine how far trees affect crop growth in parkland agroforestry systems. Based on geostatistics and the use of unmanned aerial vehicle (UAV) that determined crop biomass, they found that pearl millet (Pennisetum glaucum (L.) R.Br.) yield was three times higher immediately below Faidherbia albida (Delile) A. Chev. ...
... In Sudan it was estimated that scattered trees of Faidherbia albida have increased the harvests of surrounding cereals and groundnut up to 200% (Fadl and El sheikh, 2010). Pearl millet yield was recently found to be three times higher below the tree crown than at five tree-crown radii (Roupsard et al., 2020). ...
Agroforestry generally contributes to rural food and nutrition security (FNS). However, specialization on commodity-oriented agroforestry practices or management strategies can weaken local food sourcing when terms of trade fluctuate, as is the case of coffee in Ethiopia. Hence, this study assessed the trade-offs that smallholder farming households in south-western Ethiopia face between growing coffee in agroforestry systems and their food and nutrition security based on home production as well as markets. Data collected from 300 randomly selected households included: (i) attributes of agroforestry practices (AFP) and plants: structure, use type, edibility, marketability, nutritional traits, and (ii) the householders' FNS attributes: food security status, nutritional adequacy, and nutritional status. Data were collected both in food surplus and shortage seasons, during and after coffee harvesting. Within these data, the number of plant species and vegetation stories were significantly correlated with household food access security in both seasons and for all AFP identified, i.e., homegarden, multistorey-coffee-system, and multipurpose-trees-on-farmlands. The number of stories in homegardens and the richness of exotic species in multipurpose-trees-on-farmlands were significantly correlated with the biometric development of children below 5 years old during the shortage season. The richness of “actively-marketed” species in all AFP correlated with the food access security of the household, except in the multistorey-coffee-system, oriented to coffee production. Also, families that cultivate all three AFP showed significantly higher household diversity dietary during the shortage season. We conclude that no single AFP can secure FNS status of the households by itself, but the combination of all three can. Household and individual dietary scores were positively correlated with the AFP diversity-attributes, especially in the shortage season. Thus, the diversity of useful groups of plant species deserves to be promoted for instance by enriching AFP with edible and storable crops needed during the shortage season.
... Since the early 2000's several eddy covariance fluxtowers have been measuring land-atmosphere exchanges of CO 2 and water in this region (Tagesson et al., 2016b). These fluxtowers are located in a grassland savanna ) and a Faidherbia agroforestry plot (Roupsard et al., 2020) in Senegal, woody savannas and open woodland in Mali (Mougin et al., 2009), fallow bush ) and a millet plantation in Niger, and a woody savanna site in Sudan (Sjöström et al., 2009). Data collected at these fluxtower sites significantly increased our understanding of these ecosystems. ...
Drylands form a major component of our Earth’s land surface. These ecosystems encompass several biomes, such as dry forests, savannas, grasslands, shrublands and deserts. More than 30% of the global human population lives in dryland ecosystems and depends on the ecosystem services that drylands provide. However, these ecosystems are subject to climate extremes that are projected to increase in frequency and severity under most future climate scenarios. Such extremes can have a devastating impact on the ecosystems and livelihoods of global drylands. Drylands account for a large fraction of the total land carbon sink, and have been shown to dominate its trend and year-to-year variability. Despite their global importance, drylands remain severely understudied and especially a detailed optimization of vegetation models is lacking. In this thesis I contributed to resolve this problem. Based on data from our own field work, I parameterized two dynamic vegetation models (LPJ-GUESS and ED2.2) to dryland conditions, specifically the Sudano-Sahel region. My optimized parameterization enables these models to realistically simulate carbon and water fluxes that were measured at several fluxtower sites across the Sahel. Using the LPJ-GUESS model I then studied how the distribution of rainfall over the rainy season can impact dryland ecosystems at the site level. Using the ED2.2 model I studied how access to the perennial soil moisture layer can influence dry season water use by deep rooted trees. In a next step I upscaled the LPJ-GUESS model to the regional level and I evaluated its simulations of the Sudano-Sahel vegetation against satellite data. Using this regional model I then studied how soil texture can influence dryland leaf cover and ecosystem composition. While soil texture had almost no impact at the ecosystem scale, at the plant level soil texture strongly shifted the competitive balance between evergreen and deciduous woody species. In another chapter I studied the impact of rainfall variability and found that an increased year-to-year rainfall variability can decrease dryland ecosystem productivity, offseting the gains by CO2 fertilization, especially for southern regions. By using and tuning dynamic vegetation models for simulating dryland vegetation, this thesis provides a unique insight into dryland ecosystem functioning.
... These indices can then be linked to different biophysical vegetation variables, particularly those of the herbaceous layer of crops or natural species. These indices can be combined with volumes obtained from the 3D model, then used to assess yield or biomass using multispectral mosaics for grassland (Pecina et al., 2021) or cropland (Roupsard et al., 2020). Some studies only used RGB indices (Lussem et al., 2019). ...
The phytomass of herbaceous and woody plants is the main source of feed for pastoral livestock in the Sahelian savanna. The assessment of the available feedstock plays a key role in national livestock policies and generally requires many field measurements of both herbaceous and woody plants. In this study, we tested the possibility of using a red-green-blue (RGB) unmanned aerial vehicle (UAV) to evaluate the phytomass of both woody and herbaceous species. We thus mapped 38 one hectare plots with a Dji Spark UAV in Northern Senegal. The herbaceous phytomass was measured on the ground. For the woody communities, we evaluated the leaf phytomass using dendrometric parameters combined with allometric equations. We performed partial-least square regressions between UAV-based three-dimension and color indices and phytomass. Results showed a Q² (cross validation results for each response variable) of 0.57 for woody phytomass, 0.68 for herbaceous dry mass, and 0.76 for their fresh mass. This study confirmed the relevance of using low-cost RGB UAV to assess savanna phytomass.
... Faidherbia albida trees are found in parklands throughout the Sudano-Sahelian zone of West Africa, as well as in eastern and southern Africa [10][11][12][13][14]. Several scholars have demonstrated that Faidherbia albida trees improve crop yields as well as agricultural resilience and sustainability [15][16][17][18][19]. Roupsard [20] used unmanned aerial vehicles (UAV) to derive detailed field data to quantify the radius where Faidherbia albida trees influence crop yields in an agroforestry parkland in Senegal. Accurate mapping of Faidherbia albida trees is therefore important for a better understanding and management of the interaction of trees with crops within the agricultural system (species, density, planting design, etc.). ...
Multipurpose Faidherbia albida trees represent a vital component of agroforestry park-lands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faid-herbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidher-bia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.
... & Investigate spatial dynamics. Future studies should better document lateral spatial heterogeneities generated in such systems (Battie-Laclau et al. 2020;Marsden et al. 2020), depending on the types of agroforestry systems and spatial arrangement of trees, and how these affect soil functions and related ecosystem services (Cardinael et al. 2017(Cardinael et al. , 2019bRoupsard et al. 2020). Beyond the role of trees, that of the herbaceous cover under the trees shall also be better accounted for (Cardinael et al. 2017(Cardinael et al. , 2018aBattie-Laclau et al. 2020), whenever present, or the contribution of grazing animals, when relevant, as they are likely to be additional sources of heterogeneity. ...
Editorial of the special issue "Agroforestry: a belowground perspective" publihsed here:
https://link.springer.com/journal/11104/volumes-and-issues/453-1
Droughts are extreme events that have major impacts on communities, ecosystems and economies due to slow onset and complex processes. Land and ecosystem degradation increase the risks of loss and damage during droughts, whereas well-adapted practices and policies can enable society to (re)build resilience. This review highlights actions needed to connect and fill gaps in the present systems for ecological and hydrological monitoring, governance, and alignment of economic incentives at regional, national and local scales. Stopping the slow-burning fuse of drought damage requires improved tracking and reversal of the observable slow-onset nature of hydrological and socio-economic drought. International scientific and technical cooperation to better map and quantify changing loss and damage risks could provide evidence-based action triggers.
West African Sahelian and Sudanian ecosystems
provide essential services to people and also play a significant role
within the global carbon cycle. However, climate and land use are
dynamically changing, and uncertainty remains with respect to how these changes will affect
the potential of these regions to provide food and fodder resources or how they will affect
the biosphere–atmosphere exchange of CO2. In this study, we investigate
the capacity of a process-based biogeochemical model, LandscapeDNDC, to
simulate net ecosystem exchange (NEE) and aboveground biomass of typical
managed and natural Sahelian and Sudanian savanna ecosystems. In order to
improve the simulation of phenology, we introduced soil-water availability
as a common driver of foliage development and productivity for all of these
systems. The new approach was tested by using a sample of sites (calibration
sites) that provided NEE from flux tower observations as well as leaf area index
data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy,
we applied the calibrated model to 42 additional sites (validation sites)
across West Africa for which measured aboveground biomass data were
available. The model showed good performance regarding biomass of crops,
grass, or trees, yielding correlation coefficients of 0.82, 0.94, and 0.77 and
root-mean-square errors of 0.15, 0.22, and 0.12 kg m−2, respectively.
The simulations indicate aboveground carbon stocks of up to 0.17, 0.33, and
0.54 kg C ha−1 m−2 for agricultural, savanna grasslands, and
savanna mixed tree–grassland sites, respectively. Carbon stocks and exchange rates were particularly
correlated with the abundance of trees, and grass biomass and crop yields were higher under more humid climatic conditions. Our
study shows the capability of LandscapeDNDC to accurately simulate carbon
balances in natural and agricultural ecosystems in semiarid West Africa
under a wide range of conditions; thus, the model could be used to assess the
impact of land-use and climate change on the regional biomass productivity.
Understanding how the spatial organization of diversified plant communities alters their performance is an important step in designing and managing diversified agroecosystems. The high level of spatial heterogeneity in tropical agroforests makes this task challenging. In 19 agroforestry plots in Talamanca (Costa Rica), we analyzed the effect of the structure of the plant community in the neighborhood of each individual cacao tree and banana plant on their growth and yield parameters. We developed an individual-based analysis in two steps. First, we selected without a priori the distance at which the number of neighboring plants of a given functional group (banana plants, cacao trees, fruit trees, or wood trees) best explained the proportion of attainable yield (PAY) of cacao and banana plants. In a second step, we tested the significance of the abundances of the four groups of plants in a complete model that predicted the PAY of banana and cacao plants. The abundance of neighboring plants did not increase banana PAY except in the case of other banana plants, suggesting that banana plants yield better when aggregated. All other groups of plants tended to reduce both banana and cacao PAY. In the case of wood trees, these trends were not significant. Interestingly, our results suggests that it is possible to associate banana plants and cocoa trees to moderate densities of other plants without reducing their yield. The two complete linear models predicted about 60 % of the variance of the average response of the PAY to the neighboring plant assemblage. We suggest that in the future, it would be important to differentiate processes (resources partitioning, pest and diseases) inside our statistical approach. While requiring much more data, it could be useful to address the effect of cultural practices.
West African Sahelian and Sudanian ecosystems are providing essential services to people and also play a significant role within the global carbon cycle. However, climate and land use are dynamically changing and it remains uncertain how these changes will affect the potential of these regions for providing food and fodder resources or the biosphere-atmosphere exchange of CO2. In this study, we investigate the capacity of a process-based biogeochemical model, LandscapeDNDC, to simulate net ecosystem exchange (NEE) and aboveground biomass of typical managed and natural Sahelian and Sudanian savanna ecosystems. We tested the model for various sites with different proportions of trees and grasses, as well as for the most typical arable cropping systems of the region. In order to describe the phenological development with a common parameterization across all ecosystem types, we introduced soil-water availability in addition to temperature as a driver as seasonal soil water-shortage is a common feature for all these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations and leaf area index data from satellite images (MODIS). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed a good performance regarding simulated biomass development. Overall, the comparison of simulated and observed biomass at sites with a dominating land cover of crops, grass or trees yielded correlation coefficients of 0.82, 0.94, and 0.77 and the Root Mean Square Error of 0.15, 0.22, and 0.12 kg m−2, respectively. In absolute terms, the model results indicate above-ground carbon stocks up to 1733, 3291, and 5377 kg C ha−1 yr−1 for agricultural, savanna grasslands, and savanna mixed tree-grassland sites. Carbon stocks as well as exchange rates correlated in particular with the abundance of trees. The simulations indicate higher grass biomass and crop yields under more humid climatic conditions as can be found in the Sudanian savanna region. Our study shows the capability of LandscapeDNDC to accurately simulate carbon balances in natural and agricultural ecosystems in semi-arid West Africa under a wide range of conditions, so that it might be used to assess the impact of land-use and climate change on the regional biomass productivity.
Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry’s small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.
In Africa, adaptation will be crucial to offset expected negative climate change impacts on food security and agriculture development. In this study, we combine meteorological data from 18 local stations, field surveys on agricultural practices and agronomic information on the growth of millet to demonstrate the crop suitability to the present climate and the ability of Senegalese farmers to adapt their practices to climate variability, and to disseminate them. From data collected in both 665 villages and 1061 farmers, our study provides quantitative evidence of the responsive adaptation of farmers in the Sahel where the recent resumption of rainfall has provided new agricultural opportunities. Statistical models and cropping simulations show that these farmers innovate by reintroducing and disseminating a long cycle millet cultivar—more suitable for wet environments. We note that although this adaptation is a clear response to recent changes in quantity and distribution of rainfall, its adoption remains limited (50% of the villages visited and 25% of the surveyed agricultural producers have cultivated the new millet variety) and varies strongly within the same climatic context and by characteristics of farmers (willing and capacity), indicating different agricultural strategies (diversification, market exchanges). If land access and development of cash crops are hindrances to the adoption of sanio, poverty is clearly not a barrier and adaptation is not a lever for wealth creation. Such adaptative capacities, together with government incentives for farmers to sustainably adapt to climate change, can be important in reducing climate risks in the coming years.
Agroforestry, the intentional integration of trees with crops and/or livestock, can lead tomultiple economic and ecological benefits compared to trees and crops/livestock grown separately.Field experimentation has been the primary approach to understanding the tree–crop interactionsinherent in agroforestry. However, the number of field experiments has been limited by slow treematuration and difficulty in obtaining consistent funding. Models have the potential to overcomethese hurdles and rapidly advance understanding of agroforestry systems. Hi‐sAFe is a mechanistic,biophysical model designed to explore the interactions within agroforestry systems that mix treeswith crops. The model couples the pre‐existing STICS crop model to a new tree model that includesseveral plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water,and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of theland equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representingmany competition and facilitation processes. Hi‐sAFe is a novel tool for exploring agroforestrydesigns (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning,branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation(e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). Byimproving our understanding of the complex interactions within agroforestry systems, Hi‐sAFe canultimately facilitate adoption of agroforestry as a sustainable land‐use practice.
In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.
Agroforestry systems comprise trees and crops, or trees and pastures within the same field. Globally, they cover approximately 1 billion hectares of land and contribute to the livelihoods of over 900 million people. Agroforestry systems have the capacity to sequester large quantities of carbon (C) in both soil and biomass. However, these systems have not yet been fully considered in the approach to C accounting developed by the Intergovernmental Panel on Climate Change (IPCC), largely due to the high diversity of agroforestry systems and scarcity of relevant data. Our literature review identified a total of 122 scientific, peer-reviewed articles associated with biomass C storage (50) and with soil organic carbon (SOC) (72), containing of total of 542 observations (324 and 218, respectively). Based on a synthesis of the reported observations, we are presenting a set of Tier 1 coefficients for biomass C storage for each of the 8 main agroforestry systems identified, including alley cropping, fallows, hedgerows, multistrata, parklands, shaded perennial-crop, silvoarable and silvopastoral systems, disaggregated by climate and region. Using the same agroforestry classification, we are presenting a set of stock change factors (FLU) and SOC accumulation/loss rates for three main land use changes: cropland to agroforestry; forest to agroforestry; and grassland to agroforestry. Globally, the mean SOC stock change factors (± confidence intervals) were estimated to be 1.25 ± 0.04, 0.89 ± 0.07, and 1.19 ± 0.10, for the three main land use changes, respectively. However, these average coefficients hide huge disparities across and within different climates, regions, and types of agroforestry systems, highlighting the necessity to adopt the more disaggregated coefficients provided herein. We encourage national governments to synthesize data from local field experiments to generate country-specific factors for more robust estimation of biomass and SOC storage.
The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (Beta vulgaris L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (Cab), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents. Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GFGREENPIX and VICAB) extracted from the images are shown to play a major role in these performances. GFGREENPIX is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (VARI) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VICAB is the mNDblue index value averaged over the darkest green pixels, and provides critical information on Cab. When exploited within uni- or multivariate empirical models, these two variables improve the GF, GAI, Cab, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (R2 = 0.90) are obtained by multiplying Cab and GAI estimates respectively derived from VICAB and a log-transformed version of GFGREENPIX, log(1-GFGREENPIX). The GFGREENPIX and VICAB variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.
Strategies that strengthen and use biodiversity are crucial for sustained food production and livelihoods in semi-arid West Africa. The objective of this paper was to examine the role of biodiversity in sustaining diverse forms of multifunctional farming practices while at the same time providing ecological services to subsistence-oriented farming families in the region of study through mechanisms as (a) crop species diversification, (b) management of spatial heterogeneity, and (c) diversification of nutrition-sensitive landscapes. Our analysis shows that crop associations between cereals and legumes or between perennials and annuals, have overall positive effects on soil characteristics and often improve crop yields. Soil heterogeneity is produced by woody perennials and termites. Local management provides opportunities to collect a diversity of nutrition-rich species year-round and sustain household nutrition.
Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments. Vegetation indices from UAV images strongly related to ground cover (R² = 0.85), light interception (R² = 0.79) and vegetation indices derived from satellite images (R² values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q² values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn't fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.
Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.
Greater understanding of the influences on long-term coffee productivity are needed to develop systems that are profitable, while maximizing ecosystem services and lowering negative environmental impacts. We examine a long-term experiment (15 years) established in Costa Rica in 2000 and compare intensive conventional (IC) coffee production under full sun with 19 agroforestry systems combining timber and service tree species with contrasting characteristics, with conventional and organic managements of different intensities. We assessed productivity through coffee yield and coffee morphological characteristics. IC had the highest productivity but had the highest yield bienniality; in the agroforestry systems productivity was similar for moderate conventional (MC) and intensive organic (IO) treatments (yield 5.3 vs 5.0 t/ha/year). Significantly lower yields were observed under shade than full sun, but coffee morphology was similar. Low input organic production (LO) declined to zero under the shade of the non-legume timber tree Terminalia amazonia but when legume tree species were chosen (Erythrina poepiggiana, Chloroleucon eurycyclum) LO coffee yield was not significantly different than for IO. For the first 6 years, coffee yield was higher under the shade of timber trees (Chloroleucon and Terminalia), while in the subsequent 7 years, Erythrina systems were more productive, presumably this is due to lower shade covers. If IC full sun plantations are not affordable or desired in the future, organic production is an interesting alternative with similar productivity to MC management and in LO systems incorporation of legume tree species is shown to be essential.
Agroforestry is an increasingly popular farming system enabling agricultural diversification and providing several ecosystem services. In agroforestry systems, soil organic carbon (SOC) stocks are generally increased, but it is difficult to disentangle the different factors responsible for this storage. Organic carbon (OC) inputs to the soil may be larger, but SOC decomposition rates may be modified owing to microclimate, physical protection, or priming effect from roots, especially at depth. We used an 18-year-old silvoarable system associating hybrid walnut trees (Juglans regia × nigra) and durum wheat (Triticum turgidum L. subsp. durum) and an adjacent agricultural control plot to quantify all OC inputs to the soil – leaf litter, tree fine root senescence, crop residues, and tree row herbaceous vegetation – and measured SOC stocks down to 2 m of depth at varying distances from the trees. We then proposed a model that simulates SOC dynamics in agroforestry accounting for both the whole soil profile and the lateral spatial heterogeneity. The model was calibrated to the control plot only.
Farmers in the semi-arid regions of West Africa face challenges related to poor crop establishment, variable rainfall, low soil fertility and a shortage of labour at times of peak demand. Farmers are generally low on resources. Given these conditions, it is important to develop farming practices that make efficient use of the available resources and reduce risks. Here, we review agricultural intensification in semi-arid West Africa using the principles of precision farming to assess the possibilities they offer. The basic idea is to create a favourable micro-environment in the planting pocket and to ensure timely sowing and weeding. In the context of precision farming in the semi-arid West Africa, this means (1) large seeds are selected, primed and treated with a mix of pesticides/fungicides. Seed priming increases yields in the order of 20 to 30%, while seed treatment increases yields by 15%. (2) Mineral fertilizers are applied; at doses as low as 0.3 g of fertilizer per pocket, they have been found to increase yields by half or more. (3) Seeds and fertilizers are distributed accurately by means of a combined planter-weeder, which can be motorized. (4) Mechanized sowing and weeding enable timely farm operations and reduce the workload. (5) Water loss is prevented by using zaï and stone bunds on soils with high run-off rates. (6) Care is taken to make use of farm resources in a targeted and efficient way. This can imply adjusting micro-doses of manure and fertilizer to crops (sorghum needs less than millet) and soil types, sequenced sowing of crops according to their vulnerability to delayed sowing and applying organic input to soils. This paper is the first to review agricultural intensification in semi-arid West Africa within the context of precision farming. It shows how a low-cost package for precision farming can be developed, which can help to increase land and labour productivity, and works with all the major field crops in the region.
In agroforestry systems, shade trees strongly affect the physiology of the undergrown crop. However, a major paradigm is that the reduction in absorbed photosynthetically active radiation is, to a certain extent, compensated by an increase in light-use efficiency, thereby reducing the difference in net primary productivity between shaded and non-shaded plants. Due to the large spatial heterogeneity in agroforestry systems and the lack of appropriate tools, the combined effects of such variables have seldom been analysed, even though they may help understand physiological processes underlying yield dynamics. In this study, we monitored net primary productivity, during two years, on scales ranging from individual coffee plants to the entire plot. Absorbed radiation was mapped with a 3D-model (MAESPA). Light-use efficiency and net assimilation rate were derived for each coffee plant individually. We found that although irradiance was reduced by 60% below crowns of shade trees, coffee light-use efficiency increased by 50%, leaving net primary productivity fairly stable across all shade levels. Variability of aboveground net primary productivity of coffee plants was caused primarily by the age of the plants and by intraspecific competition among them (drivers usually overlooked in the agroforestry literature) rather than by the presence of shade trees.
Tree diversity in West Africa is threatened by intensified land uses and salinization, and farmers’ role in conservation of tree species is unclear. We hypothesized that farmers contribute to conservation of tree diversity through protection of trees in their agroforestry landscapes and compared the diversity and structure of the tree vegetation across landscape classes. Inventories were carried out in three villages in the Groundnut Basin in Senegal, assessing tree diversity, density and crown cover. Tree diversity as assessed by species accumulation curves was high in forests, but cultivated landscapes had comparable or almost comparable diversity, especially in the cases where the forest was planted or was affected by charcoal production. However, the occurrence of exotic species was higher in cultivated parts of the landscape, and although many species were in common, ordination plots indicated that forests and cultivated landscapes to some degree had different species composition. Salinity had a strong influence on vegetation, not only in the tans (salt marshes) but also across the other landscape classes. In conclusion, agroforestry landscapes in the three villages harbor considerable tree diversity, but insufficient to fully conserve the tree species. We argue that informing and including farmers in tree management in the region will contribute to overall conservation of tree genetic resources.
The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite Systems (GNSS) and high quality and cost-effective sensors; second, to advice the target audience – mostly farmers and foresters – how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels’ size and crop’s types.Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors’ data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions.
Sorted soil circles are a form of periglacial patterned ground that is
commonly noted for its striking geometric regularity. They consist of an
inner fine domain bordered by gravel rings that rise some decimetres above
the fine domain. Field measurements and numerical modelling suggest that
these features develop from a convection-like circulation of soil in the
active layer of permafrost. The related cyclic burial and exhumation of
material is believed to play an important role in the soil carbon cycle of
high latitudes. The connection of sorted circles to permafrost conditions and
its changes over time make these ground forms potential palaeoclimatic
indicators. In this study, we apply for the first time photogrammetric
structure-from-motion technology (SfM) to large sets of overlapping
terrestrial photos taken in August 2007 and 2010 over three sorted circles at
Kvadehuksletta, western Spitsbergen. We retrieve repeat digital elevation
models (DEMs) and orthoimages with millimetre resolution and precision.
Changes in microrelief over the 3 yr are obtained from
DEM differencing and horizontal displacement fields from tracking features
between the orthoimages. In the fine domain, surface material moves radially
outward at horizontal rates of up to ~2 cm yr−1. The coarse
stones on the inner slopes of the gravel rings move radially inward at
similar rates. A number of substantial deviations from this overall radial
symmetry, both in horizontal displacements and in microrelief, shed new light
on the spatio-temporal evolution of sorted soil circles, and potentially of
periglacial patterned ground in general.
Background and aims:
In Costa Rica, coffee (Coffea arabica) plants are often grown in agroforests. However, it is not known if shade-inducing trees reduce coffee plant biomass through root competition, and hence alter overall net primary productivity (NPP). We estimated biomass and NPP at the stand level, taking into account deep roots and the position of plants with regard to trees.
Methods:
Stem growth and root biomass, turnover and decomposition were measured in mixed coffee/tree (Erythrina poeppigiana) plantations. Growth ring width and number at the stem base were estimated along with stem basal area on a range of plant sizes. Root biomass and fine root density were measured in trenches to a depth of 4 m. To take into account the below-ground heterogeneity of the agroforestry system, fine root turnover was measured by sequential soil coring (to a depth of 30 cm) over 1 year and at different locations (in full sun or under trees and in rows/inter-rows). Allometric relationships were used to calculate NPP of perennial components, which was then scaled up to the stand level.
Key results:
Annual ring width at the stem base increased up to 2·5 mm yr(-1) with plant age (over a 44-year period). Nearly all (92 %) coffee root biomass was located in the top 1·5 m, and only 8 % from 1·5 m to a depth of 4 m. Perennial woody root biomass was 16 t ha(-1) and NPP of perennial roots was 1·3 t ha(-1) yr(-1) Fine root biomass (0-30 cm) was two-fold higher in the row compared with between rows. Fine root biomass was 2·29 t ha(-1) (12 % of total root biomass) and NPP of fine roots was 2·96 t ha(-1) yr(-1) (69 % of total root NPP). Fine root turnover was 1·3 yr(-1) and lifespan was 0·8 years.
Conclusions:
Coffee root systems comprised 49 % of the total plant biomass; such a high ratio is possibly a consequence of shoot pruning. There was no significant effect of trees on coffee fine root biomass, suggesting that coffee root systems are very competitive in the topsoil.
Agroforestry systems and tree cover on agricultural land make an important contribution to climate change mitigation, but are not systematically accounted for in either global carbon budgets or national carbon accounting. This paper assesses the role of trees on agricultural land and their significance for carbon sequestration at a global level, along with recent change trends. Remote sensing data show that in 2010, 43% of all agricultural land globally had at least 10% tree cover and that this has increased by 2% over the previous ten years. Combining geographically and bioclimatically stratified Intergovernmental Panel on Climate Change (IPCC) Tier 1 default estimates of carbon storage with this tree cover analysis, we estimated 45.3 PgC on agricultural land globally, with trees contributing >75%. Between 2000 and 2010 tree cover increased by 3.7%, resulting in an increase of >2 PgC (or 4.6%) of biomass carbon. On average, globally, biomass carbon increased from 20.4 to 21.4 tC ha−1. Regional and country-level variation in stocks and trends were mapped and tabulated globally, and for all countries. Brazil, Indonesia, China and India had the largest increases in biomass carbon stored on agricultural land, while Argentina, Myanmar, and Sierra Leone had the largest decreases.
Agroforestry has attracted considerable attention in recent years because of its potential to reduce poverty, improve food security, reduce land degradation and mitigate climate change. However, progress in promoting agroforestry is held back because decision-makers lack reliable tools to accurately predict yields from tree-crop mixtures. Amongst the key challenges faced in developing such tools are the complexity of agroforestry, including interactions between various system components, and the large spatial domains and timescales over which trees and crops interact. A model that is flexible enough to simulate any agroforestry system globally should be able to address competition and complementarity above and below ground between trees and crops for light, water and nutrients. Most agroforestry practices produce multiple products including food, fiber and fuel, as well as income, shade and other ecosystem services, all of which need to be simulated for a comprehensive understanding of the overall system to emerge.
In order to quantify the effects of forests to oil palm conversion occurring in the tropics on land–atmosphere carbon, water and energy fluxes, we develop a new perennial crop sub-model CLM-Palm for simulating a palm plant functional type (PFT) within the framework of the Community Land Model (CLM4.5). CLM-Palm is tested here on oil palm only but is meant of generic interest for other palm crops (e.g., coconut). The oil palm has monopodial morphology and sequential phenology of around 40 stacked phytomers, each carrying a large leaf and a fruit bunch, forming a multilayer canopy. A sub-canopy phenological and physiological parameterization is thus introduced so that each phytomer has its own prognostic leaf growth and fruit yield capacity but with shared stem and root components. Phenology and carbon and nitrogen allocation operate on the different phytomers in parallel but at unsynchronized steps, separated by a thermal period. An important phenological phase is identified for the oil palm – the storage growth period of bud and "spear" leaves which are photosynthetically inactive before expansion. Agricultural practices such as transplanting, fertilization and leaf pruning are represented. Parameters introduced for the oil palm were calibrated and validated with field measurements of leaf area index (LAI), yield and net primary production (NPP) from Sumatra, Indonesia. In calibration with a mature oil palm plantation, the cumulative yields from 2005 to 2014 matched notably well between simulation and observation (mean percentage error = 3 %). Simulated inter-annual dynamics of PFT-level and phytomer-level LAI were both within the range of field measurements. Validation from eight independent oil palm sites shows the ability of the model to adequately predict the average leaf growth and fruit yield across sites and sufficiently represent the significant nitrogen- and age-related site-to-site variability in NPP and yield. Results also indicate that seasonal dynamics of yield and remaining small-scale site-to-site variability of NPP are driven by processes not yet implemented in the model or reflected in the input data. The new sub-canopy structure and phenology and allocation functions in CLM-Palm allow exploring the effects of tropical land-use change, from natural ecosystems to oil palm plantations, on carbon, water and energy cycles and regional climate.
Trees inside and outside forests contribute to food security in Africa in the face of climate variability and change. They also provide environmental and social benefits as part of farming livelihoods. Varied ecological and socio-economic conditions have given rise to specific forms of agroforestry in different parts of Africa. Policies that institutionally segregate forest from agriculture miss opportunities for synergy at landscape scale. More explicit inclusion of agroforestry and the integration of agriculture and forestry agendas in global initiatives on climate change adaptation and mitigation can increase their effectiveness. We identify research gaps and overarching research questions for the contributions in this special issue that may help shape current opinion in environmental sustainability.
Planetary stability must be integrated with United Nations targets to
fight poverty and secure human well-being, argue David Griggs and
colleagues.
Spatial relationships between root length density of Coffea arabica (coffee RLD) and soil nutrient- related factors at plot scale in a coffee- Erythrina poeppigiana system was studied by geostatistics. In a 24 × 29 m area, (organic and conventional management), coffee and Erythrina fine roots and soil chemical properties were sampled on an irregular grid in the topsoil. A factor analysis explained 83 % of the total variation of the soil attributes. Soil factors were identified: Chemical fertility (CF), Micronutrients, Organic matter, and Acidity (Ac). Based on the spherical model, all the attributes presented a strong spatial structure. The scale of spatial correlation for CF was lesser than for Ac, but similar to coffee RLD. Erythrina RLD had a short-range variation. Patchy areas of high spots of coffee RLD were greater in organic plot. Cross-semivariogram analysis estimated a correlation between soil factors and coffee RLD over a scale of 5.50 m; but 4.23 m with Erythrina RLD. Nutrients linked to P, Zn, exchangeable bases and acidity soil affected the scale of spatial aggregation pattern of coffee RLD. The spatial response of coffee RLD suggests a differential nutrient uptake strategy for acquiring soil nutrients induced by the quality of organic and inorganic fertilizer inputs. The fact that coffee RLD had higher scale of spatial variation than Erythrina RLD and a negative spatial correlation indicate that pruned Erythrina trees are not so competitive for acquiring shared nutrients in an agroforestry system.
Process-based models (PBMs) of vegetation function can be used to
interpret and integrate experimental results. Water limitation to plant
carbon uptake is a highly uncertain process in the context of
environmental change, and many experiments have been carried out that
study drought limitations to vegetation function at spatial scales from
seedlings to entire canopies. What is lacking in the synthesis of these
experiments is a quantitative tool incorporating a detailed mechanistic
representation of the water balance that can be used to integrate and
analyse experimental results at scales of both the whole-plant and the
forest canopy. To fill this gap, we developed an individual tree-based
model (MAESPA), largely based on combining the well-known MAESTRA and
SPA ecosystem models. The model includes a hydraulically-based model of
stomatal conductance, root water uptake routines, drainage,
infiltration, runoff and canopy interception, as well as detailed
radiation interception and leaf physiology routines from the MAESTRA
model. The model can be applied both to single plants of arbitrary size
and shape, as well as stands of trees. The utility of this model is
demonstrated by studying the interaction between elevated
[CO2] (eCa) and drought. Based on theory, this
interaction is generally expected to be positive, so that plants growing
in eCa should be less susceptible to drought. Experimental
results, however, are varied. We apply the model to a previously
published experiment on droughted cherry, and show that changes in plant
parameters due to long-term growth at eCa (acclimation) may
strongly affect the outcome of Ca × drought
experiments. We discuss potential applications of MAESPA and some of the
key uncertainties in process representation.
Aims
Dehesas are agroforestry systems characterized by scattered trees among pastures, crops and/or fallows. A study at a Spanish dehesa has been carried out to estimate the spatial distribution of the soil organic carbon stock and to assess the influence of the tree cover.
Methods
The soil organic carbon stock was estimated from the five uppermost cm of the mineral soil with high spatial resolution at two plots with different grazing intensities. The Universal Kriging technique was used to assess the spatial distribution of the soil organic carbon stocks, using tree coverage within a buffering area as an auxiliary variable.
Results
A significant positive correlation between tree presence and soil organic carbon stocks up to distances of around 8 m from the trees was found. The tree crown cover within a buffer up to a distance similar to the crown radius around the point absorbed 30 % of the variance in the model for both grazing intensities, but residual variance showed stronger spatial autocorrelation under regular grazing conditions.
Conclusions
Tree cover increases soil organic carbon stocks, and can be satisfactorily estimated by means of crown parameters. However, other factors are involved in the spatial pattern of the soil organic carbon distribution. Livestock plays an interactive role together with tree presence in soil organic carbon distribution.
1.Faidherbia (Acacia) albida is a multipurpose tree widely distributed in semiarid Africa, notably in agroforestry parklands. It is in leaf during the dry season and defoliated during the rainy season, displaying therefore a peculiar reverse phenology. The related water-use strategy, including leaf water potential, sapflow, hydraulic conductance and depth of uptake, were monitored on adult trees in a Sudanese west-African parkland.
2. Despite a severe drought in the superficial soil layers, the predawn leaf water potential of F. albida dropped only to ca.– 0·5 MPa during the end of the dry season, indicating only a moderate water stress.
3. Radial trunk growth ceased before the end of the dry season and could have been affected by the moderate drought stress. However, leafiness remained constant during the dry season. Leaf shedding occurred after the first rains and was probably independent of drought.
4.Faidherbia albida displayed large transpiration rates under favourable conditions but the ratio of sapflow to Penman evapotranspiration and the soil-to-leaf specific hydraulic conductance decreased severely towards the end of the dry season.
5. Roots of F. albida were distributed through the weathered rock, down to a depth of 7 m, and vanished in the vicinity of a permanent water-table. The isotopic composition of oxygen in the xylem sap (δ18O) remained very close to the values recorded in the water-table during the course of the year. Phreatophytism thus explained the maintenance of growth and transpiration during the dry season. Nevertheless, during early rains, δ18O of sap switched towards the composition of the superficial soil layers, indicating facultative phreatophytism.
6. Reverse phenology, low density and depth of water uptake of F. albida indicated a low competition with annual crops for water; the fraction of annual rainfall used by the trees was estimated to remain below 5%.
The DynACof model was designed to model coffee agroforestry systems and study the trade-offs to e.g. optimize the system facing climate changes. The model simulates net primary productivity (NPP), growth, yield, mortality, energy and water balance of coffee agroforestry systems according to shade tree species and management. Several plot-scale ecosystem services are simulated by the model, such as production, canopy cooling effect, or potential C sequestration. DynACof uses metamodels derived from a detailed 3D process-based model (MAESPA) to account for complex spatial effects, while running fast. It also includes a coffee flower bud and fruit cohort module to better distribute fruit carbon demand over the year, a key feature to obtain a realistic competition between sinks.
The model was parameterized and evaluated using a highly comprehensive database on a coffee agroforestry experimental site in Costa Rica. The fluxes simulated by the model were close to the measurements over a 5-year period (nRMSE = 26.27 for gross primary productivity; 28.22 for actual evapo-transpiration, 53.91 for sensible heat flux and 15.26 for net radiation), and DynACof satisfactorily simulated the yield, NPP, mortality and carbon stock for each coffee organ type over a 35-year rotation.
Pearl millet is an important cereal for food security in the arid regions of Africa and India. The recently published genome of this tough cereal crop has shed new light on its history and adaptation to dry, hot climates and paves the way for much-needed genomic-based breeding efforts.
Faidherbia (Faidherbia albida) is being promoted widely in interventions for combating desertification, regreening of the Sahel and agroforestry projects in dry lands. Some scientists have questioned the wisdom of its wider promotion without clear evidence of its impacts. This review provides novel analyses of the magnitude and spatial extent of its influence on soil properties and primary productivity. A meta-analysis provided evidence for significant increases in soil organic carbon (SOC) (by 46%), total nitrogen (50%), phosphorus (21%), potassium (32%), and yields of maize (150%) and sorghum (73%) under the tree canopy compared to the open area. However, larger increases in SOC and nutrients occurred on inherently nutrient-poor sites than on nutrient-rich sites. Similarly, large increases in crop yields occurred in suboptimal conditions for crop productivity than in optimal conditions. The tree created predictable patterns in soil nutrients and crop yields consistent with distance–decay models of spatial interaction. Its growth and canopy development appear to explain the size dependence of the spatial extent of its influence, with a marked influence observed under large trees than small trees. The review also identified enormous variability in study design and statistical rigor, which appear to mask the expected patterns. Study designs and inferential statistics in current use neither address the intrinsic causality of patterns nor do they offer a mechanistic insight into the observed patterns. The major concerns and their implications are discussed and improvements for future research on single-tree influences on ecosystem properties in dry lands are suggested.
For the semiarid Sahelian region, climate variability is one of the most important risks of food insecurity. Field experimentations as well as crop modeling are helpful tools for the monitoring and the understanding of yields at local scale. However, extrapolation of these methods at a regional scale remains a demanding task. Remote sensing observations appear as a good alternative or addition to existing crop monitoring systems. In this study, a new approach based on the combination of vegetation and thermal indices for rainfed cereal yield assessment in the Sahelian region was investigated. Empirical statistical models were developed between MODIS NDVI and LST variables and the crop model SARRA-H simulated aboveground biomass and harvest index in order to assess each component of the yield equation. The resulting model was successfully applied at the Niamey Square Degree (NSD) site scale with yield estimations close to the official agricultural statistics of Niger for a period of 11 years (2000–2011) ( [Formula: see text] ). The combined NDVI and LST indices-based model was found to clearly outperform the model based on NDVI alone ( [Formula: see text] ). In areas where access to ground measurements is difficult, a simple, robust, and timely satellite-based model combining vegetation and thermal indices from MODIS and calibrated using crop model outputs can be pertinent. In particular, such a model can provide an assessment of the year-to-year yield variability shortly after harvest for regions with agronomic and climate characteristics close to those of the NSD study area.
Terrestrial ecosystems significantly contribute to the global carbon cycle. The chapter discusses that stocks and fluxes are increasingly altered by human activities, through changes in land use, in atmospheric composition, and in climate. This carbon sink results from an increase in global terrestrial net primary productivity (NPP). Thus, it is essential to know what the present value of global NPP is, and whether it will continue to increase to sustain an important terrestrial carbon sink. This chapter uses recent information provided in each of the biome-updated estimates of global values for NPP and phytomass. Finally, the current and expected changes in global NPP and net ecosystem productivity are discussed. The main source of uncertainty on global NPP lies in the biome area, and especially in forest area. Significant progress on biome maps is expected in the near future from the use of satellite data, once the classification done using such data can be given biome names that can receive a large acceptance. There is also some uncertainty on the current and future rates of change in NPP. Research on this aspect, at all scales, is developing fast. The chapter emphasizes on predicting more precisely the impact of human activities on NPP and on the carbon cycle in general. This information is crucial to design and implement sustainable development of human societies.
The increase in atmospheric carbon dioxide (CO2) concentrations due to emissions from fossil fuel combustion is contributing to recent climate change which is among the major challenges facing the world. Agroforestry systems can contribute to slowing down those increases and, thus, contribute to climate change mitigation. Agroforestry refers to the production of crop, livestock, and tree biomass on the same area of land. The soil organic carbon (SOC) pool, in particular, is the only terrestrial pool storing some carbon (C) for millennia which can be deliberately enhanced by agroforestry practices. Up to 2.2 Pg C (1 Pg = 10(15) g) may be sequestered above- and belowground over 50 years in agroforestry systems, but estimations on global land area occupied by agroforestry systems are particularly uncertain. Global areas under tree intercropping, multistrata systems, protective systems, silvopasture, and tree woodlots are estimated at 700, 100, 300, 450, and 50 Mha, respectively. The SOC storage in agroforestry systems is also uncertain and may amount up to 300 Mg C ha(-1) to 1 m depth. Here, we review and synthesize the current knowledge about SOC sequestration processes and their management in agroforestry systems. The main points are that (1) useful C sequestration in agroforestry systems for climate change mitigation must slow or even reverse the increase in atmospheric concentration of CO2 by storing some SOC for millennia, (2) soil disturbance must be minimized and tree species with a high root biomass-to-aboveground biomass ratio and/or nitrogen-fixing trees planted when SOC sequestration is among the objectives for establishing the agroforestry system, (3) sequestration rates and the processes contributing to the stabilization of SOC in agroforestry soils need additional data and research, (4) retrospective studies are often missing for rigorous determination of SOC and accurate evaluation of effects of different agroforestry practices on SOC sequestration in soil profiles, and (5) the long-term SOC storage is finite as it depends on the availability of binding sites, i.e., the soil's mineral composition and depth. Based on this improved knowledge, site-specific SOC sequestering agroforestry practices can then be developed.
Recent investigations have demonstrated that inter-year NOAAAVHRR NDVI variations in the middle of the rainy season can provide information on final crop yield in Sahelian countries. The present work continues this line of research by the use of 10-day Global Area Coverage (GAC) NDVI Maximum Value Composites, which are widely available and cost-effective in Africa. This use actually posed some problems which were mitigated by a multistep methodology aimed at forecasting millet and sorghum yield in Niger. The soil effect was first minimized in the NDVI images, and a geographical standardization was applied to the sub-district mean NDVI values and to the relevant ground yield estimates in order to remove most of the noninteresting information related to variations in land resources. A correlation analysis on the data obtained showed that the best period for yield forecasting was from the end of August to the middle of September. A further improvement in the forecasting capability of the procedure was then achieved by an image-based statistical identification of the most intensively cultivated areas. The final result of the complete methodology was the forecast of crop yield within the middle of September with an acceptable level of accuracy (mean error of 72 kg ha).
It is possible to assess crop yields at the end of the growing season in a semi-arid environment using data from meteorological satellites. This is the result of a work carried out in northern Burkina Faso. The technique used is based on linear correlation between millet yield and the time integral of the Normalized Difference Vegetation Index (iNDVI) derived from NOAA AVHRR data. In contrast to earlier related studies, the correlation has been established using satellite data extracted exclusively within the agricultural domain. The integration period for the iNDVI correponds to the reproductive phase only of the growing period of millet. Furthermore, iNDVI can also be used to estimate the acreage or the agricultural domain, by the application of a suitable threshold to classify areas into agricultural and non-agricultural domains.It is therefore possible to assess the yield and the acreage of the agricultural domain and to derive an estimate of the millet production of the area by the end of the season, on the basis of NOAA AVHRR data alone.
When the sensible heat flux is uniform and directed upwards across a field then the air near the ground is warmer in the quiet zone and cooler in the wake zone when compared with conditions in the open. Other scalars show similar behaviour. In conditions of dry air advection, when evaporation in the open exceeds the equilibrium rate, evaporations is reduced in the quiet zone and is expected to be enhanced in the wake zone.Recent work on the aerodynamics of shelter has shown that there exists a quiet zone of reduced turbulence and smaller eddy size immediately behind windbreaks of all porosites. Beyond that, further downwind, lies an extended wake region of increased turbulence with eddy sizes returning to upwind scale. There is evidence to show that turbulent transport of heat, vapour and carbon dioxide is reduced in the quiet zone and enhanced in the wake.
Agroforestry represents an opportunity to reduce CO2 concentrations in the atmosphere by increasing carbon (C) stocks in agricultural lands. Agroforestry practices may also promote mineral N fertilization and the use of N-2-fixing legumes that favor the emission of non-CO2 greenhouse gases (GHG) (N2O and CH4). The present study evaluates the net GHG balance in two adjacent coffee plantations, both highly fertilized (250 kg N ha(-1) year(-1)): a monoculture (CM) and a culture shaded by the N-2-fixing legume tree species Inga densiflora (CIn). C stocks, soil N2O emissions and CH4 uptakes were measured during the first cycle of both plantations. During a 3-year period (6-9 years after the establishment of the systems), soil C in the upper 10 cm remained constant in the CIn plantation (+0.09 +/- 0.58 Mg C ha(-1) year(-1)) and decreased slightly but not significantly in the CM plantation (-0.43 +/- 0.53 Mg C ha(-1) year(-1)). Above-ground carbon stocks in the coffee monoculture and the agroforestry system amounted to 9.8 +/- 0.4 and 25.2 +/- 0.6 Mg C ha(-1), respectively, at 7 years after establishment. C storage rate in the phytomass was more than twice as large in the CIn compared to the CM system (4.6 +/- 0.1 and 2.0 +/- 0.1 Mg C ha(-1) year(-1), respectively). Annual soil N2O emissions were 1.3 times larger in the CIn than in the CM plantation (5.8 +/- 0.5 and 4.3 +/- 0.3 kg N-N2O ha(-1) year(-1), respectively). The net GHG balance at the soil scale calculated from the changes in soil C stocks and N2O emissions, expressed in CO2 equivalent, was negative in both coffee plantations indicating that the soil was a net source of GHG. Nevertheless this balance was in favor of the agroforestry system. The net GHG balance at the plantation scale, which includes additionally C storage in the phytomass, was positive and about 4 times larger in the CIn (14.59 +/- 2.20 Mg CO2 eq ha(-1) year(-1)) than in the CM plantation (3.83 +/- 1.98 Mg CO2 eq ha(-1) year(-1)). Thus converting the coffee monoculture to the coffee agroforestry plantation shaded by the N-2-fixing tree species I. densiflora would increase net atmospheric GHG removals by 10.76 +/- 2.96 Mg CO2 eq ha(-1) year(-1) during the first cycle of 8-9 years. (c) 2011 Elsevier B.V. All rights reserved.
The shade impact by Ingo densiflora on water use and drainage in a coffee agroforestry system (AFS) was compared to coffee monoculture (MC) in Costa Rica. Rainfall interception, transpiration, runoff and soil water content were monitored during 3 years. Runoff was lower in AFS than MC (5.4 and 8.4% of total rainfall, respectively) and a higher water infiltration was observed under AFS. Still, the higher combined rainfall interception + transpiration of coffee and shade trees in AFS resulted in a lower drainage than in MC. No coffee water stress was recorded either in AFS or MC as relative extractable soil water remained above 20% during the dry seasons. Time course of soil water content showed enhanced access to soil water between 100 and 200 cm depth in AFS. This suggests complementarity for soil water between coffee and shade trees. The model HYDRUS 1D predicted that drainage at 200 cm depth accounted for a large fraction of annual rainfall (68% for MC and 62% for AFS). Climatic scenario simulations showed (1) a potential competition for water between coffee and shade trees when the dry season was extended by 4-6 weeks compared to actual, and (2) a severe reduction in annual drainage, but without competition for water when rainfall was reduced down to 40% of the actual. (C) 2010 Elsevier B.V. All rights reserved.
Criteria for evaluating different intercropping situations are suggested, and the Land Equivalent Ratio (LER) concept is considered for situations where intercropping must be compared with growing each crop sole. The need to use different standardizing sole crop yields in forming LERs is discussed, and a method of calculating an ‘effective LER’ is proposed to evaluate situations where the yield proportions achieved in intercropping are different from those that might be required by a farmer. The possible importance of effective LERs in indicating the proportions of crops likely to give biggest yield advantages is discussed.
In this unpublished memoir, written in the early 1990s, the late Dana Meadows reflects on the history of The Limits to Growth, including its origins, conclusions, and the reactions it generated. This memoir had been condensed and edited by Dennis Meadows. Copyright © 2007 John Wiley & Sons, Ltd.