How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland

Roupsard, Olivier; Audebert, Alain; Ndour, Adama; Clermont-Dauphin, Cathy; Agbohessou, Yélognissè; Sanou, Josias; Koala, Jonas; Faye, Émile; Sambakhe, Diaretou; Jourdan, Christophe; Maire, Guerric Le; Tall, Laure; Sanogo, Diaminatou; Seghieri, Josiane; Cournac, Laurent; Leroux, Louise

doi:10.1016/j.agee.2020.106928

Cited by 42 publications

(33 citation statements)

References 47 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

et al. 2021

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“…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.…”

Section: Current State Of the Artmentioning

confidence: 99%

Climate change adaptation in and through agroforestry: four decades of research initiated by Peter Huxley

Noordwijk

Coe

Sinclair

et al. 2021

Mitig Adapt Strateg Glob Change

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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.

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“…Note that in (2), unwrapping is done after topographic phase (φ 0 ) removal in (1). It is also possible to estimate h pha by first creating a DEM and then subtracting a DTM.…”

Section: Phase Heightmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Mapping Tree Height in Burkina Faso Parklands with TanDEM-X

Soja

Karlson

Bayala³

et al. 2021

Remote Sensing

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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.

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How far does the tree affect the crop in agroforestry? New spatial analysis methods in a Faidherbia parkland

Cited by 42 publications

References 47 publications

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Climate change adaptation in and through agroforestry: four decades of research initiated by Peter Huxley

Mapping Tree Height in Burkina Faso Parklands with TanDEM-X

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