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Soil organic carbon (SOC) sequestration was evaluated for several long-term rain-fed cropping systems for conservation agriculture (CA) and conservation tillage (CT) in Cambodia using the Environmental Policy Integrated Climate (EPIC) model. The mean crop yield, biomass and SOC stocks of four treatments and three replication in each primary cropping system (rice, soybean and cassava) were used for calibration and validation during the period 2009–2013. The CT and CA1 treatments were assigned to continuous cropping of primary crops. CA2 and CA3 treatments were assigned to rotated primary crops with maize. In all CA treatments, forage or legume cover crops were prior planted and intercropped with the primary crops to maintain full soil cover. The results show that EPIC successfully simulated crop yields, biomass, and SOC. However, the model tended to underestimate SOC in the CT treatments and overestimate SOC in the CA2 and CA3 treatments. Crop residue was found to highly influence SOC sequestration. Sediment loss in the CT treatments was found to be four times greater than CA treatments due to the CT tillage effects. The 20-year future simulations, using historical weather and automatically generated by EPIC, showed a decrease trend in SOC stocks in all CT treatments and an increase trend in most CA treatments, with the greatest increase for CA2 and CA3 treatments. Thus, the CA treatments in combination with the maize rotation were demonstrated to be more efficient to manage SOC sequestration over CA with one continuous primary crop.

Coffee is a key export commodity of East Africa, but average smallholders' yields are low. To guide sustainable yield improvements of smallholders' coffee systems, we investigated coffee yield components in three different types of coffee cropping systems along an altitude gradient (1100−2100 m.a.s.l.) during two production years (2015 and 2016). We selected 810 coffee trees distributed over 27 farms and monitored number of stems per tree, fruit load per branch, productive nodes per branch (on four branches of one stem per tree) and number of productive branches per stem (on one stem per selected tree) in both years. Additionally, we monitored productive stems per ha, coffee tree density and cherry weight in combination with pest and disease occurrence and management information from interviews. Coffee farms were classified as Coffee-Open (CO) (< 20 % shade cover), Coffee-Banana (CB) (coffee dominantly intercropped with bananas) or Coffee-shade Tree (CT) (coffee dominantly intercropped with shade trees). Coffee-Banana had larger yield per ha (green bean kg ha−1) (1086±736 kg ha−1) and yield per stem (green bean kg stem−1) (0.24±0.16 kg stem−1) than CO (670±457 kg ha−1 and 0.21±0.26 kg stem−1) and CT (428±259 kg ha-1 and 0.10±0.12 kg stem−1). Fruit loads, productive nodes, productive branches and cherry weight declined with shade cover, especially for shade cover>30 %. Additionally, the same yield components correlated negatively with number of stems per tree. Fertilizer and fungicide use were related to more productive branches and cherry weight respectively, and stem borer was identified as the most important pest in this area. Our results suggest that yield in the region could be increased, i) by maintaining shade at an intermediate level, particularly at low and mid altitude and by reducing the number of stems per coffee tree (< 4), and ii) by improving soil fertility and protection against pest and disease.

The cocoa tree (Theobroma cacao L.) is cultivated typically in agroforestry systems in close association with a rich list of tree species and other useful plants on the same plot. Cocoa based agroforestry systems are credited for stocking significant amounts of carbon and hence have the potential to mitigate climate change. Since cocoa yields decrease non-linearly with increasing shade, a need is to design optimal cocoa agroforestry systems with high yields and high carbon stocks. We estimated the carbon stocked in a network of 229 permanent sample plots in cacao-based agroforestry systems and natural forests in five Central American countries. Carbon stocks were fractioned by both system compartments (aboveground, roots, soil, litter, dead wood – fine and coarse, and total) and tree use/form (cocoa, timber, fruit, bananas, shade and ornamentals, and palms). Cocoa plantations were assigned to a five-class typology and tested for independence with growing region using contingency analysis. Most Central American cocoa plantations had mixed or productive shade canopies. Only 4% of cocoa plantations were full sun or rustic (cocoa under thinned natural forest). Cocoa tree density was low (548 ± 192 trees ha−1). Total carbon (soil + biomass + dead biomass) was 117 ± 47 Mg ha−1, with 51 Mg ha−1 in the soil and 49 Mg ha−1 (42% of total carbon) in aboveground biomass (cocoa and canopy trees). Cocoa trees accumulated 9 Mg C ha−1 (18% of carbon in aboveground biomass). Timber and fruit trees stored 65% of aboveground carbon. The annual rate of accumulation of carbon in aboveground biomass ranged between 1.3 and 2.6 Mg C ha−1 y−1. Trade-offs between carbon levels and yields were explored qualitatively using functional relationships documented in the scientific and technical literature, and expert knowledge. We argue that it is possible to design cocoa-based AFS with good yields (cocoa and shade canopy) and high carbon stock levels. The botanical composition of the shade canopy provides a large set of morphological and functional traits that can be used to optimize shade canopy design. Our results offer Central American cocoa producers a rigorous estimate of carbon stocks in their cocoa plantations. This knowledge may help them to certify and sell their cocoa, timber, fruits and other goods to niche markets with good prices. Our results will also assist governments and the private sector in (i) designing better legal, institutional and policy frameworks, local and national, promoting an agriculture with trees and (ii) contributing to the development of the national monitoring, reporting and verification systems required by the international community to access funding and payment for ecosystem services.

Acacia senegal is of paramount importance in sub Saharan Africa because it produces gum arabic. Inoculation with specific rhizobial strains improves the vigour of the trees but also positively enhances the gum arabic production of mature trees. A host-specific cocktail of Ensifer strains was used to inoculate mature A. senegal trees in Burkina Faso and Niger. Rhizosphere soils were sampled during dry and rainy seasons from 2006 to 2008. Total microbial biomass and soil inorganic nitrogen were assessed, and DNA extracted and used to analyse diversity of A. senegal rhizosphere bacterial communities on Denaturing Gradient Gel Electrophoresis after PCR amplification of the 16S rDNA. In Niger, results showed a significant increase of total bacterial diversity due to the season but no effect of the rhizobial inoculation was observed. However, a significantly higher microbial biomass was found in soil from uninoculated trees than from inoculated trees. In Burkina Faso, no effect of both season and rhizobial inoculation was observed on total bacterial diversity, but higher microbial biomass values were obtained compared to Niger, especially for 2006. However, there were no significant differences between inoculated and uninoculated trees. Rhizobial inoculation significantly enhanced gum arabic production in Niger but not in Burkina Faso.

International audience; Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches.