Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry. (July 2017)

• Record Type: Journal Article
• Authors: Smethurst, Philip J.; Huth, Neil I.; Masikati, Patricia; Sileshi, Gudeta W.; Akinnifesi, Festus K.; Wilson, Julia; Sinclair, Fergus
• Is Part Of: Agricultural systems. Volume 155(2017)
• Page Start: 70
• Page End: 77
• Publication Date: 2017-07
• Subjects: Hedgerow -- Alley cropping -- Intercropping -- Competition -- Water -- Nitrogen; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2017.04.008
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: Agroforestry systems, containing mixtures of trees and crops, are often promoted because the net effect of interactions between woody and herbaceous components is thought to be positive if evaluated over the long term. From a modelling perspective, agroforestry has received much less attention than monocultures. However, for the potential of agroforestry to impact food security in Africa to be fully evaluated, models are required that accurately predict crop yields in the presence of trees. The positive effects of the fertiliser tree gliricidia ( Gliricidia sepium ) on maize ( Zea mays ) are well documented and use of this tree-crop combination to increase crop production is expanding in several African countries. Simulation of gliricidia-maize interactions can complement field trials by predicting crop response across a broader range of contexts than can be achieved by experimentation alone. We tested a model developed within the APSIM framework. APSIM models are widely used for one dimensional (1D), process-based simulation of crops such as maize and wheat in monoculture. The Next Generation version of APSIM was used here to test a 2D agroforestry model where maize growth and yield varied spatially in response to interactions with gliricidia. The simulations were done using data for gliricidia-maize interactions over two years (short-term) in Kenya and 11 years (long-term) in Malawi, with differing proportions of trees and crops and contrasting management. Predictions were compared with observations for maize grain yield, and soil water content. Simulations in Kenya were in agreement with observed yields reflecting lower observed maize germination in rows close to gliricidia. Soil water content was also adequately simulated, except for a tendency for slower simulated drying of the soil profile each season. Simulated maize yields in Malawi were also in agreement with observations. Trends in soil carbon over a decade were similar to those measured, but could not be statistically evaluated. These results show that the agroforestry model in APSIM Next Generation adequately represented tree-crop interactions in these two contrasting agro-ecological conditions and agroforestry practices. Further testing of the model is warranted to explore tree-crop interactions under a wider range of environmental conditions. Highlights: A tree-crop interaction model was evaluated for gliricidia-maize agroforestry in Africa. Predicted grain yield and soil water and carbon dynamics agreed with observed data. The model simulated crop yield accurately in both water- and N-limited situations. Model utility could be further improved through evaluation over a broader range of conditions.