Growth of Bean Strip‐Intercropped with Maize: Evaluation of the CROPGRO Model

Abstract

Optimizing strip-intercropping systems requires a comprehensive modeling approach to study competition effects, especially for solar radiation, and to integrate numerous crops. Therefore, we studied the radiation availability and its effect on canopy and leaf level and finally on total dry matter and yield formation of bush bean (Phaseolus vulgaris L. var. nana) strip-intercropped with maize. Nine rows of bush bean were sown on two sowing dates between strips of six rows of maize (Zea mays L.). The CROPGRO model was tested with an hourly input of solar radiation for its ability to simulate observed growth and yield formation of the two most shaded bush bean rows next to maize. Experimental results showed that bush bean had a good potential for stripintercropping systems tolerating up to 30% shading due to an increased light interception by a larger canopy with a considerable increase in its leaf area. The CROPGRO model, calibrated on data of monocropped bush bean, captured well the effect of the strongly reduced radiation on total and pod dry matter in the most shaded bush bean row. This indicates the model's applicability on other intercropping systems exhibiting high levels of shading. Under a lower level of shading further from maize, cultivar parameters responsible for leaf area expansion and the maximum photosynthetic rate had to be increased to achieve a high accuracy of the simulations. Future studies should focus on measurements of photosynthesis, radiation distribution and absorption within the canopy, and leaf area expansion under shaded conditions to improve model simulations.

Current knowledge and future research opportunities for modeling annual crop mixtures. A review

“…We assert that models would be useful tools to understand and predict the functioning of annual crop mixtures and to help designing these agroecosystems, as a complement to field experiments, keeping in mind that experiments are crucial to improve and refine simulation models whereas models are useful to guide experiments and test all possible plant-environment combinations (Craufurd et al 2013;Reynolds et al 2018;Rötter et al 2018). However, modeling of annual crop mixtures is still in its infancy, and most modeling studies focus on model evaluation (Gou et al 2017b) or the evaluation of sole crop models extended to crop mixtures, in particular for bi-specific mixture of annual crops (Corre-Hellou et al 2009;Knörzer et al 2010;Munz et al 2014). Use of modeling remains limited in part because models are not yet completely operational, indicating the need to increase understanding of the ecological processes that influence crop mixture functioning before simulating them.…”

“…An initial strategy to simulate crop mixtures involved modifying how one crop senses its environment by altering environmental variables according to predictions of another sole crop model (i.e. successively running individual sole crop models, Monzon et al 2007) or external calculations (Knörzer et al 2010;Munz et al 2014). After calculating the impact of crop 1 on the environment, the modified environmental variables are used to simulate crop 2.…”

“…Modeling of annual crop mixtures is under development, and the few models that currently exist focus mainly on abiotic resource partitioning. Two main modeling approaches are currently used: i) processbased crop models, in which crop characteristics are represented at the field scale (Launay et al 2009;Knörzer et al 2011;Fayaud et al 2014;Munz et al 2014;Gou et al 2017b), and ii) individual-based models (IBMs), in which each plant is represented individually at various levels of architectural realism (Garcia-Barrios et al 2001;Potting et al 2005;Postma and Lynch 2012;Barillot et al 2014b;Colbach et al 2014b;Zhu et al 2015). These models have difficulty representing the specific characteristics and complexity of crop mixtures because they may not completely capture certain processes (complementarity, facilitation, resource partitioning, etc.)…”

Economics of strip cropping with autonomous machines

“…Agronomic research on strip cropping with varying height plants has demonstrated the edge effects that increase yields of the taller species and often lead to a yield penalty for shorter crop plants (Jurik & Van, 2004). These studies were conducted in large‐scale farming in the United States (Ward et al., 2016; West & Griffith, 1992) and Argentina (Bravo & Silenzi, 2002; Verdelli et al., 2012), medium‐scale farming in Germany (Munz, Claupein et al., 2014), and small‐scale farming in China (Du et al., 2018; Liu et al., 2022; Munz, Claupein et al., 2014), as well as in Africa (Kermah et al., 2017; Rahman et al., 2021). Research on corn ( Zea mays L.), grain sorghum [ Sorghum bicolor (L.) Moench], and soybean [ Glycine max (L.) Merr.]…”