Toward systems agroecology: Design and control of intercropping | Vien Khoa Hoc Ky Thuat Nong Nghiep Mien Nam

Toward systems agroecology: Design and control of intercropping

Tuesday, 2024/12/31 | 08:17:53

Sirio Belga Fedeli and Stanislas Leibler

PNAS; December 16 2024; 121 (52) e2415315121; https://doi.org/10.1073/pnas.2415315121

Significance

Understanding how one can mitigate water scarcity, soil infertility, and the impact of changing weather conditions is essential to guaranteeing future food security. Here, we examine intercropping, a practice considered to be a promising alternative to monocrop agriculture, as it requires limited inputs and often provides comparable yields. In order for it to be largely adopted, it is imperative to assess its outcomes, robustness, and controllability. Based on a dataset regrouping the results of thousands of experiments, we establish the foundations of intercropping design and control. More experiments are needed before computational methods can guide the practice of intercropping with high confidence. Our results represent a prefatory step on the way to more robust and sustainable multiplant farming.

Abstract

In view of changing climatic conditions and disappearing natural resources such as fertile soil and water, exploring alternatives to today’s industrial monocrop farming becomes essential. One promising farming practice is intercropping (IC), in which two or more crop species are grown together. Many experiments have shown that, under certain circumstances, IC can decrease soil erosion and fertilizer use, improve soil health and land management, while preserving crop production levels. However, there have been no quantitative approaches to predict, design, and control appropriate IC implementation for given particular environmental and farming conditions, and to assess its robustness. Here, we develop such an approach, based on methods and concepts developed in data science and systems biology. Our dataset groups the results of 2258 IC experiments, involving 274 pairs of 69 different plants. The data include 4 soil characteristics and 5 environmental and farming conditions, together with 8 traits for each of the two intercropped plants. We performed a dimensional reduction of the resulting 25-dimensional variable space and showed that, from a few quantities, one can predict IC yield relative to sole cultivation with good accuracy. For given environmental conditions, our computational approach can help to choose a companion plant and appropriate farming practices. It also indicates how to estimate the robustness of IC to external perturbations. This approach, together with its results, can be viewed as an initial step toward “systems agriculture,” which would ultimately develop systems of multiple plant grown together in appropriately designed and controlled settings.

See https://www.pnas.org/doi/10.1073/pnas.2415315121

Figure 1:

Importance of the T-EF variables for the experiments contained in the dataset. (A) The T-EF variables, xi, listed in Table 1, are ranked by their averaged (over the dataset) importance, |ϕi|¯. Error bars represent the SE of the mean. Variables above the dashed line are the 7 “major variables” considered for analyzing IC robustness and control through agroecological modifications. Blue and gray color bars correspond to T and EF variables, respectively. (B–G) The importance values, ϕi(xi), plotted as a function of the corresponding T-EF variable, xi. The dark line is the LOWESS nonparametric regression; CIs (in gray) were obtained by bootstrapping (Methods). Two horizontal black dotted lines represent the significant levels obtained by training RFR over the IC dataset with randomly shuffled outcomes pLER (SI Appendix, section 11). Numbers in white circles give the rank of the depicted T-EF variables, as exhibited in (A). The plot in (B) shows the importance of dM. The box-plot in (C) shows the distribution of dM for different crop families. Main-crop families presented here are Lin = Linaceae, Poa = Poaceae, Bra = Brassicaceae, Fab = Fabaceae, Ped = Pedaliaceae, Ast = Asteraceae, Mal = Malvaceae, and Eup = Euphorbiaceae. The plot in (D) shows the importance of dMIC, and the plot in (E) the importance values of dCIC. The pink, white, and blue regions in (B, D, and E) approximately indicate three regimes of densities discussed in the text. (F) The importance values of the pH variable. (G) The importance values of Rad variable. (H) Color bar for pLER values in (B, D, and E–G).

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