Agroecological Crop Protection

• Agroecological crop protection is defined as integrating ecological principles with quantitative methods to manage pests and diseases through diversified spatial and temporal strategies.
• Quantitative models, such as SIR-type frameworks and optimization of trap crop allocation, demonstrate significant reductions in virus prevalence and yield loss in intercropped systems.
• Advanced techniques including precise pest monitoring, natural enemy enhancement, and molecular volatile deployment collectively improve crop resilience and economic outcomes.

Agroecological crop protection encompasses the integration of ecological principles and quantitative methods for managing pests, diseases, and weeds in agricultural systems. It seeks to deliver effective crop protection while reducing reliance on synthetic pesticides and maximizing ecosystem services through spatial, temporal, and biotic diversification. Central to agroecological crop protection are strategies such as optimized intercropping, trap cropping, natural enemy enhancement, residue microbiome management, precise monitoring networks, and context-sensitive input allocation, all rigorously tested and parameterized in model-guided and empirical frameworks.

1. Epidemiological and Spatial Models of Diversified Crop Arrangements

The spatial configuration of crops and integration of trap crops are critical levers for suppressing pest epidemics in agroecological systems. A landmark SIR-type model for vector-borne virus spread in intercropped fields quantifies the dynamics of susceptible ($S_i$), infected ($I_i$), and removed ($R_i$) plant states:

$$\begin{align*} \dot S_i(t) &= -\beta S_i(t) \sum_{j=1}^N \widetilde{A}_{ij} I_j(t-\tau) \ \dot I_i(t) &= \beta S_i(t) \sum_{j=1}^N \widetilde{A}_{ij} I_j(t-\tau) - \mu I_i(t) \ \dot R_i(t) &= \mu I_i(t) \end{align*}$$

where $\widetilde{A}_{ij}$ encodes vector (e.g., aphid) mobility and trap crop effect through a topological decay kernel modulated by interplant distance and trap strength parameter $\gamma$ (Allen-Perkins et al., 2019).

Chessboard-pattern intercropping with a 1:1 main-to-trap crop ratio drastically elevates the epidemic threshold $1/\lambda_{\max}$ (up to 32-fold increase) and yields up to 95% reduction in virus prevalence under moderate to high vector mobility, compared to monocrop or strip/patch layouts. The epidemic threshold is analytically defined as $R_0 = (\beta/\mu)\lambda_{\max}(\widetilde{A})$, guiding field-level parameterization.

For practical implementation, empirically informed estimations of vector dispersal ($s$), infection rates ($\beta$), removal rates ($\mu$), and trap effectiveness ($\gamma$) are essential. Deviation from full chessboard layouts can be replaced with random 50:50 mixtures with only marginal loss of efficacy, provided plant–plant spacing falls within typical vector “hop radii” ($20$–$50$ cm).

2. Spatiotemporal Intercropping Configurations and Multi-Trophic Interactions

Meta-analysis of 7,584 global field experiments identifies relay intercropping (sequential, temporally staggered sowing of component crops) as the configuration most effective at boosting beneficial insect populations and suppressing pests (Datta et al., 19 Sep 2025). The Management Efficiency Ratio (MER), defined as $$\ln(A_{\mathrm{intercrop}}/A_{\mathrm{monoculture}})$$, consolidates effects across natural enemy and pest functional groups.

Key findings include:

• Relay intercropping: predators $MER = 0.473$, parasitoids $MER = 0.512$, pest suppression $MER = -0.611$.
• Best spatial templates: row- or strip-relay with cereal–legume pairs, universally optimal in 57% of climates.
• In regions with functional-group mismatches, mixed or replacement patterns and tailored crop species must be deployed.
• Implementation requires: staggered sowing (4–6-week overlap), spatial arrangements (e.g., alternation of 2 m maize and 1 m bean rows), and monitoring to adjust sowing interval or density to reach predator $MER > 0.3$ and pest suppression $MER < -0.4$.

These approaches permit formal linking of intercropping design, ecosystem service benchmarks, and policy/extension incentive schemes.

3. Quantitative Optimization of Trap Cropping and Area Allocation

Deciding the optimal proportion of land for trap cropping is formulated as a maximization problem for net yield:

$Y(p) = (1-p)\left[Y_{\max} - L(D(p;a))\right]$

where $p$ is the trap crop area fraction, $a$ the relative attractiveness, $L(D)$ the yield-loss function, and $D(p;a)$ the pest density per cash plant (Holden, 7 Aug 2025).

Closed-form solutions for $p^*(a)$ (linear loss) yield an optimal trap crop allocation typically in the 5–20% range for realistic values of $a$ (5–50) and pest risk ($\beta$). The optimum increases under high pest pressure or suboptimal attractiveness, but declines toward zero for traps indistinguishable from the main crop. Spatial deployment (rows vs. borders) can be embedded in the framework via settlement probabilities.

Implementation protocol: estimate $a$ from choice tests, $β$ from unsprayed yield losses, and realize $p^*(a,β)$ by buffer or border planting, with ongoing outcome monitoring. The result is a quantitative decision-answerable prescription for maximizing yield under ecological pest management.

4. Landscape Complexity, Natural Enemies, and Economic Trade-offs

The integration of pest biocontrol by natural enemies depends not only on within-field measures but also on landscape mosaics and farm structure. A coupled ecological–economic spatial model reveals that biocontrol efficacy—and its economic value—is modulated by farm size, semi-natural habitat (SNH) configuration (hedgerows vs. grasslands), pesticide regime, and natural enemy dispersal (Moretti et al., 23 May 2025).

Small-to-medium farms (5–50 ha) profit most (+5–15% income) by reducing or eliminating pesticides and restoring hedgerows (≥1 m/100 m² cropland), while maintaining or increasing natural enemy populations by >100%. For large farms (>500 ha), these strategies have diminished net gain, and success may require coordinated inter-farm SNH corridors or larger-scale grassland additions. Sensitivity analyses confirm the nonlinearity of input–output responses: partial pesticide reduction (–50%) plus targeted SNH yields the most robust win–win outcome for biodiversity and profit.

5. Biological and Microbial Control: From Natural Enemies to Microbiomes

Biological control strategies, encompassing release or conservation of predators, parasitoids, and antagonists, are quantitatively catalogued by efficacy estimates derived from broad literature bases. For example, field predation or parasitism can routinely exceed 67–90% for major pests with appropriate choice and deployment of agents (e.g., Encarsia formosa for Bemisia tabaci; Trichogramma spp. for Helicoverpa armigera) (Wyckhuys, 12 Dec 2025).

Residue microbiome management leverages competitive, antagonistic, and mutualistic interactions within decomposing crop residues to curtail fungal pathogen survival. Empirical relationships are expressed in terms of exponential inoculum decay $S(f_b) = S_0 e^{-\kappa f_b}$, where $f_b$ is the fraction of residue buried and $\kappa$ is taxon-specific (Kerdraon et al., 2019). A 30–50% surface residue retention with partial burial can reduce Z. tritici inocula by ≥60% while preserving beneficial keystone taxa (e.g., Pseudomonas fluorescens, Clonostachys rosea). Synthetic consortia (SynComs) combining complementary taxa exceed single isolates in accelerating pathogen decline (e.g., $k_{syn} = 0.05\,d^{-1}$ vs. $k_{ctrl} = 0.01\,d^{-1}$).

Recommended management integrates no-till or reduced-till, green manure boosting, and empirically designed stable SynComs (Shannon diversity $H' \geq 1.5$), resulting in additive suppression of residue-borne diseases.

6. Pest Monitoring, Decision Thresholds, and Timeliness

Advanced Pest Monitoring Networks (PMNs), incorporating dense spatial sampling and Bayesian updating, can drive significant pesticide reductions (up to 67%) for soil-borne pathogens and weeds by application of a formally derived risk threshold $p_i^t > p_0$ (Cros et al., 2020). For pests with high mobility, notably insects, local PMNs are less effective at decision differentiation and necessitate larger, landscape-scale or edge-based monitoring networks.

For biopesticide-based interventions regulated by farmer awareness, mathematical models specify that the basic reproduction number $R_0$—and system stability—are governed by the synergy of awareness, biopesticide application, and rapid detection-response loops. To avoid destabilizing pest outbreaks (Hopf bifurcation), the sum of sensing and response delays ($\tau_1 + \tau_2$) must be kept below critical values (e.g., $\approx16$ d at standard parameters) (Abraha et al., 2021).

Decision rules follow a bang-bang control structure: initiate full field application upon pest density threshold exceedance, then withdraw as crop returns to equilibrium, optimized using Pontryagin’s Maximum Principle with delay-constrained feedback.

7. Molecular and Volatilome-Based Innovations

Molecular communication (MC) paradigms employing herbivore-induced plant volatiles (HIPVs), such as methyl salicylate (MeSA), provide novel routes for spatially controllable pest suppression and natural enemy recruitment (Vakilipoor et al., 25 Jun 2025). Controlled-release formulations are optimized for physicochemical release profiles (Korsmeyer–Peppas model: $M(t)/M_\infty = k t^n$) and are modeled in a 3D advection–diffusion framework under variable wind fields.

Spatial arrangements of volatile emitters (microsphere TXs) substantially alter the Coverage Effectiveness Index (CEI), quantifying the proportion of field volume above functional olfactory thresholds. Maximizing CEI at behaviorally relevant thresholds (e.g., $C_{th}=10^{12}$–$10^{14}$ molecules/m³) is achieved via decentralized four-corner emitter layouts, enabling enhanced antagonist attraction with minimal synthetic input.

• SIR spatial models for trap cropping: (Allen-Perkins et al., 2019)
• Global meta-analysis of relay intercropping efficiency: (Datta et al., 19 Sep 2025)
• Quantitative optimization of trap area and allocation: (Holden, 7 Aug 2025)
• Landscape-scale modeling of farm structure, biocontrol, and profitability: (Moretti et al., 23 May 2025)
• Systematic review of biological agents and control tactics: (Wyckhuys, 12 Dec 2025)
• Microbiome-based residue management and SynCom assembly: (Kerdraon et al., 2019)
• Design of densified pest monitoring for rational pesticide reduction: (Cros et al., 2020)
• Biopesticide timing, awareness, and delay-robust control strategies: (Abraha et al., 2021)
• MC-based HIPV deployment for volatile-mediated crop protection: (Vakilipoor et al., 25 Jun 2025)