Army worm damage on corn

Not only crop growth and yield, crop models can be also used to estimate potential damages from pest/disease through a predefined set of damage pathways, or coupling points. This feature can be especially useful for designing integrated pest management practices by extension agents or researchers. This post describes the use of coupling points using a hypothetical scenario of wide-spreading acute disease epidemic on maize in Tanzania, and their spatially varying degree of yield impacts.

Modeling sick plants

As plant pathologists say, “Plants get sick too (Esnard, 2003)”. This is particularly so in highly-stressed smallholder farming systems in developing countries. As with humans, crops are susceptible to damage by many organisms, such as bacteria, viruses, and insects. Crops are particularly susceptible when they are under water and nutrient stress and thus not healthy enough to sustain immunity and recover their usual vigor. Such stress conditions are commonly found in subsistence-oriented, low-input cropping systems where farmers often have limited capacity to mitigate crop damage or even to prevent total crop loss. Some types of damage can be minimized by proper prevention or intervention measures (e.g., residue management, applying pesticides or planting resistant crop varieties), but such measures are either knowledge-intensive, costly, or both and are therefore often improperly utilized or underutilized by resource poor farmers. Providing assessments of the vulnerability of different cropping systems to pest and disease damage over space and time represents a potentially major contribution to the design, targeting and support of crop loss mitigation efforts by both the public and private sectors.

To assess the potential impacts of pests and diseases on crop production, some crop models include coupling points, special model variables whose changing values can be used to represent pest and disease damage to crop organs or growth processes. Examples of coupling point variables include leaf mass or area, stem mass, root mass, root length and seed mass or number, all of which might be negatively impacted by pests and diseases. By identifying specific damage pathways and rates of damage using these variables (e.g., an army-worm infestation might be defined as reducing leaf area by 10% by the 40th day after planting), growth models can quantify how crop development would be affected and, ultimately, what would be the impact on crop yield. The coupling point concept was first introduced in 1983 (Boote et al.), and later formally implemented in the DSSAT crop modeling platform (Hoogenboom et al., 2009; Jones, et at., 2003) as a Pest Module (Batchelor et al., 1993). The Pest Module allows users to input field observations and scouting data on insect damage, disease severity, and physical damage to plants or plant components (e.g., grains or leaves) and to simulate the likely effects of those pest and diseases on crop growth and economic yield.

For damage to be simulated, information must be provided on the specific pathways by which individual pests or diseases impact crop organs or crop growth processes. These pathways can define, for example, how much damage occurs on which parts of a plant on a daily basis as either a relative (e.g., percentage leaf area destruction per day) or an absolute (e.g., 10g seed destruction per day) value. The DSSAT Pest Module supports definition of the following pathways of pest and disease impact on crop growth:

  • Leaf mass destruction (%/day or g/m2/day)
  • Leaf area destruction (%/day or g/m2/day)
  • Stem mass destruction (%/day or g/m2/day)
  • Root mass destruction (%/day or g/m2/day)
  • Number of plants destroyed (#/m2/day)
  • Share of plants destroyed (%/day)
  • Reduction in assimilation of biomass (%/day)
  • Seed destruction (%/day , #/m2/day, or g/m2/day)

Depending on the characteristics of the pest or disease, damage can be expressed in a single or in multiple pathways. For example, one corn earworm larva is known to damage susceptible maize in following ways: 

  • Reduce small seed numbers by 10/m2/day
  • Reduce large seed numbers by 2.5/m2/day
  • Reduce leaf area by 0.5 %/m2/day

Example: Evaluating Acute Disease Infestation Impacts at Site and Regional Scales

Figure 1. Simulated impacts of leaf-damaging pest infestation on maize canopy development throughout the growing season at a single site. A range of “single shot” leaf damage events (at day 60) was implemented through damage pathway “coupling points” in the DSSAT model.

We illustrate the coupling point (or damage pathway) approach using the scenario of an acute “single shot” pest infestation on maize occurring two months (60 days) after planting. 

Figure 1 shows the simulated leaf area damage according to a range of assumptions about the severity of the infestation on canopy development throughout one growing season. Although the maize plant continues to grow (except in the case of complete leaf loss), the plant’s initial production potential cannot be recovered, resulting in a permanent loss of overall leaf development and, hence, compromised photosynthesis capacity and biomass production.  As more detailed ecological information about pest infestation becomes available, more realistic scenarios (e.g., damage with or without crop stress, extreme weather events, tolerant cultivars, or flexible planting windows) can be developed and tested to assess impacts on crop productivity, as well as to design efficient integrated pest management strategies (e.g., Willocquet et al., 2002). 

Figure 2. Simulated seasonal maize yield losses due to the impacts of recurring leaf-damaging pest infestation on maize canopy development throughout each growing season at (A) single site and (B) averaged over multiple major maize growing sites in Tanzania.

Beyond the single plot for a single season, such simulation experiments can be scaled up across larger areas for multiple seasons using the HarvestChoice Crop Systems Simulation Platform (Simplr) and help understand the spatial and temporal variability of crop production and yield under the impacts of disease infestation. For all major maize growing sites in Tanzania, represented as 10-km grid cells, three levels of hypothetical single-shot pest/disease infestation scenarios that are repetitively occurring two months after planting every season were simulated on rainfed/low-input maize production system (i.e., 20 kg[N] of urea application on traditional variety) for 20 years. 

Figure 3. Simulated maize yield (A) averaged for 20-year period in major maize growing areas in Tanzania and (B) average yield loss (%) from 50% of leaf area damage compared with the no-damage case.

Figure 2 shows the maize yield trends at one example site (Kwimba) and averaged over all sites. Seasonal yield loss was calculated by comparing the yield with controls with no damage. For the one site example, the highest level of yield loss coincided with the highest temporal variability, compared to the other two levels (Figure 2A). When averaged over space, however, such variability of yield loss was reduced and shown to be relatively stable over time. These suggest that there may be a threshold of leaf damage on maize, beyond which the damage is so devastating that the yield loss highly exacerbates, and the threshold level can be site-specific. 

Figure 4. Histogram showing the area for a range of average yield loss over the simulated time-period of 20 years.

Spatially, on average, yield loss increased as the damage became more severe--but at different rates (Figure 3). This is likely due to the different soil and climate conditions at each site influencing the stresses and development of maize thus the different extents of susceptibility. A histogram drawn for the range of yield loss also showed that there were distinctive spatial patterns of yield losses for the severity of leaf damage (Figure 4). 

With more detailed information about potential pest prevalence over time and space (e.g., HarvestChoice’s pest mapping outputs), more reliable estimate of crop yield losses can be made. HarvestChoice is evaluating the feasibility and reliability of such approaches for predicting the changing vulnerability of crops to pests and diseases at local, national and regional scale. 


  • Batchelor, W.D., J.W. Jones, K.J. Boote, and H.O. Pinnschmidt. 1993. Extending the Use of Crop Models to Study Pest Damage. Transactions of the Asae 36:551-558.
  • Boote, K.J., J.W. Jones, J.W. Mishoe, and R.D. Berger. 1983. Coupling Pests to Crop Growth Simulators to Predict Yield Reductions. Phytopathology 73:1581-1587.
  • Esnard, J. 2003. Plants Get Sick Too!, pp. Poster. The American Phytopathological Society, St. Paul, MN.
  • Hoogenboom, G., J.W. Jones, P.W. Wilkens, C.H. Porter, K.J. Boote, L.A. Hunt, U. Singh, J.L. Lizaso, J.W. White, O. Uryasev, F.S. Royce, R. Ogoshi, A.J. Gijsman, and G.Y. Tsuji. 2009. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 [CD-ROM]. University of Hawaii, Honolulu, Hawaii.
  • Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping system model. European Journal of Agronomy 18:235-265.
  • Willocquet, L., S. Savary, L. Fernandez, F.A. Elazegui, N. Castilla, D. Zhu, Q. Tang, S. Huang, X. Lin, H.M. Singh, and R.K. Srivastava. 2002. Structure and validation of RICEPEST, a production situation-driven, crop growth model simulating rice yield response to multiple pest injuries for tropical Asia. Ecological Modelling 153:247-268.


HarvestChoice, 2010. "Using Crop Systems Models to Study Yield Susceptibility to Pests and Diseases." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at

Aug 5, 2010