peanut_field_a_flavusv3.nlogo

breed [peanuts peanut]
breed [spores spore]
patches-own [soil-infected?]
peanuts-own [plant-infected? pod-count kernel-count kernel-af]



to setup
  clear-all
  setup-soil
  setup-spores

  reset-ticks
end

to setup-soil
  ask patches [ set pcolor brown set soil-infected? FALSE]

  ;make a square field of peanut plants
  ask patches with [ abs pxcor < 13 and abs pycor < 13 ]
    [sprout-peanuts 1 [ set shape "peanut_plant"  set size .25 set pod-count  0 set kernel-af 0] ;setting up our peanut plants. Note that each plants has no pods at the beginning
  ]


end


to setup-spores ;here each patch can be infected or not. We color it orange.
  ask n-of (infected_patches * (32 ^ 2) / 100) patches [sprout-spores 1 [set shape "aspergillus" set size 0.5 set pcolor orange]]


end

to go
  ask peanuts [set size size + 0.005] ;allow our peanut plants to grow each tick, which represent 1 day
  infect-patches; spores infect the soil
  infect-from-patches ; the soil infects the peanut plants
  move-spores
  add_pods ;as peanuts grow, they produce pods in the soil
  infect_kernels ;kernels become infected when a plant is infected and and it's vulnerable due to environmental stress

  if ticks >= Season_length [ stop ] ; assume peanut harvest after 9-120 days
  tick
end


to move-spores
  ask spores [
    right random 360
    forward 0.1
  ]
end


to infect-patches
  ask patches with [ any? spores-here]
    [set pcolor orange]
end

to infect-from-patches
; note that infection of plants is a stochastic process. However, the probability depends partly on whether pesticides are used to prevent plant disease and other pests in the first place
  ask peanuts [
    ifelse (pcolor = orange) and (pesticide-use = false) and (random 100 < 50) ;we assume plant infection is more likely if pesticides are not used.
    [
      set shape "peanut_plant_infected"
      set plant-infected?  true

    ]
    [
    if (pcolor = orange) and (pesticide-use = true) and (random 100 < 5)
      [
        set shape "peanut_plant_infected"
        set plant-infected?  true
      ]

    ]]
end


to add_pods ;we add pods based on how many days we are into the growing season. The pods start to develop at day forty
  ask peanuts[
  (ifelse
    ticks < 40
    [set pod-count 0]
    ticks = 40
    [set pod-count 10]
    ticks mod 5 = 0
      [
        if heat-stress = true [set pod-count pod-count + 1]   ;water stressed plants will add pods more slowly
        if heat-stress = false [set pod-count pod-count + 2]
      ]
   )


               ]
  ask peanuts [set kernel-count pod-count * 2]
end

to infect_kernels ;infection of peanut kernels depends on both use of water stress of the the plant and pesticide use. With more water stress, plants produce fewer phytolexins
                  ;and other defenses against aflatoxigenic fungi
  ask peanuts[
  if plant-infected? = true[
    (ifelse
      (heat-stress = false) and (pesticide-use = true)
      [set kernel-af 1 ]

       (heat-stress = false) and (pesticide-use = false)
      [set kernel-af  20  ]

      (heat-stress = true) and (pesticide-use = true)
      [set kernel-af 50 ]

      (heat-stress = true) and (pesticide-use = false)
      [set kernel-af 100 ]
  )]]

end
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Infected Plants
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TEXTBOX
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Agent Based Model of Peanut Aflatoxin Contamination in a Field
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0.0
1

@#$#@#$#@
## WHAT IS IT?

All of us want food that is safe from contaminants, right? Aspergillus flavus is a toxigenic fungus that can infect grains before and after harvest in hot and humid areas, producing a potent carcinogen know as aflatoxin. Here we model the infection and growth of A. flavus in a peanut field over a growing season and the contamination of peanut kernels due to aflatoxins. The agents in this model are spores of A. flavus and peanut plants. Patches are soil. 

## HOW IT WORKS


A peanut crop planted in rows grows over a season ranging from 90 to 120 days.

Aspergillus flavus is randomly distribute in the field and its life cycle is composed of two main phases:
1) colonization of plant residues in soil
2) infection of crop tissues

As the pods of peanuts grow at the roots, peanut pods come into contact with  aspergillus flavus spores in the soil. Normally healthy plants counter the growth of A flavus with resistance mechanisms such as production of compounds such as phytolexins. However, when plants are water stressed, water activity lowers, and kernels produce lower levels of phytolexins. Plants can be left more vulnerable by additional state variables  such as pest infestation, disease, and commpetition of soil resources against weeds. Therefore use of pesticides is a factor affecting plant health and resistance to aflatoxigenic fungi. Ultimately peanut kernel contamination occurs when peanut pods crack as a result of disease, water stress, and pest infestation. 

Important note: as this model is a simple proof of concept, it does not employ an exhaustive use of current knowledge of fungal soil ecology or the biosynthetic pathways underpinning aflatoxin production by A. flavus and other toxigenic fungi. Furthermore, aflatoxin contamination tends to occurs in hotspots with extreme contamination and is rarely uniform within a field. 


## HOW TO USE IT

For a quickstart, press setup, then go. 

You can use sliders and settings to adjust the % of soil patches infected with A. flavus, peanut growing season length, and agronomic factors (pesticide use, water stress).

The plots indicate the number of sick plants in the field, the average number of pods per plant, and the average aflatoxin in the whole field. 

## THINGS TO NOTICE

You'll see that this is a dynamic system: fungal spores move within the field, peanut plants grow... They become infected depending on the agronomic conditions and fungal prevalence that you control.

## THINGS TO TRY

Change the percentage of infected soil patches. You'll see a big impact on peanut contamination. 

## EXTENDING THE MODEL

Some parameters in this model were not taken directly from literature and should be rigorously reviewed, such as empirical rates of A. flavus spread within a field during a growing season, average aflatoxin contamination associated with the agronomic factors in the model, etc.  This is especially true for the estimation of kernel aflatoxin, which is influenced by many more factors than we can model here (micro-stresses localized within a field, rainfall, humidity, temperature, irrigation efficacy, soil history, peanut variety, etc). Since aflatoxins are not normally distributed, it may be interesting to model a different estimator for contamination instead of the mean. 


## RELATED MODELS

I'm not aware of other open source ABMs that model aspergillus flavus spread in a field and the amount of aflatoxin crops. But if there are, please contact me to share. There are however, models of aflatoxin risk. See these for examples: https://cropmanagement.cals.ncsu.edu/risk-tools/default.html

## CREDITS AND REFERENCES

The deterministic variables and relationships of this model were inspired by the following papers. They can be further reviewed by those interested in improving the parameters of the model. 

Abbas, HK, et al. 2009. Ecology of Aspergillus flavus, regulation of aflatoxin production, and management strategies to reduce aflatoxin contamination of corn. Toxin Reviews. 28 (2-3). 

Abde-Hadi, A, et al. 2012. Journal Royal Society Interface.A systems approach to model the relationship between aflatoxin gene cluster expression, environmental factors, growth and toxin production by Aspergillus flavus. 9(69)

Dorner JW, et al. 1989. Interrelationship of kernel water activity, soil temperature, maturity, and phytoalexin production in preharvest aflatoxin contamination of drought-stressed peanuts. Mycopathologia. 105. 

Horn, BW. 2006. Relationship between soil densities of Aspergillus species and colonization of wounded peanut seeds. Can J Microbiol.

Magan, N and Olsen M. 2004. Chapter 8: Environmental Conditions Affecting Mycotoxins. In: Mycotoxins in food: Detection and control. CRC Press.

Sanchis V., Magan N. 2004. Environmental conditions affecting mycotoxins. In Mycotoxins in food: detection and control, ch. 8 (eds Magan N., Olsen M.), pp. 174–189 Cambridge, UK: Woodhead Publishing Ltd

US Department of Agriculture. Web Soil Survey- Soil Health-Organic Matter- Summary by Map Unit-- Decatur County, Georgia. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. Accessed 12/28/2022. 

Zablotowicz RM. 2007. Population ecology of Aspergillus flavus associated with Mississippi Delta soils. Food Additives and Contaminants. 24. 
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