Modeling smallholder crop-livestock systems in the Yucatan

Peninsula, Mexico

David Parsons

Yucatan - background

Location

Climate

• Tropical sub-humid• Dry & rainy seasons• Average rainfall 800-

1000mm• Hot• Rainfall distribution is

variable on peninsula

Soils

• Soils formed on Tertiary limestone, flat terrain• Shallow and stony, limited cultivation

Soils

• Alkaline, limited in nitrogen, phosphorus, manganese, iron, zinc

• Low water holding capacity

Vegetation

• Low-stature tropical dry forests (wet season)

Vegetation

• Low-stature tropical dry forests (dry season)

Vegetation

• Dominated by leguminosae

Milpa• Slash and burn, 0.5

to 2.5 ha• 2-3 year cultivation• 10-20 year fallow

Milpa Maize mixed with melons and beans

Weeds and pests controlled manually

Sisal production – the Boom…

• Sisal production from henequén, the foundation of the Yucatán economy

• Drop in demand post WWII, due to surplus production and synthetic fibers

…and the Bust• Industry collapse in 1992, after withdrawal of

Government subsidies• “Henequén zone” left underemployed

Rural poverty in Yucatan

• In 1996, 63% of children in Mayan communities were chronically malnourished (Balam, 1996)

• In 2000, nearly twice as many counties in the Yucatan Peninsula were classified as extremely poor compared to 1995 (41 v 21 - - Yucatan census)

The livestock revolution

• Expected 7.7 B people by 2020

• Greater demand for food will aggravate agricultural pressure on the environment

• Increasing global demand for livestock products

• By 2020 developing country consumers will eat 87% more meat and 75% more milk

The consequences

• Opportunities & challenges for rural poor• Potential to alleviate poverty & spur

economic growth• Negative consequences e.g. environment• How can livestock production increase to

meet this demand using sustainable methods?

• We need to avoid generalizations about livestock and their relationship with the environment

Global Animal Production Systems

• Grazing

• Industrial

• Mixed farming

“Systems utilizing native grasslands with little or no integration with cropping systems.”

Global Animal Production Systems

• Grazing

• Industrial

• Mixed farming

“Systems where the animals are detached from the land base of feed supply.”

Global Animal Production Systems

• Grazing

• Industrial• Mixed farming

“Systems where livestock rearing and crop cultivation are to a greater or lesser extent integrated components of one farming system.”

3 Production Systems

• Production systems– Grazing– Industrial– Mixed farming

• What are the implications of these practices for the sustainability of the agricultural systems, the environment and farmer livelihoods

Smallholder sheep farmers

Why hair sheep?

• Adapted to the tropics

• High fertility

• Adept at browsing fibrous biomass

• Low entry level, accessible to small producers

• Strong demand in Mexico City

Sheep Farmer Survey

• 2004 survey of Yucatan sheep farmers

• Villages randomly chosen

• 66 farmers interviewed– Household– Livestock– Cropping– Infrastructure– Technical & Financial Assistance

The house

The house

‘Milpa’

The house

4 km

‘Milpa’

The house

4 km

‘Milpa’

The house

4 km

‘Milpa’

ForestThe house

4 km

1 km

‘Milpa’

ForestThe house

4 km

1 km

And the point is…..

• Each of these practices has implications for agricultural sustainability, the environment and the household

• Agricultural systems are complex

• People like to simplify, generalize, detach

• It is difficult to assess the implications of a combination of agricultural practices

System Dynamics

• A thinking and modeling approach that emphasizes the role of the structure of a system in determining the evolution of a problematic behavior over time

• Problems often arise from feedback processes rather than simple one-way causal relationships

• A mechanistic rather than empirical approach to modeling

Basic System Dynamics

Population(sheep)

Basic System Dynamics

Population(sheep)

Population(sheep)

Death Rate(sheep/year)

Birth Rate(Sheep/year)

Basic System Dynamics

Population(sheep)

Population(sheep)

Death Rate(sheep/year)

Birth Rate(Sheep/year)

+ ++ -

‘That, detective is the right question.’

Research Proposal

Problems/Issues

• In Yucatán there exist seemingly competing goals for forest use, including agriculture, livestock production, and preservation for biodiversity.

• Smallholder sheep farming is a relatively recent but expanding agricultural enterprise.

• Little is known about the interactions between sheep farming, household economics, crop production and the environment.

Research Questions

• How do the presence of sheep alter nutrient availability and recycling, and other biophysical attributes, and what are the consequences of this?

• How do specific agricultural management practices, both currently used and hypothetical, affect the system?

• Can integration of livestock and cropping potentially result in longer use of a milpa, thus intensifying agricultural production?

Hypotheses• Livestock can increase the income of

smallholder farmer households• Livestock can accelerate the flow of

nutrients in a mixed cropping system, making them available for crop growth

• Livestock and cropping can be synergistic• Livestock facilitate the importation of

nutrients• The use of animal manure on crops can

maintain soil fertility

Types of data

• Soil • Plant • Animal • Manure • Household - including economic data

and labor

Methods of data collection for model

• Existing data

• Experimentation

– Corn response to sheep manure

– Forage response to sheep manure?

• ‘Case study’ farms

Farm selection• 8 ‘Case Study’ Farms

– 4 different villages• Mixed sheep & cropping (Milpa)• Solely cropping

• Sheep farms chosen to characterize a range of representative mixed farming management practices – e.g. growing forages, forest browsing, forest cut

and carry, stubble utilization, manure use

Simplified Physical Structure

Cornbiomass S4

Croplandsoil S7

Corn nutrientuptake F15

Corndecomposition

F14

Weedbiomass S5

Weed nutrientuptake F16

Weeddecomposition F17

Corn grainS3

Corn harvest F13

Sheep S2

Foragebiomass S6

Forage soilS8

Foragedecomposition F20

Forage nutrientuptake F21

Forage fertilization F22

Cut & carry oftrees F8

Corn stubbleconsumption F4

Weed consumptionF5

Forageconsumption F6

Sheeppurchases/sales F1

Forage soilleaching F23

Crop soilleaching F19

Nutrient increasefrom fallow F18

Manure S1

Defecation F9

Manure use oncrops F10

Manure use onforages F11

Sheepbirths/deaths F2

Purchase of feed F7Grain fed tosheep F3

Humanconsumptionof grain F12

Smallholder Crop-Livestock Model

• Brief overview of model

• Policy optimization example

Model overview

• Model sections– Crop production (Corn, no weeds)– Forage (Taiwan grass)– Nutrient cycling (Nitrogen)– Sheep (No aging chain)– Manure– Feeding options

A Farmer Policy Optimization• Maximize:

– the amount of corn harvested– Maximize animal production (through fed forage)– 1kg corn grain = 2kg forage produced

• What can the farmer do (in this example)?– Change number of sheep (0-50)– Change area of forage (0-10 ha)– Change area of maize (0-4 ha)– Change proportion of manure applied to forage

(0-1)– Decide whether or not to graze corn stubble (0 or

1)

Optimization Results

Base run OptimizedManure proportion used on forage 1 0.28Corn stover feeding 0 1

Forage area 1 0.7

Corn area 1 4

Initial sheep 20 24

Forage manure N decomposition

40

30

20

10

0

6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60Time (Month)

Forage manure N decomposition : optimize kg N/(ha*Month)Forage manure N decomposition : base kg N/(ha*Month)

Total fed forage

40,000

30,000

20,000

10,000

0

6 12 18 24 30 36 42 48 54 60Time (Month)

Total fed forage : optimize kg forage/haTotal fed forage : base kg forage/ha

Crop soil available N

2,000

1,700

1,400

1,100

800

6 12 18 24 30 36 42 48 54 60Time (Month)

Crop soil available N : optimize kg N/haCrop soil available N : base kg N/ha

Forage soil available N

2,000

1,700

1,400

1,100

800

6 12 18 24 30 36 42 48 54 60Time (Month)

Forage soil available N : optimize kg N/haForage soil available N : base kg N/ha

Corn harvest

2,000

1,500

1,000

500

0

6 12 18 24 30 36 42 48 54 60Time (Month)

Corn harvest : optimize kg corn/(ha*Month)Corn harvest : base kg corn/(ha*Month)

Total corn harvest

8,000

6,000

4,000

2,000

0

6 12 18 24 30 36 42 48 54 60Time (Month)

Total corn harvest : optimize kg corn/MonthTotal corn harvest : base kg corn/Month

Points to consider…• The forage is receiving some

fertilization• This assumes corn is to be grown on

the same plot of land, so the farmer has to maintain fertility to get a corn yield

• Constraints of labor and cash• A lot depends on:

– The structure of the model

– How the optimization is weighted

Improving the model

• Get the parameters ‘right’– Data collection

– Literature review

– Sensitivity analysis

• Get the structure ‘right’– Level of model aggregation

– Appropriate boundary

Model Application

• Generate an accounting of nutrient flows and economic characteristics of 8 farms with different management practices

• Increase understanding of how these crop-livestock systems function

• Test the possible outcomes of farming strategy combinations outside the range of experimental data collection

• Prediction of the effects of potential interventions, including a better understanding of possible ‘side-effects’

Model Misapplication

• The model should not be asked to answer questions that it is not equipped to answer

“I’m sorry. My responses are limited. You must ask the right question.”

Model Misapplication

• The model should not be used as a ‘magic’ black box

Model Misapplication

• This is not a model for farmers to use

Conclusions

• Livestock have the potential to improve rural livelihoods in Yucatan

• However, there is a need for better understanding of interactions between livestock and cropping systems

• They are complex, dynamic and variable systems, best studied using systems thinking and methods