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Sep 30, 2023 125 likes •355 views

A linear programming model to optimize diets in environmental policy scenarios Moraes, L.E. et al. (2012) Katarina Nielsen Dominiak Department of Large Animal Sciences Department of Large Animal Sciences Introduction Global food demands

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A linear programming model to optimize diets in environmental policy scenarios Moraes, L.E. et al.(2012)

Katarina Nielsen Dominiak Department of Large Animal Sciences

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Introduction

Global food demands increase -> more animal products will be produced Carbohydrate fermentation (from forage) in dairy cattle produces CH4 Protein, starch and minerals in feed can lead to excretion of N and minerals if fed at higher levels than animal requirements 72% of total emitted CH4 in Brazil was from enteric fermentation (1994)

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Introduction

Greenhouse gas (GHG), NO3-, and minerals contaminate the environment Policies and legislations formulated to limit environmental impacts of livestock production

Clean Water Act (US EPA 2003)
Manure applied to crops and pastures at levels the

plants can extract (Nitrate,Phosporous)

Kyoto Protocol reduces GHG to level of 1990
Carbon markets in Europe (and the States)

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Problem

Mandatory carbon (CH4) emission policies might be the future Two policies are discussed:

Limit the quantity of CH4 emitted
Require emission taxes

A precise diet formulation and balance will meet BOTH animal nutritient requirements AND decrease environmental impacts of animal agriculture

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Aim

Reduce CH4 emission and excretion of N and minerals Optimal diet cost and feed selection Joint optimization of costs and emisions+excretions Linear programming: A method to achieve best outcome given certain constraints Constraints are represented by linear relationships

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Three scenarios – three models

BASEM: Cheapest possible feed – No GHG policies (baseline) TAXM: Effect of tax on diet costs and composition, emission and excresion Computes optimal feed input mix that balances off tax savings for lower emissions REDM: Feed costs vs emission and excresion if forced to reduce CH4 emission Decision variables like BASM but with the extra constraint

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

Constraints: Nutrient requirements Defined feed limits Fibre proportions Decision variables: Available feed and their costs

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Assumptions

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Assumptions

Locally collected

kg/kg DM $/kg DM

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

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Prediction of emission and manure production

~55 MJ/kg How much is emitted?

Feed composition – absorbstion Excrete composition

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

CH

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Objective functions

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

BASEM and REDM

j = feed x = amount of feed (kg of DM) c = cost of feed

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Objective functions TAXM

e = expected emission (tonnes) p = tax price per tonne

CH

K.N. Dominiak, AQMHM 2015

Department of Large Animal Sciences

j = feed x = amount of feed (kg of DM) c = cost of feed

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Constraints

Dias 12

Nutrient req.

Diet. Feet Limits

Methane Restrictions DMI Other Limits, 1

Diet. Barley Limits

Other Limits, 2

Diet. Forage Proportions

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Nutrient requirement constraints

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

Amount Nutrient content Requirement

j = feed a = cow category (7 kinds) i = nutrient (14 kinds)

7 x 14 = 98 equations

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Dietary feed limits constraints

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

∙

j = feed a = cow category (7 kinds) l = limit (for 10 kinds of j)

7 x 10 = 70 equations

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Methane Restriction constraints (REDM)

Dias 15

Total feed (all cows, all feed) Methane emission predictions reduction (%) BASEM emission Max. Emission 1 - CH

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Results TAXM

Cost of reducing emission through the diet > tax costs Therefore: No differences in emission between BASEM and TAXM

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

CH CH

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Shadow prices

Sensitivity analysis ‘What is the cost of reducing 1 tonne of CH4 emission?’ Extremely sensitive to feed prices – further analysis required

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

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Results REDM

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

CH emission reduced but diet costs increased

5% 19,1% 48,5%

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Because forage = NDF (lignin, cellulose, hemicellulose) And grain/soy = ME (protein, starch, minerals) The model results in a trade off between the two types But at what consequences?

Results – trade offs in diet formulas

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

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Results – trade offs in diet formulas

Consequences of CH4 reduction Feed ME NDF N+min ex Corn silage Low High Low Soy silage High Low High Grain High Low High Cereal High Low High Soybean meal High Low High Total reduction in DMI

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

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Discussion Animals vs Human in competition for feed ressources ‘N + mineral excresions follows same pattern as intake’ CH4 vs NO3

results in conflicting environmental interests

Should mineral diets be based on excresion levels?

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

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What about the cow?

Department of Large Animal Sciences

K.N. Dominiak, AQMHM 2015

Consequences of CH4 reduction Feed ME NDF N+min ex Corn silage Low High Low Soy silage High Low High Grain High Low High Cereal High Low High Soybean meal High Low High Total reduction in DMI