A commentary on systems studies in agriculture

Ebersohn, J. P.

doi:10.1016/0308-521x(76)90039-1

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…Systems modeling often demands multidisciplinary expertise and cooperation. A multidisciplinary approach can identify emergent properties ( Ebersohn, 1976 ; Fischer, 2008 ), which result from allowing discipline-specific knowledge to interact within a complex system. Single-discipline, physical experiments often provide the inputs and grounding for systems modeling, while modeling helps identify: 1) knowledge gaps where further experimentation or data collection is warranted and 2) the true contribution of new discoveries or novel strategies to a broader system.…”

Section: Introductionmentioning

confidence: 99%

Stochastic, individual animal systems simulation model of beef cow–calf production: development and validation

Aherin

Weaber

Pendell

et al. 2022

Translational Animal Science

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A stochastic, individual animal systems simulation model describing U.S. beef cow-calf production was developed and parameterized to match typical U.S. Angus genetics under cow-calf production conditions in the Kansas Flint Hills. Model simulation results were compared to available actual, multi-variate U.S. cow-calf production data reported according to Beef Cow-calf Standardized Performance Analysis (SPA) methodology through North Dakota State University’s CHAPS program to assess model validity. Individual animal nutrition, reproduction, growth, and health characteristics, as well as production state are determined on a daily time step. Any number of days can be simulated. These capabilities allow for decision analysis and assessment of long-run outcomes of various genetic, management, and economic scenarios regarding multiple metrics simultaneously. Parameterizing the model to match Kansas Flint Hills production conditions for the years 1995 through 2018, 32 different genetic combinations for mature cow weight and peak lactation potential were simulated with 100 iterations each. Sire mature cow weight genetics ranged from 454 kg to 771 kg in 45 to 46 kg increments. Sire peak lactation genetics were considered at 6.8, 9, 11.3, and 13.6 kg/d for all eight mature cow weights. Utilizing model results for the years 2000-2018, raw model results were assessed against actual historical cow-calf production data. Exploratory factor analysis was applied to interpret the underlying factor scores of model output relative to actual cow-calf production data. Comparing modeled herd output with CHAPS herd data, median average calf weaning age, average cow age, percent pregnant per cow exposed, and percent calf mortality per calf born of model output was 3.4 d greater, 0.2 yr greater, 1 percentage point less, and 1.7 percentage points greater, respectively. Subtracting the median CHAPS pre-weaning average daily gain from the median modeled pre-weaning average daily gain for each of the eight respective mature cow weight genetics categories, and then calculating the median of the eight values, the median difference was -0.21 kg per d. Performing the same calculation for birth weight and adjusted 205 d weaning weight, the modeled data was 4.9 kg and 48.6 kg lighter than the CHAPS data, respectively. Management and genetic details underlying the CHAPS data were unknown.

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Section: Introductionmentioning

confidence: 99%

Stochastic, individual animal systems simulation model of beef cow–calf production: development and validation

Aherin

Weaber

Pendell

et al. 2022

Translational Animal Science

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“…In this way, a model becomes a repository for past research and a precursor for future research. 3,4 Model construction is now a common activity that gives research direction and focus.…”

mentioning

confidence: 99%

Beef cattle production

Rickert

2001

Food Process Modelling

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“…Various benefits have been proposed as a result of model development, the more universal ones being (Goodall 1969, Anderson 1974, Ebersohn 1976, Dent et al 1979, Black et al 1982, Morley and White 1985, Trapp and Walker 1986, Blackbum and Cartwright 1987a, Walker 1993, SeUgman 1993, Stuth etal. 1993, Faichney 1996:…”

Section: Why Build a Model ?mentioning

confidence: 99%

“…Simulation of agricultural systems can be considered as the formalisation or quantification of ideas, observations, concepts and hypotheses based on experimental data into a series of mathematical equations (Smith and Williams 1973, Black et al 1982, Denham and Spreen 1986, Loewer 1989, Rickert and McKeon 1991. As such, simulation models act as a repository for past research and a guide for ftiture research (Ebersohn 1976, Dent et al 1979.…”

Section: What Is a Simulation Model?mentioning

confidence: 99%

Near-real time financial assessment of the Queensland wool industry on a regional basis

Hall

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TJiis thesis describes a systems analysis of the Queensland wool industry at a farm-enterprise level, and development of a computer simulation model designed to provide near real-time physical and financial information to policy makers at a regional level as an aid to decision making.Extensive reviews of the main biological factors affecting the wool enterprise were completed: diet selection and feed intake, protein digestion and metabolism, energy digestion and metabolism, wool growth, reproduction and mortalit}\ A sensitivity analysis of three separate bio-economic models was carried out to identify system components having the greatest impact on the financial performance of wool enterprises. Fleece production, wool price and total variable costs were found to be the most important determinants in each model. Grazfeed, a commercially available software package based on the Australian feeding standards for ruminants, was tested for its ability to simulate animal production as observed in Queensland sheep grazing experiments. A FORTRAN version of the required code for sheep and cattle was written and used in the subsequent analysis. These experiments varied greatly, both temporally and spatially. A number of problems in trial management and methodology were identified, these often required adjustment of recorded data. The collation of trial data was done in a manner that allowed ease of use in computer models. Optimisation software was used to modify parameters and equations within the model in an attempt to improve the agreement between predicted and observed values. Grazfeed was found to be unsuitable using the available test data.Regression analysis techniques were then used to identify climate, soil water, pasture and dietary variables which were able to explain the observed grazing trial variation in annual fleece production and liveweight change. The dietary variables were estimated using a theoretical diet selection subroutine, and a feed intake subroutine based on

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A commentary on systems studies in agriculture

Cited by 10 publications

References 1 publication

Stochastic, individual animal systems simulation model of beef cow–calf production: development and validation

Stochastic, individual animal systems simulation model of beef cow–calf production: development and validation

Beef cattle production

Near-real time financial assessment of the Queensland wool industry on a regional basis

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