R.H. Fawcett

Bio-economic evaluation of dairy farm management scenarios using integrated simulation and multiple-criteria models

Abstract Appropriate selection of holistic management strategies for livestock farming systems requires: (1) understanding of the behaviour of, and interrelations between, the different parts of the system; (2) knowledge of the basic objectives of the decision maker managing such enterprise; and (3) understanding of the system as a whole in its agro-ecoregional context. A decision-support system based on simulation and mathematical programming techniques has been built to represent pastoral dairy production systems. The biological aspects (grass growth, grazing, digestion and metabolism, animal performance) are represented by simulation studies under a variety of management regimes. The outputs from the simulation runs (such as pasture utilisation, stocking rates, milk yields, fertilizer use, etc.) are used as data input to the multi-criteria decision-making models, and the latter have been used to select the management strategies which make the most efficient use of the farms resources (i.e. land, animals, pastures). The paper discusses the effects and implications of different management scenarios and policies on the bio-economic performance of highland dairy farms in Costa Rica. Nevertheless, the model frameworks are generic and can be adapted to different farming systems or ruminant species. The effect of model formulation and sensitivity, different decision-maker objectives, and/or activity or constraint definitions on management strategy selection are also analysed.

Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy

Abstract The objective of this study was to predict the in vitro gas production and the estimated metabolisable energy (ME), crude protein (CP) and neutral detergent fibre (NDF) concentrations of kikuyu grass ( Pennisetum clandestinum ) by near infrared reflectance spectroscopy (NIRS). A total of 288 samples collected in the Poas Region, Costa Rica were scanned (Population 1). The in vitro gas production and ME calibrations were done on a subset of samples in which gas production measurements (3, 6, 12, 24, 36, 48, 72 and 96 h incubations) had been previously carried out (Population 2) while 41 samples for the CP and NDF calibrations (Population 3) were selected on the basis of their H distances from Population 1. The parameters a , b , c and lag for the exponential equation p = a + b (1 − e − c ( t − lag ) ) (McDonald, 1981), where p is the volume of gas produced at time t , were fitted to the gas production data and an attempt was also made to predict them. The volumes of gas produced between 6 and 48 h were successfully calibrated and cross-validated. Coefficients of determination for the cross-validation (1 − RV ) were 0.65, 0.74, 0.78, 0.70 and 0.60 for the volumes of gas produced at 6, 12, 24, 36 and 48 h respectively. The volumes of gas produced at 72 h could only be calibrated ( R 2 = 0.71) but not cross-validated, while the calibration results for the gas production at 3 and 96 h and the parameters for the exponential equation were poor. An analysis of the wavelength segments associated with the in vitro gas production indicated that the primary wavelength was always located between the 1664 and the 1696 nm spectral region regardless of incubation time. The estimated ME, CP and NDF concentrations were accurately calibrated and cross-validated. Standard errors of cross-validation of 0.23 MJ kg −1 DM, 11.4 g kg −1 DM and 15.9 g kg −1 DM were obtained for the ME, CP and NDF concentrations respectively. Scatter correction for particle size improved the performance of most of the equations across all constituents. The effects of different calibration methods, maths treatments and the factors affecting the results are discussed.

A decision support modelling framework for multiple use forest management: The Queen Elizabeth Forest case study in Scotland

Abstract A multiple criteria interactive modelling framework has been developed to support forest resource allocation decisions in the context of multiple use forest management at the tactical level. The modelling framework consists of four components: (i) Intelligence, referring to the forest management problem formulation; (ii) design, which uses a technological forecast model to produce technical coefficients; (iii) choice, which uses multi-criteria analysis based on a combined MINMAX approach and generates iteratively in “trade-off” outputs; and (iv) implementation, related to the final resource allocation scheme adopted after examining trade-offs. The system allows exploration of alternatives, which are not extreme points of the feasible solution set. The weighting procedure is done internally by the system and forest managers are given more flexibility in changing goal targets as more information is gained about the problem. The combined MINMAX approach used in the choice component converges fast to the final solution. Forest managers are in full control of the decision making process and therefore, they can provide their answers and solutions to forest resource allocation problems. The decision support framework is closely linked to the planning and management conditions of the Queen Elizabeth National Forest Park in Central Scotland. However, the system is adaptable to other similar multiple use forest management cases.

A Decision Support System for smallholder campesino maize-cattle production systems of the Toluca Valley in Central Mexico. Part I— Integrating biological and socio-economic models into a holistic system

Abstract The objective of this work was to develop a Decision-Support System (DSS) in order to support the decision making process by campesino farmers of Central Mexico. Two biological models, one socio-economic model and a survey database form the DSS. The CERES-Maize model simulated the yield response of three local land-races of maize to different management systems. The second biological model, a cow model (dynamic hybrid model), was used to simulate alternative feeding systems. A multi-period mathematical programming model integrated the outputs of the previous models with the survey database. This model was used to find the optimal combination of resources and technologies that maximised farmers’ income. This model consists of 15,698 structural columns and 612 rows. The DSS successfully reproduced the functioning of the farming systems main components. More importantly, it simulated the complex interactions observed between the farmers and their crops and cattle, including traditional maize management practices.

Modelling the growth and utilisation of kikuyu grass (Pennisetum clandestinum) under grazing. 1. Model definition and parameterisation.

This paper describes a Tropical Pasture Simulator. This is a simple mechanistic model representing the growth of vegetative tropical pastures under rotational or continuous grazing with responses to N fertiliser, temperature and irradiance that can be used as a tool to study management options for the development of sustainable grazing systems. The model was derived as an adaptation of the ‘Hurley pasture models’ which were originally designed to simulate the growth of ryegrass. The model incorporates the processes of light interception and photosynthesis, leaf area expansion; growth, ageing and senescence of plant tissues, recycling of substrates from senescing tissues, nitrogen uptake, mineralisation of soil organic N, N leaching and grazing. The paper discusses the main structure of the model, its components and the main adaptations required to simulate the growth of tropical pastures. The model was parameterised for kikuyu grass (Pennisetum clandestinum) growing in dairy systems in highland regions of Costa Rica. This paper also discusses the sensitivity of parameter values and the development of minimum parameter datasets for time and cost-eAective implementation of the model. # 2000 Elsevier Science Ltd. All rights reserved.

Prediction of the in vitro gas production dynamics of kikuyu grass by near-infrared reflectance spectroscopy using spectrally-structured sample populations

A study was carried out to test: (1) if the prediction of in vitro gas production of kikuyu grass (Pennisetum clandestinum) samples by near-infrared reflectance spectroscopy (NIRS) could be improved by the use of a spectrally-structured sample population; and (2) if the parameters from exponential models used to describe the kinetics of gas production could be calibrated by NIRS. Forty-one kikuyu grass samples (calibration set) were chosen out of a total of 288 on the basis of their spectral characteristics as representative samples of the whole sample population. Measurements of cumulative in vitro gas production were recorded at 3, 6, 12, 24, 48, 72 and 96 h. Spectra were transformed with 1st, 2nd or 3rd derivative mathematical treatments. NIRS calibration equations were derived for in vitro gas production, with or without scatter correction for particle size using modified partial least squares. The equations were validated using a set of 48 samples previously chosen at random from the total sample population (validation set). Satisfactory calibrations and cross-validations were obtained for all the static measurements of gas production (R2 = 0.77−0.86 (S.E. 0.48−2.06) and 0.74−0.82 (S.E. 0.50−2.18), for the gas volumes from 3 to 96 h, respectively), and the use of a spectrally-structured population improved the calibration and cross-validation statistics of the NIRS equations. However, when three exponential models were fitted to the gas production data, only the asymptote values could be satisfactorily calibrated or cross-validated. When the NIRS equations were used in the validation set, the static gas volumes were predicted with R2 values between 0.60 and 0.71 (S.E. 0.65 to 3.83, for the gas volumes from 3 to 96 h). These results were less accurate than within the calibration set, but they were still better than when the parameters from the exponential models were fitted. The results suggested that the calibration of static gas volumes is a more promising alternative than to fit specific parameters of kinetic fermentation models by NIRS.

A Decision Support System for smallholder campesino maize-cattle production systems of the Toluca Valley in Central Mexico. Part II--Emulating the farming system

Abstract This paper describes the functioning and validation of the Decision Support Systems described in the first part of the paper. The DSS ran three case studies with different farm sizes that represent the range of farmers found in the Valley. The DSS results were validated against survey data for the same cases. Traditional technologies for maize and milk production were reproduced by the DSS including land use and cattle feeding systems. The generic nature of the DSS was demonstrated as well as its capacity to deal with the systems socio-economic and biological aspects. The results suggest that the DSS was successful in reproducing the functioning of the farming systems main components. More importantly it simulated the complex interactions observed between the farmers and their crops and cattle. Finally, it is acknowledged that despite the size and complexity of the DSS, it only was able to emulate the functioning of the main components of the farming system.

Modelling the growth and utilisation of kikuyu grass (Pennisetum clandestinum) under grazing. 2. Model validation and analysis of management practices

This paper presents validation results and some management applications of a Tropical Pasture Simulator. The eAects of diAerent environmental conditions, N fertilisation regimes and grazing intensities are analysed, and physiological concepts are used to aid in the interpretation of the responses obtained. The model demonstrates the importance of flexible management guidelines depending on environmental conditions. It stresses the need for an increased understanding of the processes controlling the development and senescence of the sward, since these largely influence the responses to nutrients and determine sward structure. Strategies based on morphological indicators such as number of live leaves were derived in order to develop simple and applicable pasture management guidelines at the farm level. Examples are presented with reference to kikuyu grass (Pennisetum clandestinum) production and utilisation in dairy systems in the highlands of Costa Rica. # 2000 Elsevier Science Ltd. All rights reserved.

The role of systems research in grazing management: applications to sustainable cattle production in Latin America

Advances in pasture science over the past 25 years have considerably widened the scope for improvement in ruminant livestock production from grazing systems. The introduction of improved pastures and legumes to increase output per animal and/or per hectare is a classic example of such advances. To ensure the long-term success of these technologies, management of the grazing system has to be improved, and must deal with strategies integrating multiple choices over an extended planning horizon. For example, the control of stocking rates, paddock rotation lengths and fertilizer practices is important to maintain pastures in the long run. However, sustainable grazing cannot be achieved if management strategies like these are taken without understanding the whole farming system and the interactions between its components. This paper describes current efforts and the role of decision-support systems created by linking livestock information systems and whole-farm models to support management decisions in cattle farms of Latin America.

EVALUATION OF THE CERES-MAIZE MODEL IN SIMULATING CAMPESINO FARMER YIELDS IN THE HIGHLANDS OF CENTRAL MEXICO

A procedure was used to calibrate the DSSATv3 CERES–Maize ( Zea mays ) model and to evaluate its performance in simulating growth and development of maize using input data collected from campesino farmers instead of using data obtained from on-station experiments or from the literature. The problems encountered in the calibration process are illustrated, particularly the failure of the model to simulate the growth and development of local highland maize cultivars (HMC). It is argued that the low ambient temperatures to which HMCs are exposed in the Toluca Valley are responsible for this failure, because HMCs respond differently to temperature and have a different optimum temperature range from temperate and tropical maize cultivars. It was concluded that the model needs to be adjusted to allow for consideration of the effects of constant low temperatures on the prediction of plant phenology and production, and partition of biomass in HMCs, since grain yield is not the main criterion used by smallholders when selecting maize cultivars.