Development of profitable milk production systems for northern Australia: a field assessment of the productivity of five potential farming systems using farmlets.

Farmlets, each of 20 cows, were established to field test five milk production systems and provide a learning platform for farmers and researchers in a subtropical environment. The systems were developed through desktop modelling and industry consultation in response to the need for substantial increases in farm milk production following deregulation of the industry. Four of the systems were based on grazing and the continued use of existing farmland resource bases, whereas the fifth comprised a feedlot and associated forage base developed as a greenfield site. The field evaluation was conducted over 4 years under more adverse environmental conditions than anticipated with below average rainfall and restrictions on irrigation. For the grazed systems, mean annual milk yield per cow ranged from 6330 kg/year (1.9 cows/ha) for a herd based on rain-grown tropical pastures to 7617 kg/year (3.0 cows/ha) where animals were based on temperate and tropical irrigated forages. For the feedlot herd, production of 9460 kg/cow.year (4.3 cows/ha of forage base) was achieved. For all herds, the level of production achieved required annual inputs of concentrates of similar to 3 t DM/animal and purchased conserved fodder from 0.3 to 1.5 t DM/animal. This level of supplementary feeding made a major contribution to total farm nutrient inputs, contributing 50% or more of the nitrogen, phosphorus and potassium entering the farming system, and presents challenges to the management of manure and urine that results from the higher stocking rates enabled. Mean annual milk production for the five systems ranged from 88 to 105% of that predicted by the desktop modelling. This level of agreement for the grazed systems was achieved with minimal overall change in predicted feed inputs; however, the feedlot system required a substantial increase in inputs over those predicted. Reproductive performance for all systems was poorer than anticipated, particularly over the summer mating period. We conclude that the desktop model, developed as a rapid response to assist farmers modify their current farming systems, provided a reasonable prediction of inputs required and milk production. Further model development would need to consider more closely climate variability, the limitations summer temperatures place on reproductive success and the feed requirements of feedlot herds.

A multi-field bio-economic model of irrigated grain-cotton farming systems.

We present a participatory modelling framework that integrates information from interviews and discussions with farmers and consultants, with dynamic bio-economic models to answer complex questions on the allocation of limited resources at the farm business level. Interviews and discussions with farmers were used to: describe the farm business; identify relevant research questions; identify potential solutions; and discuss and learn from the whole-farm simulations. The simulations are done using a whole-farm, multi-field configuration of APSIM (APSFarm). APSFarm results were validated against farmers' experience. Once the model was accepted by the participating farmers as a fair representation of their farm business, the model was used to explore changes in the tactical or strategic management of the farm and results were then discussed to identify feasible options for improvement. Here we describe the modelling framework and present an example of the application of integrative whole farm system tools to answer relevant questions from an irrigated farm business case study near Dalby (151.27E - 27.17S), Queensland, Australia. Results indicated that even though cotton crops generates more farm income per hectare a more diversified rotation with less cotton would be relatively more profitable, with no increase in risk, as a more cotton dominated traditional rotation. Results are discussed in terms of the benefits and constraints from developing and applying more integrative approaches to represent farm businesses and their management in participatory research projects with the aim of designing more profitable and sustainable irrigated farming systems.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.