The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.

This paper describes the physiological basis and validation of a generic legume model as it applies to 4 species: chickpea (Cicer arietinum L.), mungbean (Vigna radiata (L.) Wilczek), peanut (Arachis hypogaeaL.), and lucerne (Medicago sativa L.). For each species, the key physiological parameters were derived from the literature and our own experimentation. The model was tested on an independent set of experiments, predominantly from the tropics and subtropics of Australia, varying in cultivar, sowing date, water regime (irrigated or dryland), row spacing, and plant population density. The model is an attempt to simulate crop growth and development with satisfactory comprehensiveness, without the necessity of defining a large number of parameters. A generic approach was adopted in recognition of the common underlying physiology and simulation approaches for many legume species. Simulation of grain yield explained 77, 81, and 70% of the variance (RMSD = 31, 98, and 46 g/m2) for mungbean (n = 40, observed mean = 123 g/m2), peanut (n = 30, 421 g/m2), and chickpea (n = 31, 196 g/m2), respectively. Biomass at maturity was simulated less accurately, explaining 64, 76, and 71% of the variance (RMSD = 134, 236, and 125 g/m2) for mungbean, peanut, and chickpea, respectively. RMSD for biomass in lucerne (n = 24) was 85 g/m2 with an R2 of 0.55. Simulation accuracy is similar to that achieved by single-crop models and suggests that the generic approach offers promise for simulating diverse legume species without loss of accuracy or physiological rigour.

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The duration from sowing to flowering is an important determinant of fibre yield potential in hemp, since maximum stem yield occurs shortly after flowering. As a short-day plant, daylength has a key influence on the timing of flowering in hemp. This paper reports on studies into the effect of photoperiod on the thermal time duration from sowing to flowering for 2 hemp cultivars, and develops parameters to enable simulation of post-emergent phenology in the hemp model described in the final paper of this series. The hemp model divides the post-emergent period into a vegetative phase that ends at floral initiation, aflower development phase (FDP) between flower initiation and appearance, and a short phase between first flower appearance and harvest maturity (male anthesis). The vegetative phase is further divided into a temperature-dependent basic vegetative phase (BVP) and a daylength-dependent photoperiod induced phase (PIP). For a short-day plant, the duration of PIP is assumed to be zero degree days at daylengths below a base or maximum optimum photoperiod (MOP). Daylengths in excess of the MOP lead to an increase in thermal time within PIP, the duration of which is determined by a genotype’s photoperiod sensitivity (PS). Two hemp genotypes, Kompolti and Futura 77, were exposed to 6 different photoperiod regimes ranging from 8 to 16 h in a growth chamber. Thermal time durations from emergence to flower initiation and first flower formation (harvest) were calculated from thermograph plots. The flowering responses for the 2 cultivars were typical for a short-day plant, with flowering occurring rapidly in daylengths less than about 14 h and with increasing delay at longer photoperiods. With the exception of a longer thermal time duration from flower formation to harvest maturity in the case of Kompolti, the 2 cultivars had similar values for the key phenology parameters. Respectively, for Futura and Kompolti: BVP was 383˚Cd and 390˚Cd, MOP was 14 h and 13.8 h, PS was 266˚Cd/h and 252˚Cd/h, and FDP was 76.8˚Cd and 80.2˚Cd.

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Pearl millet landraces from Rajasthan, India, yield signi®cantly less than improved cultivars under optimum growing conditions, but not under stressed conditions. To successfully develop a simulation model for pearl millet, capable of capturing such genotype  environment (G  E) interactions for grain yield, we need to understand the causes of the observed yield interaction. The aim of this paper is to quantify the key parameters that determine the accumulation and partitioning of biomass: the light extinction coef®cient, radiation use ef®ciency (RUE), pattern of dry matter allocation to the leaf blades, the determination of grain number, and the rate and duration of dry matter accumulation into individual grains. We used data on improved cultivars and landraces, obtained from both published and unpublished sources collected at ICRISAT, Patancheru, India. Where possible, the effects of cultivar and axis (main shoot vs. tillers) on these parameters were analysed, as previous research suggested that G  E interactions for grain yield are associated with differences in tillering habit. Our results indicated there were no cultivar differences in extinction coef®cient, RUE, and biomass partitioning before anthesis, and differences between axes in biomass partitioning were negligible. This indicates there was no basis for cultivar differences in the potential grain yield. Landraces, however, produced consistently less grain yield for a given rate of dry matter accumulation at anthesis than did improved cultivars. This was caused by a combination of low grain number and small grain size. The latter was predominantly due to a lower grain growth rate, as genotypic differences in the duration of grain ®lling were relatively small. Main shoot and tillers also had a similar duration of grain ®lling. The low grain yield of the landraces was associated with profuse nodal tillering, supporting the hypothesis that grain yield was below the potential yield that could be supported by assimilate availability. We hypothesise this is a survival strategy, which enhances the prospects to escape the effects of stress around anthesis.