A protocol to build soil descriptions for APSIM simulations.

Soil processes have a major impact on agroecosystems, controlling water and nutrient cycling, regulating plant growth and losses to the wider environment. Process-based agroecosystem simulation models generally encompass detailed descriptions of the soil, including a wide number of parameters that can be daunting to users with a limited soil science background. In this work we review and present an abridged description of the models used to simulate soil processes in the APSIM (Agricultural Production Systems sIMulator) framework. Such a resource is needed because this information is currently spread over multiple publications and some elements have become outdated. We list and briefly describe the parameters, and establish a protocol with guidelines, for building a soil description for APSIM. This protocol will promote consistency, enhancing the quality of the science done employing APSIM, and provide an easier pathway for new users. This compilation should also be of relevance to users of other models that require detailed soil information.•This paper presents a brief description of the models for simulating soil processes in the APSIM model.•The method stablishes guidelines to define the parameters for building a soil description for APSIM.

Early vigour in wheat: Could it lead to more severe terminal drought stress under elevated atmospheric [CO2 ] and semi-arid conditions?

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO2 concentrations (e[CO2 ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO2 Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO2 ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO2 ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO2 ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

Soil Phosphorus Modeling for Modern Agriculture Requires Balance of Science and Practicality: A Perspective.

The use of phosphorus (P) fertilizers in arable crop and pastoral systems is expected to change as modern agriculture is challenged to produce more food with fewer inputs. Agricultural systems models offer a dual purpose to support and integrate recent scientific advances and to identify strategies for farmers to improve nutrient efficiency. However, compared with nitrogen and carbon, advances in P modeling have been less successful. We assessed the potential opportunity of P modeling to increase P efficiency for modern agriculture and identified the current challenges associated with modeling P dynamics at the field scale. Three major constraints were (i) a paucity of detailed field datasets to model strategies aimed at increasing P use efficiency, (ii) a limited ability to predict P cycling and availability under the local effects of climate change, and (iii) a restricted ability to match measured soil P fractions to conceptual and modelable pools in soils with different mineral properties. To improve P modeling success, modelers will need to walk a tightrope to balance the roles of assisting detailed empirical research and providing practical land management solutions. We conclude that a framework for interdisciplinary collaboration is needed to acquire suitable datasets, continually assess the need for model adjustment, and provide flexibility for progression of scientific theory. Such an approach is likely to advance P management for increased P use efficiency.

Nitrogen Cycling from Increased Soil Organic Carbon Contributes Both Positively and Negatively to Ecosystem Services in Wheat Agro-Ecosystems.

Soil organic carbon (SOC) is an important and manageable property of soils that impacts on multiple ecosystem services through its effect on soil processes such as nitrogen (N) cycling and soil physical properties. There is considerable interest in increasing SOC concentration in agro-ecosystems worldwide. In some agro-ecosystems, increased SOC has been found to enhance the provision of ecosystem services such as the provision of food. However, increased SOC may increase the environmental footprint of some agro-ecosystems, for example by increasing nitrous oxide emissions. Given this uncertainty, progress is needed in quantifying the impact of increased SOC concentration on agro-ecosystems. Increased SOC concentration affects both N cycling and soil physical properties (i.e., water holding capacity). Thus, the aim of this study was to quantify the contribution, both positive and negative, of increased SOC concentration on ecosystem services provided by wheat agro-ecosystems. We used the Agricultural Production Systems sIMulator (APSIM) to represent the effect of increased SOC concentration on N cycling and soil physical properties, and used model outputs as proxies for multiple ecosystem services from wheat production agro-ecosystems at seven locations around the world. Under increased SOC, we found that N cycling had a larger effect on a range of ecosystem services (food provision, filtering of N, and nitrous oxide regulation) than soil physical properties. We predicted that food provision in these agro-ecosystems could be significantly increased by increased SOC concentration when N supply is limiting. Conversely, we predicted no significant benefit to food production from increasing SOC when soil N supply (from fertiliser and soil N stocks) is not limiting. The effect of increasing SOC on N cycling also led to significantly higher nitrous oxide emissions, although the relative increase was small. We also found that N losses via deep drainage were minimally affected by increased SOC in the dryland agro-ecosystems studied, but increased in the irrigated agro-ecosystem. Therefore, we show that under increased SOC concentration, N cycling contributes both positively and negatively to ecosystem services depending on supply, while the effects on soil physical properties are negligible.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment.

The response of wheat crops to elevated CO2 (eCO2 ) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha-1 and 1600 to 3900 kg ha-1 , respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 µmol mol-1 CO2 ) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO2 , increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY-CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO2 . However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2 , water and temperature is required to resolve these model discrepancies.

Integration of molecular and physiological models to explain time of anthesis in wheat.

BACKGROUND AND AIMS: A model to predict anthesis time of a wheat plant from environmental and genetic information requires integration of current concepts in physiological and molecular biology. This paper describes the structure of an integrated model and quantifies its response mechanisms. METHODS: Literature was reviewed to formulate the components of the model. Detailed re-analysis of physiological observations are utilized from a previous publication by the second two authors. In this approach measurements of leaf number and leaf and primordia appearance of near isogenic lines of spring and winter wheat grown for different durations in different temperature and photoperiod conditions are used to quantify mechanisms and parameters to predict time of anthesis. KEY RESULTS: The model predicts the time of anthesis from the length of sequential phases: 1, embryo development; 2, dormant; 3, imbibed/emerging; 4, vegetative; 5, early reproductive; 6, pseudo-stem extension; and 7, ear development. Phase 4 ends with vernalization saturation (VS), Phase 5 with terminal spikelet (TS) and Phase 6 with flag leaf ligule appearance (FL). The durations of Phases 4 and 5 are linked to the expression of Vrn genes and are calculated in relation to change in Haun stage (HS) to account for the effects of temperature per se. Vrn1 must be expressed to sufficient levels for VS to occur. Vrn1 expression occurs at a base rate of 0·08/HS in winter 'Batten' and 0·17/HS in spring 'Batten' during Phases 1, 3 and 4. Low temperatures promote expression of Vrn1 and accelerate progress toward VS. Our hypothesis is that a repressor, Vrn4, must first be downregulated for this to occur. Rates of Vrn4 downregulation and Vrn1 upregulation have the same exponential response to temperature, but Vrn4 is quickly upregulated again at high temperatures, meaning short exposure to low temperature has no impact on the time of VS. VS occurs when Vrn1 reaches a relative expression of 0·76 and Vrn3 expression begins. However, Vrn2 represses Vrn3 expression so Vrn1 must be further upregulated to repress Vrn2 and enable Vrn3 expression. As a result, the target for Vrn1 to trigger VS was 0·76 in 8-h photoperiods (Pp) and increased at 0·026/HS under 16-h Pp as levels of Vrn2 increased. This provides a mechanism to model short-day vernalization. Vrn3 is expressed in Phase 5 (following VS), and apparent rates of Vrn3 expression increased from 0·15/HS at 8-h Pp to 0·33/HS at 16-h Pp. The final number of leaves is calculated as a function of the HS at which TS occurred (TS(HS)): 2·86 + 1·1 × TS(HS). The duration of Phase 6 is then dependent on the number of leaves left to emerge and how quickly they emerge. CONCLUSIONS: The analysis integrates molecular biology and crop physiology concepts into a model framework that links different developmental genes to quantitative predictions of wheat anthesis time in different field situations.

Scope for improved eco-efficiency varies among diverse cropping systems.

Global food security requires eco-efficient agriculture to produce the required food and fiber products concomitant with ecologically efficient use of resources. This eco-efficiency concept is used to diagnose the state of agricultural production in China (irrigated wheat-maize double-cropping systems), Zimbabwe (rainfed maize systems), and Australia (rainfed wheat systems). More than 3,000 surveyed crop yields in these three countries were compared against simulated grain yields at farmer-specified levels of nitrogen (N) input. Many Australian commercial wheat farmers are both close to existing production frontiers and gain little prospective return from increasing their N input. Significant losses of N from their systems, either as nitrous oxide emissions or as nitrate leached from the soil profile, are infrequent and at low intensities relative to their level of grain production. These Australian farmers operate close to eco-efficient frontiers in regard to N, and so innovations in technologies and practices are essential to increasing their production without added economic or environmental risks. In contrast, many Chinese farmers can reduce N input without sacrificing production through more efficient use of their fertilizer input. In fact, there are real prospects for the double-cropping systems on the North China Plain to achieve both production increases and reduced environmental risks. Zimbabwean farmers have the opportunity for significant production increases by both improving their technical efficiency and increasing their level of input; however, doing so will require improved management expertise and greater access to institutional support for addressing the higher risks. This paper shows that pathways for achieving improved eco-efficiency will differ among diverse cropping systems.