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Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM–WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM–WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM–WGP model for heading and physiological maturity dates. However, the CGM–WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM–WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM–WGP modelling and suggest future efforts should focus on calibrating CGM–WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect. [ABSTRACT FROM AUTHOR]

Plants pump large amounts of water, solutes, and organic matter as part of their normal physiological processes. This pumping action can be exploited to improve degraded environments by stabilising, removing, or breaking-down contaminants in the substrates. In addition, soil amendments such as fertilisers and microbes may also be added to enhance the efficacy of the operation. Basic plant physiology sets limits on the capacity of phytoremediation. However, combining this technology with the production of saleable products may render the extra time needed for clean-up relatively unimportant. Phytoremediation is still poorly developed, particularly the phytoextraction of heavy metals. Continual innovation will greatly expand the scope and efficacy of phytoremediation. The greatest potential use for this technology may be in developing countries that have the highest levels of environmental degradation, and scant funds for remediation. Phytoremediation could provide a low-cost and sustainable way to improve local economies. Here a case study is reported on the phytoremediation of a 3.6 ha sawdust pile that was leaching unacceptable amounts of boron (B) into local waterways. High water-use poplars were used to control leaching and potentially remove B from the site via phytoextraction. Selected trees that are coppiced before leaf-fall could be mulched and used to provide 'organic' B on nearby orchards that are deficient in this element. Additional keywords: heavy metals, modelling, lysimeter, phytoextraction, phytomining, phytoremediation., Introduction Phytoremediation is the use of plants to improve degraded environments. This technology has been used for hundreds of years to treat human waste, reduce erosion, and protect water quality. [...]

Irrigation contributes substantially to the water balance and environmental condition of many agriculturally productive catchments. This study focuses on the representation of spatiotemporal variability of irrigation depths in irrigation schedule models. Irrigation variability arises due to differences in farmers' irrigation practices, yet its effects on distributed hydrological predictions used to inform management decisions are currently poorly understood. Using a case study of the Barr Creek catchment in the Murray Darling Basin, Australia, we systematically compare four irrigation schedule models, including uniform versus variable in space, and continuous‐time versus event‐based representations. We evaluate simulated irrigation at hydrological response unit and catchment scales, and demonstrate the impact of irrigation schedules on the simulations of streamflow, evapotranspiration, and potential recharge obtained using the Soil and Water Assessment Tool (SWAT). A new spatially variable event‐based irrigation schedule model is developed. When used to provide irrigation inputs to SWAT, this new model: (i) reduces the over‐estimation of actual evapotranspiration that occurs with spatially uniform continuous‐time irrigation assumptions (biases reduced from ~40% to ~2%) and (ii) better reproduces the fast streamflow response to rainfall events compared to spatially uniform event‐based irrigation assumptions (seasonally adjusted Nash‐Sutcliffe Efficiency improves from 0.15 to 0.56). The stochastic nature of the new model allows representing irrigation schedule uncertainty, which improves the characterization of uncertainty in simulated catchment streamflow and can be used for uncertainty decomposition. More generally, this study highlights the importance of spatiotemporal variability of inputs to distributed hydrological models and the importance of using multivariate response data to test and refine environmental models. Plain Language Summary: In many regions irrigation is the primary water input for agriculture, and affects key productivity aspects such as the yield and quality of crops. From the hydrological perspective, irrigation affects important responses such as surface runoff, potential recharge, and actual evapotranspiration. Understanding and predicting these responses at relevant spatial and temporal scales is critical for efficient management of irrigated landscapes, in particular to improve agricultural productivity and ecosystem sustainability. This study evaluates irrigation schedule models that are used to provide inputs into spatially distributed hydrological models. We compare four of these models in terms of several criteria, including consistency with typical irrigation practices and ability to produce reliable hydrological simulations when fed as inputs into spatially distributed hydrological models. Our results emphasize the importance of representing spatiotemporal variability in irrigation inputs, with the best performance obtained using a new spatially variable event‐based model. The findings provide guidance on the use of irrigation schedule models for hydrological modelling of irrigated catchments, as well as on the broader scientific question of representing variability in environmental processes. Key Points: The representation of spatiotemporal variability of irrigation inputs impacts substantially on distributed hydrological model simulationsNew stochastic irrigation schedule model developed to generate more realistic spatially variable event‐based irrigation time seriesThe new irrigation schedule model leads to improved catchment streamflow simulated using SWAT and enables uncertainty decomposition [ABSTRACT FROM AUTHOR]

Elevated dissolved organic carbon (DOC) has been detected in groundwater beneath irrigated sugarcane on the Burdekin coastal plain of tropical northeast Australia. The maximum value of 82 mg/L is to our knowledge the highest DOC reported for groundwater beneath irrigated cropping systems. More than half of the groundwater sampled in January 2004 (n=46) exhibited DOC concentrations greater than 30 mg/L. DOC was progressively lower in October 2004 and January 2005, with a total decrease greater than 90% indicating varying load(s) to the aquifer. It was hypothesized that the elevated DOC found in this groundwater system is sourced at or near the soil surface and supplied to the aquifer via vertical recharge following above average rainfall. Possible sources of DOC include organic-rich sugar mill by-products applied as fertilizer and/or sugarcane sap released during harvest. CFC-12 vertical flow rates supported the hypothesis that elevated DOC (>40 mg/L) in the groundwater results from recharge events in which annual precipitation exceeds 1500 mm/year (average=960 mm/year). Occurrence of elevated DOC concentrations, absence of electron acceptors (O2 and NO3- ) and both Fe2+ and Mn2+ greater than 1 mg/L in shallow groundwater suggest that the DOC compounds are chemically labile. The consequence of high concentrations of labile DOC may be positive (e.g., denitrification) or negative (e.g., enhanced metal mobility and biofouling), and highlights the need to account for a wider range of water quality parameters when considering the impacts of land use on the ecology of receiving waters and/or suitability of groundwater for irrigated agriculture. [ABSTRACT FROM AUTHOR]

Various benefits of intercropping systems have been reported worldwide. However, fewer modeling studies of these systems have been undertaken. We present key intercropping features and processes in seven intercropping models, highlighting strengths and limitations. Representation of key processes such as resource sharing and plasticity are a challenge in modeling. Very few studies have investigated plasticity, focusing mainly on crop response to light. Therefore, further research is necessary to understand and incorporate these key processes in intercropping models. We selected APSIM to evaluate its ability to simulate the intercropping effects by 1) a theoretical combination of crops and crop densities and (2) a comparison against observed data in three locations in Victoria, Australia. Models for monocultures and intercrop combinations with varying crop density ratios for four crops (barley, canola, fababean and wheat) were calibrated and the model performance was evaluated. Simulated monoculture results were satisfactory but intercropping results were poor. The growth of intercrops was found to depend on interactions between the parameter KL (fraction of plant available water extracted daily) and soil water content whose dynamics in turn depended on the method and amount used to initialize soil water. The resource sharing 'alternate day' approach in the model may not be suitable in water-limited environments because it can lead to excessive advantage of one intercrop over the other. Incorporating intercrop-specific parameters, representation of plant density ratios and revising resource sharing algorithms in the model may improve results. • Plasticity mechanisms in intercrop models remain largely unaccounted for. • Intercrop-specific parameters in models need to be linked to biophysical factors. • APSIM performance for monoculture was satisfactory but poor for intercrops. • The alternate-day resource sharing may not be suitable in long water stress periods. • Better algorithms are needed to accommodate plant density ratios in intercrop models. [ABSTRACT FROM AUTHOR]