Salt occurs naturally at high levels in the subsoils of most Australian agricultural land. As a result of clearing native vegetation, groundwater tables have risen, mobilising the stored salt and causing adverse impacts on farmland, infrastructure, water resources, and biodiversity. The main action required to prevent groundwater tables from rising is establishment of perennial plants, either herbaceous (pastures or crops) or woody (trees and shrubs). Recent technical and economic research has emphasised how difficult it will be to establish sufficient perennials to get control of groundwater tables. Where watertables are already shallow, the options for farmers are salt-tolerant plants (e.g. saltbush for grazing) or engineering (e.g. deep open drains). The existing options for farm-level salinity management are reviewed, with mixed but somewhat disappointing findings regarding their suitability for addressing salinity. However, there are also a number of good prospects for development of new and better options for plant-based management of salinity, and these are described. 相似文献
Rice–wheat (RW) systems are critical to food security and livelihoods of rural and urban poor in south Asia and China, and to regional economies in southeast Australia. The sustainability of RW systems in south Asia is, however, threatened by yield stagnation or decline, and declining partial factor productivity, soil organic C and water availability. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice and CERES-Wheat in Asia and Australia in relation to their potential application towards increasing resource use efficiency and yield of RW systems.
The performance of the models was evaluated using simulated and observed data on anthesis and maturity dates, in-season LAI and growth, final grain yield and its components, and soil water and N balances from published studies across Asia and Australia, and then by computing the statistical parameters for the major characters. Over the four data sets examined for anthesis and six for maturity dates, CERES-Rice predicted those dates fairly well (normalised RMSE = 4–5%; D-index = 0.94–0.95), but over the 11 sets for grain and 4 for biomass yield, the predictions were more variable (normalised RMSE = 23% for both; D-index 0.90 and 0.76, for grain and biomass, respectively). Model performance was poorer under conditions of low N, water deficit, and low temperatures during the reproductive stages. Over the three data sets examined, CERES-Wheat predicted the anthesis and maturity dates quite well (normalised RMSE = 4–5%; D-index = 0.94–0.99), and over eight sets for grain and two sets for biomass yield the model predicted them also reasonably well (RMSE = 13–16%; D-index = 0.86–0.97). Only one study evaluated the DSSAT RW sequence model with fairly satisfactory predictions of rice and wheat yields over 20 years with adequate N, but not the long-term change in soil organic C and N. Predictions of in-season LAI and crop growth, and soil and water processes were quite limited to investigate the robustness of model processes.
Application of models to evaluate options to increase water and N use efficiency requires the ability to perform well at the margin where deficit stress begins. While both models generally perform satisfactorily under water and N non-limiting conditions, the little evidence available suggests that they do not perform well under resource-limiting situations. We recommend that the models’ key processes under the water and N limiting conditions be further evaluated urgently. The DSSAT sequence model also needs to be further evaluated against observations for a range of locations and management using data from long-term experiments in RW systems. 相似文献
Remotely sensed (RS) data is a major source to obtain spatialdata required for hydrological models. The challenge for thefuture is to obtain besides the more direct observable data(landcover, leaf area index, digital elevation model andevapotranspiration), non-visible data such as soilcharacteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inversemodeling to obtain these non-RS-visible data. For a commandarea in Haryana, India, we applied for the 2000–2001 rabiseason a RS-GIS-combined inverse modeling approach to derivenon-RS-visible data required in the regional application ofhydrological models. A Genetic Algorithm loaded stochasticphysically based soil-water-atmosphere-plant model (SWAP) wasdeveloped for the inverse problem and used in the study. Theresults showed good agreement with the inventoried data suchas soil hydraulic properties, sowing dates, groundwaterdepths, irrigation practices and water quality. The deriveddata could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluateoperational management strategies. 相似文献