Summer cover crop impacts on soil percolation and nitrogen leaching from a winter corn field

The impacts of a leguminous summer cover crop (sunn hemp; Crotalaria juncea) on nitrogen leaching from a corn (Zea mays L.) field was evaluated by direct measurements of soil water content and nitrogen balance components, complemented by direct and inverse modeling as an exploratory tool to better understand water flow and nitrogen balances in the soil. Water and nitrogen inputs and outputs were measured during winter corn production in an experimental field located in the south Miami-Dade basin in southern Florida (USA). Data from the last two seasons (2001-2002 and 2002-2003) of a 4-year study are presented. The field was divided into six 0.13 ha plots. One-half of the plots were rotated with sunn hemp (CC plots) during the summer while the remaining plots were kept fallow (NC plots). Sweet corn management was uniform on all plots and followed grower recommended practices. A numerical model (WAVE) for describing water and agrochemical movement in the soil was used to simulate water and nitrogen balances in both types of plots during the corn seasons. The hydrodynamic component of WAVE was calibrated with soil water data collected continuously at three depths, which resulted in accurate soil water content predictions (coefficients of efficiency of 0.85 and 0.91 for CC and NC plots, respectively). Measured components of the nitrogen balance (corn yields, estimated nitrogen uptake, and soil organic nitrogen) were used to positively assess the quality of the nitrogen simulation results. Results of the modeled water balance indicate that using sunn hemp as a cover crop improved the soil physical conditions (increase in soil water retention) and subsequently enhanced actual crop evapotranspiration and reduced soil drainage. However, nitrogen simulation results suggest that, although corn nitrogen uptake and yields were slightly higher in the CC plots than in the NC plots, there were net increases of soil N content that resulted in increased N leaching to the shallow aquifer. Therefore, the use of sunn hemp as cover crop should be coupled with reductions in N fertilizer applied to the winter crop to account for the net increase in soil N content.     

Evaluating a multi-level subsurface drainage system for improved drainage water quality

This paper describes a multi-level drainage system, designed to improve drainage water quality. Results are presented from a field scale land reclamation experiment implemented in the Murrumbidgee Irrigation Area of New South Wales, Australia. A traditional single level drainage system and a multi-level drainage system were compared in the experiment in an irrigated field setting. The single level drainage system consisted of 1.8 m deep drains at 20 m spacing. This configuration is typical of subsurface drainage system design used in the area. The multi-level drainage system consisted of shallow closely spaced drains (3.3 m spacing at 0.75 m depth) underlain by deeper widely spaced drains (20 m spacing at 1.8 m depth). Data on drainage flows and salinity, water table regime and soil salinity were collected over a 2-year period.     

Application of the relevance vector machine to canal flow prediction in the Sevier River Basin

This work addresses management of water for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, it employs a data-driven approach to developing canal flow prediction models using the relevance vector machine (RVM), a probabilistic kernel-based learning machine. A search is performed across model attributes including input set, kernel scale parameter and model update scheme for models providing superior prediction capability using the RVM. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to 5 days.     

Greenhouse gas implications of water reuse in the Upper Pumpanga River Integrated Irrigation System, Philippines

Enhancing water productivity is often recommended as a “soft option” in addressing the problem of increasing water scarcity. However, improving water productivity, particularly through water reuse, incurs additional investment and may result in increased greenhouse gas (GHG) emissions. In this study, we analysed the water productivity and GHG implications of water reuse through pumping groundwater and creek water, and compare this with gravity-fed canal irrigation in the Upper Pampanga River Integrated Irrigation System (UPRIIS) in the Philippines.Water productivity indicators show that water reuse contributes significantly to water productivity. For example, water productivity with respect to gross inflow (WP_gross) with water reuse (0.19 kg grain/m³) is 21% higher than without water reuse (0.15 kg grain/m³). However, there is a tradeoff between increasing water productivity and water reuse as water reuse increases GHG emissions. The estimated GHG emission from water reuse (pumping irrigation) is 1.47 times higher than without water reuse (gravity-fed canal irrigation). Given increasing concerns about climate change and the need to reduce carbon emissions, we recommend that a higher priority be given to water reuse only in areas where water scarcity is a serious issue.     

Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

As water resources are limited and the demand for agricultural products increases, it becomes increasingly important to use irrigation water optimally. At a farm scale, farmer's have a particularly strong incentive to optimize their irrigation water use when the volume of water available over a season is production limiting. In this situation, a farmer's goal is to maximize farm profit, by adjusting when and where irrigation water is used. However, making the very best decisions about when and where to irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation season. Future rainfall uncertainty further complicates decisions on when and which crops should be subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water availability is limited. Previous optimal scheduling methods generally use stochastic dynamic programming, which requires over-simplistic plant models, limiting their practical usefulness. The CIS method improves on previous methods because it accommodates realistic plant models. Future farm profit (the objective function) is calculated using a time-series simulation model of the farm. Different irrigation management strategies are tested using the farm simulation model. The irrigation strategies are defined by a set of decision variables, and the decision variables are optimized using simulated annealing. The result of this optimization is an irrigation strategy that maximizes the expected future farm profit. This process is repeated several times during the irrigation season using the CIS method, and the optimal irrigation strategy is modified and improved using updated climate and soil moisture information. The ability of the CIS method to produce near optimal decisions was demonstrated by a comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS method can incorporate more realistic farm models than is possible when using stochastic dynamic programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results show that when seasonal water limit is the primary constraint on water availability, the CIS could increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with scheduling irrigation using current state of the art scheduling practice.     

Empirical assessment of water management institutions in northern China

We examine the development of irrigation management in northern China using data from village and household panels. During the past decade, reform-oriented institutions, such as water user associations and contracting, have largely replaced the traditional institution of collective management in village-level irrigation systems. A feature unique to China is that water user associations and contractors are provided with monetary incentives to save water. Water user associations have not yet achieved the broad-based participation of farmers that some advocates consider as a primary goal for forming the associations. Many village leaders serve also as the leaders of water user associations, thus possibly reducing opportunities for receiving operational input and policy direction from farmers. However, we observe improved performance of irrigation systems managed by water user associations, relative to collective management, in terms of maintenance expenditures, the timeliness of water deliveries, and the rates of fee collection. Performance has improved also in systems managed by contractors, although not as substantially as in the case of water user associations.     

1Qy-0.9型悬挂式灌渠清淤机的研制

为实现有护坡林木沟渠的机械化清淤,研制出了1QY—0.9型悬挂式灌渠清淤机。该机通过起土铲将淤土铲起,沿轴向喂入到抛土部件中,由抛土部件中的抛土叶轮沿径向抛向沟渠坡顶。试验结果表明：该机平均清淤深度的变异系数为12.20%～13.80%;抛土距离的变异系数为21.50%～32.70%;生产率在抛土距离为11.75～4.90 m时为123.75～210.88 m³·h^-1。该机各项技术指标达到设计要求,并且较为稳定,工作可靠性较好。与传统清淤机械相比,具有可进沟作业,不受沟渠护坡林木的影响,机动性好的优点,作业质量完全满足黄灌区排灌渠道清淤作业的质量要求。     

Automated measurement of canopy stomatal conductance based on infrared temperature

Decreased water uptake closes stomates, which reduces transpiration and increases leaf temperature. The leaf or canopy temperature has long been used to make an empirical estimate of plant water stress. However, with a few supplemental measurements and application of biophysical principles, infrared measurement of canopy temperature can be used to calculate canopy stomatal conductance (g_C), a physiological variable derived from the energy balance for a plant canopy. Calculation of g_C requires an accurate measurement of canopy temperature and an estimate of plant height, but all of the other measurements are available on automated weather stations. Canopy stomatal conductance provides a field-scale measurement of daily and seasonal stomatal response to prevailing soil water and atmospheric conditions, and facilitates a comparison of models that scale conductance from single leaves (measured with porometers) to canopies. A sensitivity analysis of the input measurements/estimates showed g_C is highly sensitive to small changes in canopy and air temperature, and less sensitive to the other required measurements (relative humidity, net radiation, wind speed, and plant canopy height). The measurement of g_C becomes increasingly sensitive to all of the component factors as the conditions become cloudier, cooler, and more humid. We determined g_C for alfalfa and turfgrass by making the necessary environmental measurements and coupling them with a two-source (plant canopy layer and soil layer) energy balance model. We then compared these g_C values to maximum single leaf values scaled-up to the canopy level (g_CP, defined as potential canopy stomatal conductance herein) for the two crops. For both crops, g_C matched g_CP within approximately 10% after irrigation. The turfgrass g_C measurements were also compared to mean single leaf values measured with a porometer. At mid-day, g_C values were typically about double the single leaf values. Because this approach for determining g_C allows continuous, non-contact measurement, it has considerable potential for coupling with measurements of soil moisture to better understand plant–soil water relations. It also has potential for use in precision drought stress and irrigation scheduling.     

A preliminary assessment of climate change impacts on sugarcane in Swaziland

The spatial and temporal impacts of climate change on irrigation water requirements and yield for sugarcane grown in Swaziland have been assessed, by combining the outputs from a general circulation model (HadCM3), a sugarcane crop growth model and a GIS. The CANEGRO model (embedded with the DSSAT program) was used to simulate the baseline and future cane net annual irrigation water requirements (IR_net) and yield (t ha⁻¹) using a reference site and selected emissions scenario (SRES A2 and B2) for the 2050s (including CO₂-fertilisation effects). The simulated baseline yields were validated against field data from 1980 to 1997. An aridity index was defined and used to correlate agroclimate variability against irrigation need to estimate the baseline and future irrigation water demand (volumetric). To produce a unit weight of sucrose equivalent to current optimum levels of production, future irrigation needs were predicted to increase by 20-22%. With CO₂-fertilisation, the impacts of climate change are offset by higher crop yields, such that IR_net is predicted to increase by 9%. The study showed that with climate change, the current peak capacity of existing irrigation schemes could fail to meet the predicted increases in irrigation demand in nearly 50% of years assuming unconstrained water availability.     

盐碱地枸杞节水高效利用技术集成研究

为了改良并高效利用盐碱地、有效推动平罗盐碱地枸杞产业的发展,开展枸杞沟灌、覆盖黑膜、优化施肥技术集成研究已成为宁夏引黄灌区盐碱地农业可持续发展的迫切需要。研究结果表明:沟灌+覆膜+优化施肥技术集成下,节水、抑盐、抑制杂草效果极为显著,有效节约水分的同时土壤根层含水量充足、盐分下降12.3%,土壤养分得以改善、微生物活性增强,枸杞生长发育及品质良好、产量较农民习惯种植增产68%、获得纯收入增加93%。该技术集成的研究对于指导盐碱地枸杞高产高效发展有重要意义。