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.     

农机跨区作业紧急调配算法适宜性选择

目前农机跨区紧急作业中供需信息不对称,农机部门缺乏科学合理的紧急调配方案,无法在紧急状况下指导农机进行及时有效的调配。针对上述问题,该文研究了农机跨区作业紧急调配模型和算法。首先分析了多机多任务紧急调配过程,建立了以最小化调配成本和损失为目标的紧急调配模型,提出了基于距离最近优先的多机多任务紧急调配算法(shortest-distance first algorithm,SDFA)和基于贡献度最大优先的多机多任务紧急调配算法(max-ability first algorithm,MAFA),前者是搜索当前距离最近的农田和农机,进行优先分配,后者是搜索当前贡献度最大的农田和农机并进行优先分配。采用该文算法对河北省邯郸市2017年的真实数据以及随机生成的农田和农机实例库进行计算与分析可知,当农机数量充足时,算法MAFA的平均调配成本要比SDFA的平均调配成本降低4.34%。当农机不足时,SDFA的平均损失和平均调配成本要比MAFA的平均损失和平均调配成本分别下降了12.79%和4.11%。进一步验证可知,当农田数量为6时,上述2种算法比笔者之前提出的基于非合作博弈紧急调配算法(non-cooperative game algorithm,NCGA)的平均运算性能均提升25%以上,当农田数量为30时,性能均提升41%。该研究可为农机管理部门紧急调配与决策分析提供科学依据。     

基于气温预报的参考作物腾发量预报方法比较

为探索精确预报未来短期ET0的方法,比较了4种基于气温预报ET0预报模型,即Hargreaves-Samani(HS)、Thornthwaite(TH)、简化的Penman-Monteith(PT)及McCloud(MC)模型。收集了西藏林芝站2001年1月1日至2013年12月31日的实测逐日气象数据和2012年6月6日至2013年12月31日逐日对未来7d的气象预报数据,在气温预报精度评价的基础上,采用4种基于温度的参考腾发量计算模型直接进行ET0预报,然后采用率定后的模型进行ET0预报,最后与实测气象数据和FAO-56PM公式计算的ET0值进行比较。结果表明,未率定的4种模型预报误差均较大,其中PT公式精度稍高。经率定后,4种公式的预报精度都有所提高,平均准确率为70%,MAE值HS模型最小,平均为0.57mm/d,其他3个模型为1.27~1.50mm/d;RMSE都在2.0mm/d左右;r值总体仍不高,TH模型平均仅有0.19,其他3种模型在0.6左右。综合来看,PT模型的预报效果稳定性优于其他3个模型。对于林芝地区附近的灌区,无论有无气象观测数据供模型率定,建议采用PT模型进行ET0预报。     

基于电网自动化调度管理的电网安全稳定运行

经济的发展带动了电力行业的发展,这对于电网调度安全管理工作的要求也提升到了一个新的高度。当前电网调度管理逐渐发展成为自动化管理以及智能化管理,这与电网的安全稳定运行有着紧密的联系。文章针对当前电网调度管理问题进行了分析,并就此提出了实用性较强的措施。     

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.     

基于CROPWAT模型对不同典型年冬小麦灌溉制度的研究

利用1951—2010年的气象数据,采用CROPWAT模型对枯水、平水、丰水3个典型降水年冬小麦的灌溉制度进行研究。结果表明,3个不同的典型降水年充分灌溉净灌溉定额分别为353.3、342、296.2mm,灌水次数依次为8、7和6次;4次关键水条件下净灌溉定额分别为229.1、222.2、212.3mm。     

基于GIS和GPS的采棉机跨区作业调度与服务系统设计

精细农业是21世纪农业发展的主要方向,随着农业机械化的深入发展,农机规模作业和跨区域作业已成为趋势。为此,针对兵团棉花机械收获实际,将GPS与GIS技术相结合,设计了一套采棉机实时监控、调度与服务系统。该系统可实现在遥感影像地图上实时显示采棉机的位置,对采棉机作业进行监控,辅助农机管理人员科学有效地对采棉机进行调度和管理,从而提高采棉机的作业效率和机采棉公司的经济效益。     

新疆北部苜蓿耗水规律及灌溉制度研究

通过对苜蓿的需水量、需水时间、灌溉次数等方面的试验对比分析,总结了苜蓿的耗水规律,即在苜蓿生长的幼苗期、分枝期,耗水量依次表现出递增的趋势,开花成熟期则有所下降。同时揭示了苜蓿耗水量与产量的关系,苜蓿产量与需水量之间的关系曲线呈抛物线形式,需水量在较小值时,苜蓿的产量随着需水量的增加而增加,直至到达产量最大值,此后随着需水量的増加产量反而开始下降。结合试验成果和当地荒漠戈壁地的土质结构和性状,初步提出了当地种植苜蓿的灌溉制度,苜蓿收获三茬的灌溉定额为8 040.5m3/hm2,全生育期的灌水次数为15次。     

基于遗传算法的带时间窗的多目标flow-shop调度

结合实际生产,引入机器的空闲时间建立了一个带有时间窗的flow—shop优化调度模型。通过非支配排序来进行群体虚拟适应度值的分配,引入精英解策略来保证算法的收敛性和解的多样性,运用小生境技术来避免局部收敛和早熟,维持种群多样性。通过仿真实验得到模型具有实际意义,算法具有可行性。     

北疆干旱荒漠地区春小麦与苜蓿灌溉制度研究

新疆北部阿勒泰草原的农牧业生产主要是在荒漠瘠薄的戈壁地上开发和发展起来的,作物灌溉制度的确定,对于北疆地区水资源合理开发利用、农田灌溉管理以及自然植被生态恢复等工作有着重要的现实意义。通过对联合国“2817项目”春小麦和苜蓿灌溉试验数据进行分析比较,初步得到达到该地区小麦和苜蓿高产的灌溉制度。结果表明,春小麦应采用“量少次多”的灌溉制度,灌溉定额为433 mm,灌水次数为10次,其中最优灌水定额抽穗前60 mm,其他生育阶段灌水定额为30~45 mm;苜蓿打3茬灌溉定额为849 mm,灌水次数为15次,第1茬的第1水灌水定额在60 mm,第2、3茬的第1水灌水定额为67 mm,其他水灌水定额在40~60 mm。研究显示,春小麦达到高产的处理是土壤含水量为田间持水量的70%;苜蓿达到高产的最优方案是幼苗期、蔓枝延长期和开花成熟期的土壤含水量为田间持水量的65%、45%和45%。