Determination of the potential evapotranspiration and crop coefficient for saffron using a water-balance lysimeter

Agriculture is the major consumer of water and it is possible to decrease water consumption in this sector by proper irrigation scheduling. Irrigation scheduling is based on crop water requirements. Saffron is an important crop in Iran. The main purpose of this study was to determine the potential evapotranspiration and crop coefficient for saffron using single and dual crop coefficients, in Badjgah region, College of Agriculture, Shiraz University, Shiraz, Iran. Three water-balance lysimeters were used for this experiment in a two-year study. Total saffron potential evapotranspiration values were 523 and 640 mm in the first and second growing seasons, respectively. The maximum evapotranspiration rates for saffron were 4.5 and 6.1 mm d^?1 in the first and second growing seasons, respectively. Based on the results of this study, different saffron growing stages for evapotranspiration were 30, 40, 70 and 60 days. Crop coefficient (K _c) values for the initial, mid- and late-season growth stages were 0.41–0.45, 0.93–1.05 and 0.29–0.31 in both years, respectively. Basal crop coefficient (K _cb) values for the initial, mid- and late-season growth stages were 0.15–0.16, 0.41–0.65 and 0.15–0.17 in both years, respectively.     

石羊河流域气候变化对参考作物蒸发蒸腾量的影响

根据甘肃省气象局石羊河流域的6个气象站近50年的观测资料，应用1998年FAO最新推荐的Penman-Monteith公式计算了50年各月参考作物蒸发蒸腾量ET₀，分析了ET₀的月际变化和年际变化特征，除武威与肃南站ET₀呈逐年显著减少趋势外，其他各站的ET₀值均表现为逐年增加趋势，各个站ET₀ 20世纪90年代较80年代均有明显增加，说明气候变化对ET₀的影响较大；并分析了平均气温、平均最高气温、年日照时数、平均风速、平均相对湿度、年降水量、年蒸发量、海拔高度与ET₀的相关性，各站ET₀与平均相对湿度相关性最好；石羊河流域ET₀空间变化也较大，从山区到绿洲平原ET₀多年平均值呈递增趋势。     

Evaluation of pan coefficient equations for estimating reference crop evapotranspiration in the arid region

Reference evapotranspiration (ET₀) can be estimated on basis of pan evaporation data (E_pan), whose measurements have the advantage of low cost, simplicity of the measuring equipment, simple data interpretation and application as well as suitability for locations with limited availability of meteorological data. E_pan values were converted to ET₀ using the pan evaporation coefficient (K_pan). In this study, seven common K_pan equations were evaluated for prediction of ET₀ in the growing season (April to October) in arid region of Iran. The Cuenca approach was best suited compared to the standard FAO Penman–Monteith method (FAO-56 PM).     

遗传算法率定参照作物腾发量相关参数的研究

用气象资料计算参照作物腾发量(ET0)的方法需要各种气象(候)和物理参数,净辐射是其中的重要数据之一,而专业测量净辐射的设备在农业气象站里很少安装。为解决计算ET0时缺少太阳净辐射(Rn)测量值这一实际问题,该文采用浑善达克沙地东南缘南沙梁草甸草原区气象站观测的气象资料,用遗传算法模型对联合国粮农组织56号文本(FAO56)推荐值(as和bs)进行率定,计算了对应夏半年(4—9月)和冬半年(1—3月和10—12月)的太阳净辐射和参照作物腾发量,并将率定前后的模拟太阳辐射进行对比分析,用残差估计指数法对该方法模拟的参照作物腾发量模拟精度进行了分析。结果表明:在缺少太阳净辐射测量值的地区,采用FAO56参数(as和bs)推荐值与遗传算法模型率定参数(as和bs)相比,净辐射年内变化趋势一致,采用率定后参数计算的净辐射相对更不稳定,波动更大,但能有效提高参照作物腾发量计算精度。误差较大的模拟值均出现在降雨日前后,降雨虽然并未直接出现在Penman-Monteith公式中,但是降雨必然会对湿度和温度等气象条件造成一定影响,而as和bs是受湿度等因素影响而变化的,其深层次的原因有待进一步分析。     

西北旱区石羊河流域作物耗水点面尺度转化方法的研究

基于DEM与GIS空间分析功能研究了石羊河流域主要农作物春小麦需水量ET_c的时空分异规律。根据8个气象站近50年气象资料,应用1998年FAO推荐的Penman-Monteith公式计算参考作物蒸发蒸腾量ET₀,由收集到的春小麦需水量试验资料获得多年平均作物系数K_c。近50年来流域上游的古浪、天祝春小麦全生育期ET_c呈微弱的增加趋势,中游的凉州区表现出极显著的减少趋势,其他站减少趋势不显著。确立了ET_c与海拔高度、纬度、坡向的多元回归关系,借助Arcview3.3、ArcGIS9.0与Visual Basic6.0软件实现了春小麦ET_c的空间尺度转换,并分析了石羊河流域25%、50%、75%三个不同水文年春小麦ET_c的空间变异情况。石羊河流域春小麦ET_c由山区向绿洲平原递增,多年平均值为270～591 mm。估计值与计算值相差在11.1%以内。     

利用小蒸发皿观测资料确定参考作物蒸散量方法研究

参考作物蒸散量是土壤－植被－大气系统水分能量平衡模型的重要参数,如何准确获得将直接影响模型应用和最终模拟预测精度。该文利用分布于黄土高原地区65个气象站1971～2000年的气象资料,以FAO推荐的Penman-Monteith方法确定的参考作物蒸散量为标准,提出了根据平均相对湿度与风速为变量确定由20 cm小蒸发皿观测的水面蒸发量计算参考作物蒸散量的系数Kp。结果表明：由蒸发皿观测值计算的3 d或更长尺度的ET₀与Penman-Monteith方法计算的ET₀结果一致性很高,在对Kp方程系数进行适当的地域性调整后,由蒸发皿观测值和Kp确定的ET₀与Penman-Monteith方法确定的ET₀结果一致,从而认为在黄土高原地区参考作物蒸散量计算可以应用20 cm蒸发皿系数法。     

Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region

Estimation of reference evapotranspiration (ET_o) is essential for determination of crop water requirements. In this research, Penman–FAO (P-FAO) and Penman–Monteith (PM) equations were calibrated and validated by lysimeter-measured ET_o with six and four weather parameters. Furthermore, two input structures (six and four weather parameters) to artificial neural networks (ANNs) were investigated. Results showed that the accuracy of the PM equation is greater than that of the P-FAO equation. An empirical equation was developed to estimate daily ET_o using mean daily temperature and relative humidity, and sunshine hours. The accuracy of the equation to estimate daily ET_o using smooth weather data is greater than that of an equation using original data. Furthermore, ANNs were able to estimate ET_o properly. The accuracy of ANNs with six inputs is higher than that obtained using the P-FAO equation and is similar to that determined using the PM equation. A decrease in number of inputs to ANNs generally decreased the accuracy of estimation, however, ANNs were able to estimate ET_o properly when wind speed and solar radiation were unavailable. Furthermore, the accuracy of ANNs, with four input parameters is greater than that obtained using the PM equation and is similar to that obtained with P–FAO and the developed empirical equations.     

Short-term forecasting of daily crop evapotranspiration using the ‘Kc-ETo’ approach and public weather forecasts

Short-term forecasting of daily crop evapotranspiration (ET_c) is essential for real-time irrigation management. This study proposed a methodology to forecast short-term daily ET_c using the ‘K_c-ET_o’ approach and public weather forecasts. Daily reference evapotranspiration (ET_o) forecasts were obtained using a locally calibrated version of the Hargreaves-Samani (HS) model and temperature forecasts, while the crop coefficient (K_c) was estimated from observed daily ET_o and ET_c. The methodology was evaluated by comparing the daily ET_c forecasts with measured ET_c values from a field irrigation experiment during 2012–2014 in Yongkang Irrigation Experimental Station, China. The overall average of the statistical indices was in the range of 0.96–1.27 mm d^?1 for the mean absolute error (MAE), 1.53–2.55 mm d^?1 for the mean square error (MSE), 1.77–2.30 mm d^?1 for the normalized mean square error (NMSE), 27.5–29.4% for the mean relative error (MRE), 0.71–0.44 for the correlation coefficient (R) and 0.46–0.05 for the mean square error skill score (MSESS). Sources of error werewere K_c estion, temperature forecasts and HS model that does not consider wind speed and humidity, and.the largesourceof error is K_c determination, which suggested that care should be taken when forecasting ET_c with estimated K_c values in the study area.     

中国参考作物腾发量时空变化特性分析

分析参考作物腾发量的时空变化特征,有助于了解中国农业及生态需水的分布与演变规律。基于全国范围200多个气象站测站逐日气象观测资料,应用FAO-Penman-Monteith公式,计算得出各站历年逐日参照作物腾发量ET₀。利用GIS的空间分析功能,采用反距离空间插值方法得到全国参考腾发量的分布图,统计分析了不同分区不同时段ET₀的变化情况。结果表明：西北河西走廊地区和南方岭南地区的参考作物腾发量较大,最大值超过1500 mm。而东北黑龙江一带和四川盆地附近,参考作物腾发量较小,在600～700 mm之间。此外,夏季ET₀的分布特征决定了全年ET₀的分布特征。选取4个代表气象站,对其ET₀的历年变化及其与气象因素的关系进行了分析。分析表明,受风速减小和气温增加的共同影响,干旱地区、半干旱地区和半湿润地区的参考作物腾发量呈现减少趋势,湿润地区则相对稳定。     

蒸渗仪测定滴灌枣树不同时间尺度下腾发强度特征

为精确测定、准确模拟阿克苏地区滴灌枣树腾发过程,基于大型称重式蒸渗仪测定枣树全生育期逐时及逐日腾发强度（ET）,利用水量平衡方程、PM公式及经典统计原理,分析不同时间尺度下叶面积指数（LAI）、气象因素[温度（I）、风速（V）、净辐射（R_n）]、表层土壤含水率（W）与枣树腾发强度的相关关系并建立预测模型。结果表明：枣树日内腾发强度呈单峰型变化趋势,夜间变化幅度较小且腾发贡献率低。枣树全生育期逐日腾发强度变化呈先增大后减小的趋势,花期的腾发强度最大,为4.42 mm·d^-1;全生育期腾发总量为640.83 mm,其中花期和果实生长发育期耗水量占比较大,分别为38.61%和32.72%。在小时和日时间尺度上,影响腾发强度的主要因素不完全相同,且影响程度有所差异。综合考虑各影响因素,以萌芽期、花期、果实发育期为基础,分别建立以小时、日尺度下估算腾发强度的经验模型ET₁（h）=0.153+0.004T+0.012V+0.176R_n+0.002W+0.067LAI、ET₂（d）=-3.325+0.081T+0.163R_n+0.069W+2.089LAI,拟合度R²均在0.7以上,以果实发育期与成熟期数据对模型进行检验,纳什效率系数分别达0.63、0.80。经偏相关检验,冠层净辐射（R_n）对两种尺度的腾发强度均影响最显著,因此以枣树全生育期数据量为基础,仅建立冠层净辐射（R_n）与腾发强度的回归模型ET₁（h）=-0.063 3R_n²+0.361 2R_n—0.003 7、ET₂（d）=-0.018 3R_n²+0.684 7R_n–1.642 1,R²分别为0.704 7与0.743 6,可满足缺少数据支撑情况下的腾发过程估算。这些模型明确了阿克苏地区滴灌枣树腾发机制及影响程度,可为水分管理精准化提供计算基础。     

波文比仪与蒸渗仪测定作物蒸发蒸腾量对比

为了更准确地估算作物蒸发蒸腾量,该文结合波文比仪和大型称重式蒸渗仪,对波文比-能量平衡法估算的冬小麦蒸发蒸腾量（ET_b）和蒸渗仪实测的冬小麦蒸发蒸腾量（ET_l）进行了分析研究。结果表明,波文比计算值（ET_b）和蒸渗仪实测值（ET_l）的变化趋势基本一致,相关性比较好。波文比计算值（ET_b）和蒸渗仪实测值（ET_l）的日变化曲线都呈单峰型,早晚小,中午大,夜间多为负值,波文比计算值的日变化比较稳定,蒸渗仪实测值的日变化比较敏感。风速较大时,蒸渗仪实测值日变化随风速的增大而减小的趋势比较明显,波文比计算值日变化受大的风速影响较小;风速较小时,波文比计算值和蒸渗仪实测值的日变化与风速呈很弱的负相关关系。波文比计算值日变化和太阳净辐射日变化的关系比较密切,蒸渗仪实测值日变化和太阳净辐射日变化的关系不是很明显。波文比计算值更能稳定地反映出冬小麦蒸发蒸腾量的日变化规律。     

甘肃地区参考作物蒸散量时空变化研究

区域水土平衡模型的建立通常需要确定计算参考作物蒸散量的模型,这一模型的精确与否,直接影响整体预测模型的最终预报精度.运用FAO-24 Blaney-Criddle法、FAO-24 Radiation法、FAO PPP-17 Penman法及FAO Penman-Monteith(98) 4种方法,对甘肃省1981～2000年33个站点的月参考作物蒸散量进行了计算.对比分析结果表明,AO Penman-Monteith(98)模型的精度与灵敏度均显示了较强的优越性.运用该模型对甘肃省参考作物蒸散量的时空分布特征进行研究表明:甘肃省参考作物蒸散量年内逐月演变曲线呈单峰状;年际蒸散量变化与夏季年际波动变化存在较高一致性;全年参考作物蒸散量分布具有从东南向西北递增的趋势.     

应用自适应神经模糊推理系统(ANFIS)的ET0预测

参照作物腾发量是计算作物需水量和进行灌溉预报的基础要素。该文利用自适应神经模糊推理系统(ANFIS)所具有的直接通过模糊推理实现输入层与输出层之间非线性映射能力，和神经网络的信息存储和学习能力，将其应用于参照作物腾发量预测中。根据相关分析，输入变量选择日照时数和日最高气温；用5年共1827个数据组对系统进行训练，建立了参照作物腾发量预测系统。利用该系统对近年213个数据组进行了实际预测，与Penman-Monteith方法计算结果进行比较，结果相关性良好。     

基于随机样本的神经网络模型估算参考作物腾发量

参考作物腾发量(ET₀)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET₀)的方法众多,为规范ET₀的求法,联合国粮农组织(FAO)推荐采用修改的Penman-Monteith方法。该文指出不需要收集长序列气象资料,而以随机样本建立学习速率和动量因子自适应的BP神经网络模型估算参考作物腾发量(ET₀)的方法,并且与FAO推荐的Penman-Monteith法计算值对比分析,结果表明：利用随机样本建立的的BP神经网络模型可以很好的反映气象因子(最高温度、最低温度、最大湿度、最小湿度、净辐射和风速)与参考作物腾发量(ET₀)的非线性函数映射关系,并且取得了良好的估算效果,给出了国家自然科学基金重点项目研究区内蓝旗试验站2004年的时间尺度为日、十日参考作物腾发量(ET₀)的计算及对比分析过程。     

温室内香蕉树蒸腾量预报的试验研究

在温室内研究了香蕉树蒸腾量和小气候的关系，用5种方法计算了温室内的参考作物腾发量，用20 cm蒸发皿测定温室内的水面蒸发力，并和测定的香蕉树蒸腾量进行对比。试验结果显示香蕉树蒸腾量和蒸发皿水面蒸发量的回归系数(R²)最高，为0.94，而和5种公式计算的参考作物腾发量的回归系数为0.47～0.60，以蒸发皿水面蒸发量计算温室内的作物蒸腾量要优于以参考作物腾发量计算作物蒸腾量的方法。温室内香蕉树的蒸腾量和20 cm蒸发皿蒸发量线性相关，可以此计算温室内作物的蒸腾量。     

Carbon costs of nitrate reduction in broad bean (Vicia faba L.) and pea (Pisum sativum L.) plants

The present study aimed at the assessment of carbon (C) costs for nitrate reduction by measuring the additional CO₂ amounts released from roots of nitrate‐fed plants in comparison with urea‐fed plants. Only roots were suitable for these determinations, because nitrate reduction in illuminated shoots is fed nearly exclusively by reducing equivalents coming directly from photosynthetic processes. Therefore, in a first experiment, the sites of nitrate reduction were determined in nodule‐free broad bean (Vicia faba L.) and pea (Pisum sativum L.) plants grown in pots filled with quartz sand and supplied with KNO₃. The extent of nitrate reduction in the various plant organs was determined by measuring in vitro nitrate reductase activity and in situ ¹⁵NO reduction. Only between 9% and 16% of nitrate were reduced in roots of German pea cultivars, whilst 52% to 65% were reduced in broad bean roots. Therefore, C costs of the process could be determined only in broad bean, using an additional pot experiment. The C costs amounted to about 4.76 mol C (mol N)^–1 or 4 mg C (mg N)^–1, similar to those measured earlier for N₂ fixation. The high proportion of nitrate reduction in shoots of pea plants implies that only very little C is required for this nitrate fraction. This can explain the better root growth of nitrate‐nourished pea plants in comparison with N₂‐fixing organisms, which need C compounds for N₂ reduction in roots. Moreover, a different availability of photosynthates in roots of plant genotypes could explain physiologically the occurrence of “shoot and root reducers” in nature.     

Energy and water balance measurements for water productivity analysis in irrigated mango trees, Northeast Brazil

Crop water parameters, including actual evapotranspiration, transpiration, soil evaporation, crop coefficients, evaporative fractions, aerodynamic resistances, surface resistances and percolation fluxes were estimated in a commercial mango orchard during two growing seasons in Northeast Brazil. The actual evapotranspiration (E_a) was obtained by the eddy covariance (EC) technique, while for the reference evapotranspiration (E₀); the FAO Penman–Monteith equation was applied. The energy balance closure showed a gap of 12%. For water productivity analysis the E_a was then computed with the Bowen ratio determined from the eddy covariance fluxes. The mean accumulated E_a for the two seasons was 1419 mm year⁻¹, which corresponded to a daily average rate of 3.7 mm day⁻¹. The mean values of the crop coefficients based on evapotranspiration (K_c) and based on transpiration (K_cb) were 0.91 and 0.73, respectively. The single layer K_c was fitted with a degree days function. Twenty percent of evapotranspiration originated from direct soil evaporation. The evaporative fraction was 0.83 on average. The average relative water supply was 1.1, revealing that, in general, irrigation water supply was in good harmony with the crop water requirements. The resulting evapotranspiration deficit was 73–95 mm per season only. The mean aerodynamic resistance (r_a) was 37 s m⁻¹ and the bulk surface resistance (r_s) was 135 s m⁻¹. The mean unit yield was 45 tonne ha⁻¹ being equivalent to a crop water productivity of 3.2 kg m⁻³ when based on E_a with an economic counterpart of US$ 3.27 m⁻³. The drawback of this highly productive use of water resources is an unavoidable percolation flux of approximately 300 mm per growing season that is detrimental to the downstream environment and water users.     

ECOWAT—A model for ecosystem evapotranspiration estimation

In recent years, the availability of near real-time and forecast standardized reference evapotranspiration (E₀) has increased dramatically. Use of the E₀ information in conjunction with calibration coefficients that adjust for differences between the vegetation and the reference surface provides a method to greatly improve the estimates of actual evapotranspiration (E_a) from landscapes (or ecosystems). Difficulties in estimating evapotranspiration (ET) of well-watered vegetation in an ecosystem depend on local advection and edge effects, wide variations in radiation resulting from undulating terrain, wind blockage or funnelling, and differences in temperature due to spatial variation in radiation, wind, etc. Estimating the ET of an ecosystem that is water stressed is even further complicated because of stomatal closure and reduced transpiration. The Ecosystem Water Program (ECOWAT) was developed to help improve estimates of E_a of ecosystems by accounting for microclimate, vegetation type, plant density, and water stress. The first step in estimating E_a is to calculate E₀ using monthly climate data from one representative weather station in the study area. Then, local microclimate data are used to determine a standardized reference evapotranspiration for the local microclimate (E_m). The ratio K_m = E_m/E₀ is calculated and applied as a microclimate correction factor to estimate E_m. The product of E_m and a vegetation coefficient (K_v = E_v/E_m) is used to estimate the evapotranspiration of the ecosystem vegetation (E_v) under well-watered conditions with a full-canopy cover within the same microclimate. Next, a coefficient for plant density (K_d), which is based on the percentage ground cover, is used to adjust the full-canopy E_v to the evapotranspiration of a sparse canopy from a well-watered ecosystem (E_w). A stress (K_s) coefficient, which varies between 1.0 with no stress to 0.0 with full stress, is determined as a function of available water in the root zone. The predicted actual ecosystem evapotranspiration (E_p) is estimated as E_p = E_w × K_s. In this paper, we present how the ECOWAT model works and how it performs when the predicted actual evapotranspiration (E_p) is compared with measured actual evapotranspiration (E_a) collected in several Mediterranean ecosystems (three in Italy and two in California) over a number of years. The potential use of ECOWAT in integrated fire danger systems is discussed.     

Evapotranspiration and regional probabilities of soil moisture stress in rainfed crops, southern India

The long-term probability of soil moisture stress in rainfed crops was mapped at 0.5° resolution over the Krishna River basin in southern India (258,948 km²). Measurements of actual evapotranspiration (E_a) from 90 lysimeter experiments at four locations in the basin were used to calibrate a non-linear regression model that predicted the combined crop coefficient (K_cK_s) as a function of the ratio of seasonal precipitation (P) to potential evapotranspiration (E_p). Crops included sorghum, pulses (mung bean, chickpea, soybean, pigeonpea) and oilseeds (safflower and sunflower). E_p was calculated with the Penman–Monteith equation using net radiation derived from two methods: (1) a surface radiation budget calculated from satellite imagery (E_pSRB) and (2) empirical equations that use data from meteorological stations (E_pGBE). The model of K_s as a function P/E_p was combined with a gridded time series of precipitation (0.5° resolution, 1901–2000) and maps of E_pSRB to define the probability distributions of P, P/E_p and K_s for sorghum at each 0.5° cell over the basin. Sorghum, a C4 crop, had higher E_a and K_s values than the C3 plants (oilseeds, pulses) when precipitation was low (P < 1 mm d⁻¹) but lower maximum E_a rates (3.3–4.5 mm d⁻¹) compared with C3 crops (oilseeds and pulses, 4.3–4.9 mm d⁻¹). The crop coefficient under adequate soil moisture (K_c) was higher than the FAO-56 crop coefficients by up to 56% for oilseeds and pulses. The seasonal soil moisture coefficient (K_s) for sorghum ranged from 1.0 under high rainfall (July–October) to 0.45 in dry seasons (November–March), showing strong soil moisture controls on E_a. E_pSRB calculated at the lysimeter stations was 4–20% lower than E_pGBE, with the largest difference in the dry season. K_c derived from E_pSRB was only slightly (2–4%) higher than K_c derived from E_pSRB, because the maximum E_a occurred during the monsoon when the differences between E_pSRB and E_pGBE were small. Approximately 20% of the basin area was expected to experience mild or greater soil moisture stress (K_s < 0.80) during the monsoon cropping season 1 year in every 2 years, while 70% of the basin experienced mild or greater stress 1 year in 10. The maps of soil moisture stress provide the basis for estimating the probability of drought and the benefits of supplemental irrigation.     

对FAO推荐的作物系数计算方法的验证

作物系数是计算作物需水量必不可少的参数。该文介绍了FAO近期推荐的确定作物系数的2种计算方法,一种是比较简单实用的分段单值平均法,另一种是比较复杂但相对准确的双值作物系数法。用河北省雄县灌溉试验站的冬小麦、夏玉米田间土壤水分观测资料计算作物实际腾发量,对上述2种确定作物系数的方法进行了检验。计算的作物系数与雄县试验站的实测结果基本接近,从而初步证明了在缺少实测资料的情况下可以采用FAO推荐的方法确定华北地区主要作物的作物系数