Agriculture Organization Irrigation Research Articles

Water quality impacts of in-stream mine tailings on a headwater tributary of the Rio Pilcomayo, Potosí, Bolivia

Rio Tarapaya, a major tributary of the upper Rio Pilcomayo and a crucial water resource in the arid Southeastern Bolivian highlands, has been impacted by in-stream mine tailings and acid rock drainage over the past five centuries, from historic and ongoing mining at Cerro Rico de Potosí. Identification of the effects of in-stream mine tailings on the waters of Rio Tarapaya is essential to better understand the dynamics of these complex systems and propose successful management strategies. A study was performed on river waters and sediments at six sites in a reach of Rio Tarapaya, beginning at the city limits of Potosí and ending just after the confluence with Rio Cayara, a relatively unimpaired river 7.3 km downstream. Concentrations of dissolved metals (such as Fe, Al, Mn, and As) in the Ca–Mg–HCO3–SO4 dominated stream were generally orders of magnitude greater than Bolivian receiving water body criteria and United Nations Food and Agriculture Organization irrigation and livestock drinking water guidelines. Variability of Ca and Mg in water and sediment was controlled by dissolution of gypsum, dolomite and calcite, which was supported by Spearman correlation coefficients. The primary ionic products of in-stream sulfide mineral weathering (SO42−, Fe, Al, Mn, Zn and As), are the dominant ions of stream water and sediment leachate. Results are consistent with PHREEQC modeling of minerals such as gibbsite, melanterite, goethite, calcite and dolomite. In general, the reactivity of bed material increased and stream water quality decreased with increasing distance from Potosí. Metals and metalloids are spiraling downstream via seasonally driven particle transport, sulfide weathering, and salt dissolution coupled with biotic interactions. The reactivity of the millions of tonnes of in-stream tailings in Rio Tarapaya and persistence of contemporary illicit tailings discharges indicates that the upper Rio Pilcomayo will remain severely degraded for the foreseeable future unless substantial remediation is undertaken.

Multisite evaluation of an improved SWAT irrigation scheduling algorithm for corn (Zea mays L.) production in the U.S. Southern Great Plains

Modeling alternative irrigation strategies can be a cost-effective and time-saving approach to field-based experiments. However, the efficacy of irrigation scheduling algorithms should be verified using field data from multiple locations. In this study, an auto-irrigation algorithm recently developed for Soil and Water Assessment Tool (SWAT) was further evaluated using irrigation data for corn (Zea mays L.) grown at six research sites across the Southern Great Plains. Simulated monthly irrigation, based on the management allowed depletion (MAD) of plant available soil water, was compared to measured data for irrigation applied in accordance with crop water requirement guidelines outlined by the Food and Agriculture Organization Irrigation and Drainage Paper 56. Overall, results indicated the MAD algorithm simulated monthly field-based irrigation amounts well (Nash-Sutcliffe efficiency; NSE > 0.56). Comparisons revealed the MAD algorithm outperformed the plant water demand and soil water content approaches in SWAT, which tended to underestimate and overestimate irrigations, respectively.

Evaluation of single and dual crop coefficients over a drip-irrigated Merlot vineyard (Vitis vinifera L.) using combined measurements of sap flow sensors and an eddy covariance system

Background and Aims Irrigation is one of the most important viticultural practices in semi-arid regions. For this reason, the Food and Agriculture Organization's Irrigation and Drainage Paper No. 56 single and dual crop coefficient approaches were evaluated to estimate actual evapotranspiration (ETa) over a drip-irrigated Merlot vineyard. Methods and Results Vine transpiration was measured using sap flow sensors, and ETa was measured by an eddy covariance system. Soil evaporation was calculated as the difference between ETa and sap flow sensors. Reference evapotranspiration (ETo) was calculated using the Penman–Monteith equation. This information was used to evaluate single (Kc) and dual crop coefficients [Kcb and Ke] approaches during the 2007/08 and 2008/09 grapevine growing seasons. Conclusions Results indicated that vine transpiration is the most important component of ETa for a drip-irrigated Merlot vineyard trained on a vertical shoot-positioned system. In midseason, the observed values of single crop coefficients and Kcb were lower by 11 and 19% than those recommended by the Food and Agriculture Organization's Irrigation and Drainage Paper No. 56 for grapevines, respectively. The application of site-specific Kcb values reduced the error in the estimation of ETa by about 10%. Significance of the Study The study indicates that the use of sap flow sensors could be an alternative method for obtaining site-specific Kcb values for vineyards under field conditions.

Sensitivity of the Food and Agriculture Organization Penman–Monteith Evapotranspiration Estimates to Alternative Procedures for Estimation of Parameters

Reference crop evapotranspiration ( ETo ) is a key variable in procedures established for estimating evapotranspiration rates of agricultural crops. As per internationally accepted procedures outlined in the United Nations Food and Agriculture Organization's Irrigation and Drainage Paper No. 56 (FAO-56), using the Penman–Monteith (PM) combination equation is the recommended approach to computing ETo from ground-based climatological observations. Applying of the PM equation requires converting input climate and site data into a number of parameters, and FAO-56 recommends exact procedures for estimating these parameters. However, a plethora of alternative procedures for estimating parameters exist in literature. As a consequence, it is likely that ambiguous results may be obtained from the FAO-56 PM equation because of the adoption of such alternative (nonrecommended) supporting equations. The purpose of the present study is to evaluate differences that could arise in FAO-56 ETo estimates if nonrecommended ...

Estimating Evaporation from Bare Soil and the Crop Coefficient for the Initial Period Using Common Soils Information

The crop coefficient during the initial period sKc inid varies with wetting frequency, evaporative demand, and water-holding capacity of the upper soil layer. It is possible to develop a semitheoretical integrated function to predict the average Kc ini representing the initial period of a growing season when the soil is mostly bare and that incorporates these three factors. The function is based on a two-stage evaporation function as used in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 (FAO-56) dual crop coefficient method. Parameters in the integrated equation are soil based and can be calculated a priori without field measurements. The procedure can be used to produce graphical figures similar to that introduced in FAO-24 for Kc ini. Similar to FAO-24, the function utilizes the mean time between wetting events and reference evapotranspiration. In this paper, the development of the procedure and figures for Kc ini are described. Comparisons with measured evaporation and Kc ini in southern California indicate relatively good perfor- mance by the function without calibration.

Predicting Daily Net Radiation Using Minimum Climatological Data

Net radiation (\IR\dn\N) is a key variable for computing reference evapotranspiration and is a driving force in many other physical and biological processes. The procedures outlined in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 [FAO56 (reported by Allen et al. in 1998)] for predicting daily \IR\dn\N have been widely used. However, when the paucity of detailed climatological data in the United States and around the world is considered, it appears that there is a need for methods that can predict daily \IR\dn\N with fewer input and computation. The objective of this study was to develop two alternative equations to reduce the input and computation intensity of the FAO56-\IR\dn\N procedures to predict daily Rn and evaluate the performance of these equations in the humid regions of the southeast and two arid regions in the United States. Two equations were developed. The first equation [measured-Rs-based (\IR\Ds-M\N)] requires measured maximum and minimum air temperatures (\IT\dmax\N and \IT\Dmin\N), measured solar radiation (\IR\ds\N), and inverse relative distance from Earth to sun (\Id\dr\N). The second equation [predicted-\IR\ds\N-based (\IR\Ds-P\N)] requires \IT\Dmax\N, \IT\Dmin\N, mean relative humidity (RH\Dmean\N), and predicted \IR\ds\N. The performance of both equations was evaluated in different locations including humid and arid, and coastal and inland regions (Gainesville, Fla.; Miami, Fla.; Tampa, Fla.; Tifton, Ga.; Watkinsville, Ga.; Mobile, Ala.; Logan, Utah; and Bushland, Tex.) in the United States. The daily Rn values predicted by the \IR\Ds-M\N equation were in close agreement with those obtained from the FAO56-\IR\dn\N in all locations and for all years evaluated. In general, the standard error of daily Rn predictions (SEP) were relatively small, ranging from 0.35 to 0.73 MJ m\U-2\N d\U-1\N with coastal regions having lower SEP values. The coefficients of determination were high, ranging from 0.96 for Gainesville to 0.99 for Miami and Tampa. Similar results, with approximately 30% lower SEP values, were obtained when daily predictions were averaged over a three-day period. Comparisons of \IR\Ds-M\N equation and FAO56-Rn predictions with the measured \IR\dn\N values showed that the \IR\Ds-M\N equations’ predictions were as good or better than the FAO56-\IR\dn\N in most cases. The performance of the Rs-P equation was quite good when compared with the measured \IR\dn\N in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily \IR\dn\N predictions than the FAO56-\IR\dn\N procedures. The \IR\Ds-P\N equation was able to explain at least 79% of the variability in \IR\dn\N predictions using only Tmax, Tmin, and RH data for all locations. It was concluded that both proposed equations are simple, reliable, and practical to predict daily \IR\dn\N. The significant advantage of the Rs-P equation is that it can be used to predict daily \IR\dn\N with a reasonable precision when measured \IR\ds\N is not available. This is a significant improvement and contribution for engineers, agronomists, climatologists, and others when working with National Weather Service climatological datasets that only record \IT\Dmax\N and \IT\Dmin\N on a regular basis.

Estimation of FAO Blaney-Criddle b Factor by RBF Network

This technical note presents the application of a Radial Basis Function (RBF) network to estimate the FAO Blaney-Criddle b factor. Tabular b values are given in the United Nations Food and Agriculture Organization Irrigation and Drainage Paper Number 24. The b values obtained by the RBF network are compared to the appropriate b values produced using regression equations. The RBF network predicted b values better than the regression equations. An example is given to illustrate the simplicity and accuracy of the RBF network for b factor estimation.

FAO‐24 Reference Evapotranspiration Factors

Improved equations are presented tor calculating correction factors for the United Nations Food and Agriculture Organization Irrigation and Drainage Paper Number 24 (FAO‐24) grass reference evapotranspiration methods. Equations duplicate tables of correction factors contained in the original FAO‐24 report and simplify development of computer programming code. Correction factors are included in the original FAO‐24 publication to “correct” reference evapotranspiration estimates for varying climatic conditions of windiness, humidity, and sunshine. Equations are developed in this study using regression models where independent values are comprised of various transformations of secondary parameters used in the Food and Agriculture Organization (FAO) tables.