Normalized Water Productivity Research Articles

Variance-based global sensitivity analysis and uncertainty quantification of the AquaCrop model parameters for basil (Ocimum basilicum L.) under different nitrogen fertilizer rates

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol’ SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making.

Water Productivity Indices of Onion (Allium cepa) under Drip Irrigation and Mulching in a Semi-Arid Tropical Region of Colombia

Efficient water management is crucial for sustainable agriculture and water resource conservation, particularly in water-scarce regions. This study investigated the effect of different irrigation depths on onion (Allium cepa L.) yield and water use patterns in a semi-arid tropical region of Colombia, using a completely randomized design with five treatments. The treatments ranged from 0–100% of total available water (TAW), T1 (100% of TAW), T2 (80% of TAW), T3 (60% of TAW), T4 (40% of TAW), and T5 (20% of TAW). The experiment was conducted in a greenhouse during one growing season (2022–2023). The normalized water productivity (WP *), irrigation water productivity (IWP), consumptive water productivity, blue water footprint (WFblue), marginal water use efficiency (MWUE), and elasticity of water productivity (EWP), as well as some parameters of quality onion, were determined. The soil in the experimental field was classified as sandy loam; the results show that the WP * of onion is 17.42 g m−2, the water production function shows the maximum production will be achieved at a water application depth of approximately 943 mm, and beyond that, the biomass yield will decrease with additional water application, IWP values for onion ranged from 2.18 to 3.42 kg m−3, the highest Wfblue was in T5 (34.10 m3 t−1), and low Wfblue was T1 (20.95 m3 t−1). In terms of quality, treatment T1 had the most favorable effects on bulb weight, polar diameter, and equatorial diameter, while treatment T5 had the least favorable effects. The study highlights the importance of efficient irrigation on sandy loam soils to maximize yield and water use efficiency. It provides valuable data for evaluating the potential yield benefits of precision irrigation in the study area. Optimizing irrigation depth can significantly improve onion yield and water use efficiency in semi-arid regions.

The Second Derivative of the NDVI Time Series as an Estimator of Fresh Biomass: A Case Study of Eight Forage Associations Monitored via UAS

The estimation of crop yield is a compelling and highly relevant task in the scenario of the challenging climate change we are facing. With this aim, a reinterpretation and a simplification of the Food and Agriculture Organization (FAO) fundamentals are presented to calculate the fresh biomass of forage crops. A normalized difference vegetation index (NDVI) series observed from a multispectral camera on board an unmanned aircraft system (UAS) was the basis for the estimation. Eight fields in Spain of different rainfed intercropping forages were flown over simultaneously, with eight field measurements from February to June 2020. The second derivative applied to the NDVI time series determined the key points of the growing cycle, whereas the NDVI values themselves were integrated and multiplied by a standardized value of the normalized water productivity (WP*). The scalability of the method was tested using two scales of the NDVI values: the point scale (at the precise field measurement location) and the plot scale (mean of 400 m2). The resulting fresh biomass and, therefore, the proposal were validated against a dataset of field-observed benchmarks during the field campaign. The agreement between the estimated and the observed fresh biomass afforded a very good prediction in terms of the determination coefficient (R2, that ranged from 0.17 to 0.85) and the agreement index (AI, that ranged from 0.55 to 0.90), with acceptable estimation errors between 10 and 30%. The best period to estimate fresh biomass was found to be between the second fortnight of April and the first fortnight of May.

Simulation of Crop Productivity for Guinea Grass (Megathyrsus maximus) Using AquaCrop under Different Water Regimes

The perennial herbaceous forage crops’ (PHFC) biomass as bioindustry feedstock or source of nutrients for ruminants is very important from their final utilization point of view. In 2022, the AquaCrop-FAO version 7.0 model has been opened for PHFC. In this context, this study aimed to test for the first time the ability of the AquaCrop-FAO model to simulate canopy cover (CC), total available soil water (TAW), and biomass (B) of Guinea grass (Megathyrsus maximus cv. Agrosavia sabanera) under different water regimes at the Colombian dry Caribbean, South America. The water regimes included L1—irrigation based on 80% field capacity (FC), L2—irrigation based on 60% FC, L3—irrigation based on 50% FC, L4—irrigation based on 40% FC, L5—irrigation based on 20% FC, and L6—rainfed. The AquaCrop model uses the normalized water productivity—WP* (g m−2)—to estimate the attainable rate of crop growth under water limitation. The WP* for Guinea grass was 35.9 ± 0.42 g m−2 with a high coefficient of determination (R2 = 0.94). The model calibration results indicated the simulated CC was good (R2 = 0.84, RMSE = 17.4%, NRMSE = 23.2%, EF = 0.63 and d = 0.91). In addition, cumulative biomass simulations were very good (R2 = 1.0, RMSE = 5.13 t ha−1, NRMSE = 8.0%, EF = 0.93 and d = 0.98), and TAW was good (R2 = 0.85, RMSE = 5.4 mm, NRMSE = 7.0%, EF = 0.56 and d= 0.91). During validation, the CC simulations were moderately good for all water regimes (0.78 < R2 < 0.97; 12.0% < RMSE < 17.5%; 15.9% < NRMSE < 28.0%; 0.47 < EF < 0.87; 0.82 < d < 0.97), the cumulative biomass was very good (0.99 < R2 < 1.0; 0.77 t ha−1 < RMSE < 3.15 t ha−1; 2.5% < NRMSE < 21.9%; 0.92 < EF < 0.99; 0.97 < d < 1.0), and TAW was acceptable (0.70 < R2 < 0.90; 5.8 mm < RMSE < 21.7 mm, 7.6% < NRMSE < 36.7%; 0.15 < EF < 0.58 and 0.79 < d < 0.9). The results of this study provide an important basis for future research, such as estimating biomass production of high-producing grasses in tropical environments, yield effects under scenarios of climate variability, and change based on the presented parameterization and considering a wide range of environments and grazing variations.

Parameterizing the AquaCrop model for potato growth modeling in a semi-arid region

The AquaCrop model was used to model potato growth in a hot semi-arid environment under diverse irrigation managements including water saving irrigation treatments in two growing seasons of 2012 and 2013. The potato cultivars were Agria and Ramos in 2012, and Agria and Sante in 2013. The irrigation treatments were full irrigation and different static and dynamic deficit irrigation managements. The model was calibrated with the data of the cultivar Agria under full irrigation and static deficit irrigation in 2013. Validation was performed using the data from the other potato cultivars and growing seasons, by keeping the conservative crop parameters adjusted in calibration and changing those non-conservative parameters referring to the crop development. Analysis showed that biomass simulation was highly affected by the normalized water productivity (WP*). Unlike the previous studies on modeling with AquaCrop that assumed only one WP* for the whole growing period, we found that it is required to calibrate one WP* for the first half of the growing season until flowering, and then recalibrated WP* for the late growing season. One single WP* assumed for the whole growing season could not simulate the declining potato growth at the late growing season satisfactory, when potatoes were subject to water stress and high air temperature (heat stress). The simulations of the calibrated model showed that the model could simulate total soil water content, volumetric soil water content, and tuber dry yield satisfactory. In addition, the model showed excellent performance in simulation of the total actual evapotranspiration and end-season water productivity. The AquaCrop model was good in simulating the in-season biomass, though it showed fair simulation of biomass in the late growing season. Overall, the performance of the AquaCrop model was reasonable and satisfactory, and the model is a reliable analytical tool for irrigation water management and proper decision making in potato production under variable water resources in the hot semi-arid areas. • AquaCrop has been used for modeling potato growth in a hot and semi-arid region. • AquaCrop had satisfactory performance in simulating soil water content and tuber dry yield. • AquaCrop had excellent performance in simulating end-season actual evapotranspiration and water productivity. • Two WP* values were assumed to differentiate the effects of water and heat stresses on different growing periods. • The calibrated WP* was assigned 13 and 10 g m −2 during the early and late growing season, respectively.

Application of UAV Remote Sensing in Monitoring Water Use Efficiency and Biomass of Cotton Plants Adjacent to Shelterbelt.

Tree shelterbelts are crucial for maintaining the ecological environment of oasis, but they may also compete for soil water with adjacent crops, affecting crop yields. To evaluate the impacts of the shelterbelt on water use efficiency (WUE) and normalized water productivity (WP) of adjacent cotton plants, the biomass (B) and WUE of cotton with different distances from the shelterbelt (0.1H, 0.5H, 1H, 2H, and 3H; average tree height = 15 m [H]) were estimated based on unmanned aerial vehicle (UAV) remote sensing data combined with the FAO crop water response model AquaCrop. Besides, the accuracy and universality of the estimation method were also evaluated. The results showed that the method based on UAV remote sensing data and AquaCrop can accurately estimate the impact range and intensity of shelterbelt on WUE, water consumption, and B of adjacent cotton plants. Fierce water competition between shelterbelt and cotton was detected within 0.1H−1H, and the competitiveness of the shelterbelt was weaker in the plots >1H than in the 0.1H−1H. The B, actual evapotranspiration (Tc), and WUE of cotton at 0.1H decreased by 59.3, 48.8, and 23.6%, respectively, compared with those at 3H, but the cotton plants at 2H and 3H were completely unaffected by the shelterbelt. Besides, the B estimated based on WP (root mean square error [RMSE] = 108 g/m2, d = 0.89) was more accurate than that estimated based on WUE (RMSE = 118 g/m2, d = 0.85). This study clarifies the inter-species competition for soil water between crops and shelterbelts under drip irrigation in oases in China.

Assessing Growth and Water Productivity for Drip-Irrigated Maize under High Plant Density in Arid to Semi-Humid Climates

Determining the water productivity of maize is of great significance for ensuring food security and coping with climate change. In 2018 and 2019, we conducted field trials in arid areas (Changji), semi-arid areas (Qitai) and semi-humid areas (Xinyuan). The hybrid XY335 was selected for the experiment, the planting density was 12.0 × 104 plants ha−1, and five irrigation amounts were set. The results showed that yield, biomass, and transpiration varied substantially and significantly between experimental sites, irrigation and years. Likewise, water use efficiency (WUE) for both biomass (WUEB) and yield (WUEY) were affected by these factors, including a significant interaction. Normalized water productivity (WP*) of maize increased significantly with an increase in irrigation. The WP* for film mulched drip irrigation maize was 37.81 g m−2 d−1; it was varied significantly between sites and irrigation or their interaction. We conclude that WP* differs from the conventional parameter for water productivity but is a useful parameter for assessing the attainable rate of film-mulched drip irrigation maize growth and yield in arid areas, semi-arid areas and semi-humid areas. The parametric AquaCrop model was not accurate in simulating soil water under film mulching. However, it was suitable for the prediction of canopy coverage (CC) for most irrigation treatments.

Comparison of water productivity and crop performance in hydroponic and soil cultivation using AquaCrop software*

AbstractSoil cultivations are widely being replaced by soil‐less methods for growing plants in greenhouses. In the present study, we compared the normalized water productivity (WP*) of lettuce plants under soil and hydroponic cultivation methods. Furthermore, the AquaCrop model was assessed for the simulation of yield and water productivity (WP) of lettuce in the soil and hydroponic cultivation methods. Productivity parameters of normalized water were different for the soil and hydroponic cultivations. The AquaCrop model was applied using 14.3 and 15.8 g m−2, respectively, as obtained for WP* for soil and hydroponic cultivation methods in the first cultivation. Biomass was simulated during the growth period and was compared with the measured data from the first cultivation. The results indicated that the AquaCrop model simulates the performance of lettuce with a remarkable accuracy during the growth period using the calibrated values of WP*. In both cultivations, WP was also measured and simulated for the plants grown with the hydroponic and soil cultivation methods. WP of plants cultured in the hydroponic system was higher than the WP of soil‐grown plants. Canopy cover was measured for both cultivation methods and was simulated by the model. The model simulated the canopy covers remarkably well for both cultivation methods (coefficient of determination R2 > 0.95). Therefore, this model can be considered reliable in investigating the effect of cultivation methods on WP and biomass in lettuce plants.

Capability of a solar energy-driven crop model for simulating water consumption and yield of maize and its comparison with a water-driven crop model

As water scarcity becomes more acute in many parts of the world, crop modeling tools that effectively simulating crop response to deficit irrigation strategies to help investigate management improvement are needed. Identifying the strengths and weaknesses of the crop models with different growth-engines are therefore of great importance. The objective of this study was to investigate the capability and improvements of the new version of a solar energy-driven crop model (DSSAT-CERES-Maize, v4.7.5.0) in simulating water consumption and yield of hybrid seed maize under different soil water conditions, and its comparison with a water-driven crop model (AquaCrop, v4.0). Data obtained from a 4-year (2012–2015) field trial on maize grown under different irrigation treatments at Wuwei, Northwest China, was used for this assessment. These models were calibrated and validated using measured daily evapotranspiration (ET), leaf area index (LAI), aboveground biomass, yield (Y), harvest index (HI) and soil water content (SWC). Daily ET was measured using a combination of an eddy covariance (EC) system, sap flow sensors, and micro-lysimeter cylinders. The ability of DSSAT-CERES-Maize using the two different ET options, i.e., Priestley-Taylor/Ritchie (PT) and FAO-56 Penman-Monteith (PM) was analyzed. The results showed that DSSAT-CERES-Maize with the PT approach had fair agreement with measured daily ET of maize under non-water stress condition (R2=0.85; NRMSE=26.7%), but poor agreement with ET under water stress conditions (R2=0.51; NRMSE=43.8%). DSSAT-CERES-Maize with the PM approach systematically underestimated ET by up to 13% under non-water stress condition, which was mainly attributed to that the maximum static CERES-Maize crop coefficient (EORATIO) was currently hard coded to 1.0. Using the PT or FAO-56 PM approach as ET input in DSSAT-CERES-Maize showed no different effect on final biomass (B) and Y simulation for full irrigation. But for water stress conditions, DSSAT-CERES-Maize with the FAO-56 PM approach simulated B and Y with higher overestimation to the measured data than those simulated using the PT approach. The simulated LAI, biomass and SWC by DSSAT-CERES-Maize using the PT approach generally well followed the trend of the measured values for most irrigation treatments. The model with the PT approach showed acceptable prediction for B and Y of different irrigation treatments across years, with NRMSE of 15.5% and 26.2%, respectively, but the accuracy decreased with an aggravation of water stress. Furthermore, the strengths and weaknesses of DSSAT-CERES-Maize and AquaCrop, and their different cores of growth-engines, i.e. RUE and normalized water productivity (WP*) were carefully discussed. It was concluded that DSSAT-CERES-Maize was a superior estimate of maize yield than was AquaCrop, especially when the climate varied dramatically between years. But DSSAT-CERES-Maize, for the simulation of maize water consumption in an arid region where drought often occurs, was inferior to AquaCrop. These results contribute to recommend the appropriate crop model for specific modeling goals.

A Climate Smartness Index (CSI) Based on Greenhouse Gas Intensity and Water Productivity: Application to Irrigated Rice

Efforts to increase agricultural productivity, adapt to climate change, and reduce the carbon footprint of agriculture are reflected in a growing interest in climate-smart agriculture (CSA). Specific indicators of productivity, adaptation and mitigation are commonly used in support of claims about the climate smartness of practices. However, it is rare that these three objectives can be optimized simultaneously by any one strategy. In evaluating the relative climate smartness of different agricultural practices, plans and policies, there is a need for metrics that can simultaneously represent all three objectives and therefore be used in comparing strategies that have different benefits and trade-offs across this triad of objectives. In this context, a method for developing a Climate Smartness Index (CSI) is presented. The process of developing the index follows four steps: (1) defining system specific climate smartness; (2) selecting relevant indicators; (3) normalizing against reference values from a systematic literature review; and (4) aggregating and weighting. The CSI presented here has been developed for application in a systematic review of rice irrigation strategies and it combines normalized water productivity (WP) and greenhouse gas intensity (GHGI) The CSI was developed for application to data from published field experiments that assessed the impact of water management practices in irrigated rice, focusing on practices heralded as climate-smart strategies, such as Alternate Wetting and Drying (AWD). The analysis shows that the CSI can provide a consistent judgment of the treatments based on the evidence of water efficiency and reduced greenhouse gas intensity reported in such studies. Using a measurable and replicable index supports the aim of generating a reliable quantification of the climate smartness of agricultural practices. The same four step process can be used to build metrics for a broad range of CSA practice, policy and planning.

Newly developed water productivity and harvest index models for maize in an arid region

Simulating yield response to different irrigation scenarios is important for agricultural production, especially in the arid region where agriculture depends heavily on irrigation. To better predict yield under different irrigation scenarios, the variation of normalized water productivity (WP*) over the whole growing period of maize for seed production and the effect of different irrigation treatments on harvest index (HI) were investigated using field experiments from 2012 to 2015 in an arid region of northwest China. Two new non-linear dynamic WP* (WP*KR-L and WP*KR-S) models derived from the Logistic and Sigmoid equations, and four new HI (HIKR-J, HIKR-M, HIKR-B and HIKR-S) models developed on the basis of water deficit multiplicative or additive models at different growth stages were compared with the measurements and the WP* (WP*AC) and HI sub-model (HIAC) in the original AquaCrop model (Version 4.0). In addition, the WP*AC and HIAC models in the original AquaCrop model were replaced by the optimal WP* and HI models to build the AquaCrop-KR model. Then the yield simulated by the AquaCrop-KR model was compared with the measured yield and the yield simulated by the original AquaCrop model. The results show that both WP*KR-L and WP*KR-S models improved the simulation of final biomass, especially for the WP*KR-L model. The tested HI sub-models, namely HIKR-J, HIKR-M, HIKR-B and HIKR-S models had good performance to simulate HI under different irrigation scenarios, and the HIKR-M model was the best among all tested sub-models. When both WP*KR-L and HIKR-M sub-models were embedded into Aquacrop, the performance of the AquaCrop model was improved significantly to simulate yield, especially under severe water stress condition, with R2 increased from 0.496 to 0.653, NRMSE decreased from 26.2% to 16.1% and EF increased from 0.055 to 0.642.

Supporting local farming communities and crop production resilience to climate change through giant reed (Arundo donax L.) cultivation: An Italian case study

Bioenergy crops are well known for their ability to reduce greenhouse gas emissions and increase the soil carbon stock. Although such crops are often held to be in competition with food crops and thus raise the question of current and future food security, at the same time mitigation measures are required to tackle climate change and sustain local farming communities and crop production. However, in some cases the actions envisaged for specific pedo-climatic conditions are not always economically sustainable by farmers. In this frame, energy crops with high environmental adaptability and yields, such as giant reed (Arundo donax L.), may represent an opportunity to improve farm incomes, making marginal areas not suitable for food production once again productive. In so doing, three of the 17 Sustainable Development Goals (SDGs) of the United Nations would be met, namely SDG 2 on food security and sustainable agriculture, SDG 7 on reliable, sustainable and modern energy, and SDG 13 on action to combat climate change and its impacts.In this work, the response of giant reed in the marginal areas of an agricultural district of southern Italy (Destra Sele) and expected farm incomes under climate change (2021–2050) are evaluated. The normalized water productivity index of giant reed was determined (WP; 30.1gm−2) by means of a SWAP agro-hydrological model, calibrated and validated on two years of a long-term field experiment. The model was used to estimate giant reed response (biomass yield) in marginal areas under climate change, and economic evaluation was performed to determine expected farm incomes (woodchips and chopped forage). The results show that woodchip production represents the most profitable option for farmers, yielding a gross margin 50% lower than ordinary high-input maize cultivation across the study area.

Assessment of Nitrogen Leaching of Cropping Pattern by Soil Nitrogen Balance Equation (Case Study: Varamin Irrigation and Drainage Network)

Nitrogen is often one of the most important limiting factors for biomass production. Usually few soils have proper amount of nitrogen, so it is usually added by fertilizers. In cropping systems, nitrogen fertilization practices can provide a sufficient nitrogen supply for plants to achieve the potential yield. However, to ensure reaching to this potential yield, farmers often apply more nitrogen fertilizers than the required nitrogen for achieving maximum yield. Nitrogen fertilizer should be given to soil according to nitrogen content of soil, water and crop nitrogen demand during the growth periods. Especially in the lands that apply wastewater and groundwater for irrigation and irrigation sources have large quantities of nitrogen, therefore we Should reduce the amount of fertilizer used in agricultural lands, In other words for planning the plant fertilizer demand, nitrogen balance in soil should be considered. Nitrogen overuse increases the risk of nitrogen leaching to groundwater, contaminating groundwater and threatening the human health. In recent years, critical plant nitrogen concentration equation is used to determine crop nitrogen demand during the different growth stages of plants. The purpose of this study is to determine the nitrogen demand of Varamin network’s cropping pattern by using the critical plant nitrogen concentration equation and comparing the result with the amount of nitrogen fertilizer commonly applied in the study area. In this study, monthly biomass production for growing period was determined based on normalized water productivity index and plant canopy development. Coefficient of critical nitrogen concentration equation for each plant was determined by previous researches. The result of this study showed that for barley, the amount of nitrogen applied in Varamin network is equal to nitrogen demand of the cropsand for wheat, maize and tomato are 25%, 61% and 18%, respectively, higher than the amount obtained from critical plant nitrogen concentration equation.also according to the results of soil Nitrogen balance in lands covered by AU canal of Varamin network, 707 ton of nitrogen entered to soil and groundwater by leaching and we should considered appropriate solutions to reduce leaching, such as using high-yielding crops that remove a significant amount of N in the harvested portion, synchronizing fertilizer application with crop demand, conjunctive use of wastewater and groundwater with proper nitrogen concentration and etc.

Assessment of AquaCrop model for simulating growth and yield of saffron (Crocus sativus L.)

AquaCrop is among the crop growth models that simulates the effect of water stress on yield and biomass of field crops. AquaCrop model has been parameterized for the majority of field crops; however, it still lacks the parameterization of the saffron as a perennial crop. Since saffron growth lasts for several years, therefore, simulating saffron yield and biomass is much more complex than other crops. In this study, the AquaCrop model was calibrated and validated based on the dataset of a 6-year experiment in the semi-arid area of Iran. The data consisted of different combinations of irrigation managements including full irrigation, different levels of deficit irrigation, and rainfed (no irrigation). Primary analysis revealed that unlike former studies on annual crops, it was needed to define a set of normalized water productivity and harvest index for each year of experiment. Therefore a single crop file was defined for each year of simulation. This was due to the fact that saffron yield of each year is a function of corms growth in the preceding year. Results showed that the model could simulate soil water content well in different soil depth with overall normalized root mean square error (NRMSE)=14%. The model also simulates the potential evapotranspiration fairly with NRMSE=27%. However, the model could simulate the saffron yield satisfactorily with overall NRMSE=11%. In addition the NRMSE=25% was obtained for biomass simulation pooled over the six years. Results suggested that the AquaCrop may be used for simulating the saffron yield for the whole growing seasons, while it was more accurate for simulating biomass for the earlier years and lost its precision in the last years of growth. Finally, it was concluded that the AquaCrop could be also used for simulating perennial crops satisfactorily provided that the model parameterized carefully and calibrated well.

Cotton Water Productivity and Growth Parameters in the Humid Southeast: Experimentation and Modeling

Abstract. A global challenge for the coming decades will be increasing food and fiber production with less water. This can be partially achieved by increasing water productivity (WP) or crop water use efficiency (WUE), i.e., yield or biomass produced per unit water used. Cotton ( L.) is a major rainfed and irrigated crop in the U.S. and worldwide. While there is abundant information on cotton water use and yield relationships in drier regions, relevant field data and modeling in humid regions is not well developed. Robust crop models help complement field experimentation and predict the impact of irrigation management on production. The Food and Agriculture Organization (FAO) of the United Nations has developed a crop model (AquaCrop) that has been parameterized for a number of crops including cotton, but not in a humid region. The objective of this study was twofold: (1) to quantify cotton water use and in-season biomass and growth parameters, and (2) to parameterize and validate AquaCrop to simulate cotton water use and productivity in the humid Southeast U.S., where water conservation and sustainability are emerging issues. Irrigation experiments were conducted from 2009 to 2011 under field conditions as well as under a newly installed automated rainout shelter at a Clemson University research site near Blackville, South Carolina. Extensive season-long soil, crop, and climate data were collected under irrigation regimes ranging from rainfed (i.e., no irrigation) to fully irrigated (i.e., meeting 100% of crop water needs). Season-long data collected in 2009 and 2011 under different irrigation regimes were used to parameterize the model. Cotton seasonal water use ranged from 320 mm at 33% irrigation to 718 mm at full irrigation, corresponding to average seed cotton yields of 2134 to 3682 kg ha -1 . Water use efficiency in the three years ranged from 0.48 to 0.71 kg of seed cotton per m 3 of water applied (irrigation and rainfall). Normalized water productivity for the local climate was nearly the same during the three years, averaging 12.5 g m -2 . Model parameterization was less demanding than expected, requiring adjustments of a few model parameters. Using the parameterized model with independent field data from 2010, the model simulated canopy cover, soil water content, aboveground dry biomass, and yield values that were highly correlated with measured values, with coefficients of determination (R 2 ) greater than 0.66. Seasonal crop water use was predicted within 5% for most treatments but underpredicted by as much as 18% for the 100% treatment at the shelter and for all irrigation treatments in 2010. The underprediction of crop water use corresponded to values of deep percolation simulated by AquaCrop. Field measurements were inadequate to verify deep percolation, but such measurements are suggested for future model investigations. Results of this study suggest that a properly parameterized AquaCrop model provides a useful tool to study yield response to water stress and evaluate the effects of drought and alternate management practices on productivity.

AquaCrop: FAO's crop water productivity and yield response model

With the help of relatively few conservative crop parameters, AquaCrop simulates final crop yield in four steps that are easy to understand, which makes the modeling approach transparent. The steps consist in the simulation of development of the green crop canopy cover, crop transpiration, above-ground biomass, and final crop yield. Temperature and water stresses directly affect one or more of the above processes. Nutrient deficiencies and salinity effects are simulated indirectly by moderating canopy cover development over the season, and by reducing crop transpiration and the normalized water productivity. The effect of CO2 concentration on biomass is simulated by altering the normalized water productivity. The model requires a relatively small number of explicit parameter values and mostly intuitive input variables. The paper describes the essence of AquaCrop Version 4.0, applications and parameterization of crops, crop responses to elevated CO2 concentration, soil fertility and salinity, and further model developments.

Assessment of crop growth and water productivity for five C3 species in semi-arid Inner Mongolia

Water availability is a key biophysical factor determining agricultural production potential. The FAO crop water response model AquaCrop was developed to estimate crop production under water limiting conditions. This model uses the normalized water productivity, WP* (gm−2d−1), to estimate the attainable rate of crop growth under water limitation. In this study we assessed the value and the consistency of WP* under the semi-arid growing conditions of Inner Mongolia. Field experiments on productivity and water use of oats (Avena sativa), sunflower (Helianthus annuus), vetch (Vicia sativa), faba bean (Vicia faba) and potato (Solanum tuberosum) were conducted in 2009, 2010 and 2011. Yield, biomass, evaporation and transpiration varied substantially and significantly between crop species and years. Likewise, water use efficiency (WUE) was affected by these factors, including a significant interaction. WUE (units of yield/unit water) varied between years: from 1.1 to 1.3kgm−3 in oats, from 1.5 to 2.6kgm−3 in sunflower, from 0.5 to 1.1kgm−3 in the two legumes and from 3.1 to 4.4kgm−3 in potato (DM yields). Normalized water productivity did not vary significantly between crop species and years or their interaction, showing an overall value of 14.0±0.32gm−2d−1 in an overarching regression pooling all the data. We conclude that WP* differs from the conventional parameter for water productivity, but is a useful parameter for assessing attainable rate of crop growth and yield in the agro-pastoral ecotone of Inner Mongolia.

Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity

Continuous measurements of CO 2 and water vapor exchanges made in three cropping systems (irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation) in eastern Nebraska, USA during 6 years are discussed. Close coupling between seasonal distributions of gross primary production (GPP) and evapotranspiration (ET) were observed in each growing season. Mean growing season totals of GPP in irrigated maize and soybean were 1738 ± 114 and 996 ± 69 g C m −2, respectively (±standard deviation). Corresponding mean values of growing season ET totals were 545 ± 27 and 454 ± 23 mm, respectively. Irrigation affected GPP and ET similarly, both growing season totals were about 10% higher than those of corresponding rainfed crops. Maize, under both irrigated and rainfed conditions, fixed 74% more carbon than soybean while using only 12–20% more water. The green leaf area index (LAI) explained substantial portions (91% for maize and 90% for soybean) of the variability in GPP PAR (GPP over a narrow range of incident photosynthetically active radiation) and in ET/ET o (71% for maize and 75% for soybean, ET o is the reference evapotranspiration). Water productivity (WP or water use efficiency) is defined here as the ratio of cumulative GPP or above-ground biomass and ET (photosynthetic water productivity = ∑GPP/∑ET and biomass water productivity = above-ground biomass/∑ET). When normalized by ET o, the photosynthetic water productivity ( W P E T o ) was 18.4 ± 1.5 g C m −2 for maize and 12.0 ± 1.0 g C m −2 for soybean. When normalized by ET o, the biomass water productivity ( W P E T o ) was 27.5 ± 2.3 g DM m −2 for maize and 14.1 ± 3.1 g DM m −2 for soybean. Comparisons of these results, among different years of measurement and management practices (continuous vs rotation cropping, irrigated vs rainfed) in this study and those from other locations, indicated the conservative nature of normalized water productivity, as also pointed out by previous investigators.

AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize

The first crop chosen to parameterize and test the new FAO AquaCrop model is maize (Zea mays L.). Working mainly with data sets from 6 yr of maize field experiments at Davis, CA, plus another 4 yr of Davis maize canopy data, a set of conservative (nearly constant) parameters of AquaCrop, presumably applicable to widely different conditions and not specific to a given crop cultivar, was evaluated by test simulations, and used to simulate the 6 yr of Davis data. The treatment variable was irrigation—withholding water after planting continuously, only up to tasseling, from tasseling onward, or intermittently, and with full irrigation (FI) as the control. From year to year, plant density (7–11.9 plants m−2), planting date (14 May−15 June), cultivar (a total of four), and atmospheric evaporative demand varied. The conservative parameters included: canopy growth and canopy decline coefficient (CDC); crop coefficient for transpiration (Tr) at full canopy; normalized water productivity for biomass (WP∗); soil water depletion thresholds for the inhibition leaf growth and of stomatal conductance, and for the acceleration of canopy senescence; reference harvest index (HIo); and coefficients for adjusting harvest index (HI) in relation to inhibition of leaf growth and of stomatal conductance. With all 19 parameters held constant, AquaCrop simulated the final aboveground biomass within 10% of the measured value for at least 8 of the 13 treatments (6 yr of experiments) and also the grain yield for at least five of the cases. In at least four of the cases, the simulated results were within 5% of the measured for biomass as well as for grain yield. The largest deviation between the simulated and measured values was 22% for biomass, and 24% for grain yield. Importantly, the simulated pattern of canopy progression and biomass accumulation over time were close to those measured, with Willmott's index of agreement (d) for 11 of the 13 cases being ≥0.98 for canopy cover (CC), and ≥0.97 for biomass. Accelerated senescence of canopy due to water stress, however, proved to be difficult to simulate accurately; of the six cases, the index of agreement for the worst one was 0.957 for canopy and 0.915 for biomass. Possible reasons for the discrepancies between the simulated and measured results include simplifications in the model and inaccuracies in measurements. The usefulness of AquaCrop with well‐calibrated conservative parameters in assessing water use efficiency (WUE) of a crops under different conditions and in devising strategies to improve WUE is discussed.