The science literature was reviewed to find a suitable crop model capable of simulating crop growth, development and yield of onions. The criteria used for model selection are shown in Appendix B. The required characteristics included daily time step simulation, detailed water fluxes, crop response to available water, actual vs. potential yield and canopy cover, yield production, and input options regarding soil and crop characteristics. Considering these requirements, AquaCrop was identified as being the most suitable model for this study.
AquaCrop was developed adopting the methodology used in “FA Irrigation &
Drainage Paper no. 33, Yield Response to Water” (Doorenbos & Kassam 1979) later also adopted by FAO irrigation scheduling model CROPWAT (M. Smith
68 1992), where crop yield is estimated as a response to crop ET. AquaCrop is an accurate model, that preserves the original theory of FAO Paper no. 33, its simplicity and robustness (Steduto et al. 2012).
Fundamentally, the AquaCrop model consists of:
- Separating ET into its two components soil evaporation E and crop transpiration (Tr)
- Estimating Tr and E separation based on a simple canopy growth and senescence model
- Final yield Y is a function of final biomass (B) and harvest index (HI) - Effects of water stress are separated in four components according to its
effects on canopy growth, canopy senescence, Tr, and HI
The model determines growth on a daily bases according to Equation 3.
𝐵=𝑊𝑃 𝑟 Equation 3
Where B stands for biomass, Tr for transpiration and WP for water productivity.
WP is defined as biomass per cumulative transpiration.
The model’s soil-crop-atmosphere continuum is structured to include the following systems and components:
- The soil: water balance
- The plant: growth, development, yield processes
- Atmosphere: temperatures, rainfall, evaporative demand and CO2
concentration
Figure 10 presents the main components of the continuum soil-plant-atmosphere, and the parameters determining plant response to them. A more detailed description of the model is available in Steduto et al. (2009) and Raes et al. (2009).
69 Figure 10 Chart of AquaCrop indicating the main components of the soil–plant–
atmosphere continuum and the parameters driving phenology, canopy cover, transpiration, biomass production, and final yield [I, irrigation; Tn, minimum air temperature; Tx, Max air temperature; ETo, reference evapotranspiration; E, soil evaporation; Tr, canopy transpiration; gs, stomatal conductance; WP, water productivity; HI, harvest index; CO2, atmospheric carbon dioxide concentration;
(1), (2), (3), (4), different water stress response functions]. Continuous lines indicate direct links between variables and processes. Dashed lines indicate feedbacks. (Steduto et al., 2009)
Figure 11 presents a detailed outline of the operations running within the AquaCrop model (Dirk Raes et al. 2011). Green CC development is determined by the factors: planting density, air temperature (given as growing degree days) and the effects of water and fertility stress. Daily transpiration Tr is given as a function of water deficit, causing stomatal closure, CC, ETo and the adjusted Kc
70 based on crop aging and/or senescence. Once Tr is known, B is estimated after considering negative factors affecting biomass production (temperature and soil fertility stress) and including WP adjusted in relation to CO2 concentration and synthetized crop products. Finally yield is the result of HI, B and positive or negative stress affecting HI.
The fundamentals of the interactions and calculations of AquaCrop simulations are detailed in Raes et al. (2009); and Raes et al.(2011)
Figure 11 Schematic outline of the AquaCrop model operation (Raes et al. 2011) The AquaCcrop package incorporates crop files for certain crops (e.g. maize, wheat, potatoes, and sugar beet). However, no previous study has been conducted with AquaCrop for onion crop growth. Thus crop parameters had to be identified and the model calibrated and validated using experimental data.
71 Data required to parameterize the model consisted of crop and soil measurements comparable with the model’s intermediate results, as well as climatic data and final yield and biomass. Canopy cover, biomass, and soil moisture content were used to assess the adequacy of the model.
In terms of crop definition, firstly, the crop type had to be specified. AquaCrop has specific data requirements and growth cycles depending on whether the crop is a fruit or grain producer, a leafy vegetable, root or a tuber crop. Crop characteristics such as lower and upper temperature limits, water stress tolerance (soil water depletion factors for canopy expansion stoppage, stomatal closure and canopy senescence), and response to fertility have to be established. Figures about root expansion (maximum rooting depth, and root water extraction pattern) and canopy growth (maximum canopy cover, and decrease during decline) were found in the literature and contrasted with experimental data.
Growing stages could be defined according to days or growing degree days (GDD). Time (in days) or GDD (in ⁰C.d) determine plant emergence, achievement of maximum rooting depth, maximum CC, beginning of yield formation, start of senescence, and crop maturity. Crop response to water depends on the Water Productivity (WP), Harvest Index (HI), impacts on HI of growth under water restriction, and impacts caused by stomatal closure.
Planting date and plant density needed to be specified. Had any special land management practices taken place (soil bunds or mulches) it would be necessary to indicate this in the model; however, this was not the case in the present study. Irrigation inputs including application method (drip, sprinkler or surface), the depths applied, and their dates all were inputted into the model
72 4.2.2.1 Model calibration
AquaCrop was parameterized for onions cv Arthur using the data collected by HDC at the experimental station of Broom’s Barns Research Centre.
AquaCrop was calibrated using the climatic and soil conditions recorded under the polytunnels. Six out of the eight irrigation treatments were simulated for 2010, and validated with the eight regimes in 2011, and 2012.
The model parameterization started considering irrigation regime ‘F’, ‘excess’.
This irrigation regime was considered optimal, leading to highest potential canopy cover and maximum yield. Irrigation was the most frequent and no water stress was allowed. The model calibration continued with the other treatments in 2010.
Crop parameters found in the literature were used in this stage. Then the specific conditions of the research station (drilling date, planting density, irrigation dates and weather conditions) were reproduced. Canopy cover, biomass, yield and soil moisture records were used to assess the adequacy of the model.
While calibrating the model, canopy cover, soil moisture and final yield simulated values were compared with the observed records. Crop parameters were adjusted for a better fit.
Irrigation treatments G1 and H1 were not considered. Total water inputs during the season were of 151 and 31 mm for these treatments, because rainfall was blocked by the tunnels, hence these treatments represent a very unlikely situation in the UK.
At irrigation schedule G1, ‘stress’ treatment in 2010, irrigation was applied to return to FC at deficit of 75% of AWC; and after bulb initiation half of the deficit was returned at 75% AWC. At regime H1, no irrigation was applied in 2010.
4.2.2.2 Model validation
After calibration, the model was validated using 2011 and 2012 data (A2, B2, C2, D2, E2, F2, H2, A3, B3, C3, D3, E3, F3, G3, and H3). The model’s
73 adequacy and goodness of fit between observed and simulated yield, canopy cover and soil moisture data, were graphically and statistically assessed.
The root mean square error (RMSE), defined in Equation 4, was used to evaluate the adequacy of the model; it is defined by the following equation:
√ ∑ Equation 4
Where Si is the simulated and Oi the observed values.
This indicator is a measurement of the differences between individual simulated and measured (or observed) values. It gives an estimate of the accuracy of the model, thus, the smaller the value, the better (Loague & Green 1991).
RMSE incorporates the variance of the estimator and its bias (difference between this estimator’s expected value and the true value being estimated).
The units of this indicator are the same as for the considered parameter.
The robustness of the model was assessed with the model efficiency (ME) (Loague & Green 1991), see Equation 5.
∑ ∑ ∑
Equation 5
Where Oi and Si are observed and simulated values, and MO is the average of the observed values. ME acquires values from infinite negative to 1. The closer it gets to 1, the higher the robustness of the model.
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