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Models Comparative Study for Estimating Crop Water Requirement and Irrigation Scheduling of Maize in Metekel Zone, Benishangul Gumuz Regional State, Ethiopia
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Irrigation & Drainage Systems Engineering

ISSN: 2168-9768

Open Access

Research - (2021) Volume 10, Issue 3

Models Comparative Study for Estimating Crop Water Requirement and Irrigation Scheduling of Maize in Metekel Zone, Benishangul Gumuz Regional State, Ethiopia

Demeke Tamene1* and Ashebir Haile2
*Correspondence: Demeke Tamene, Ethiopian Institute of Agricultural Research, Pawe Agricultural Research Center, P.O.Box 25, Pawe, Ethiopia, Email:
1Ethiopian Institute of Agricultural Research, Pawe Agricultural Research Center, P.O.Box 25, Pawe, Ethiopia
2Ethiopian Institute of Agricultural Research, Debre Zeit Agricultural Research Center, P.O.Box 32, Debre Zeit, Ethiopia

Received: 05-Jan-2021 Published: 17-Mar-2021
Citation: Demeke Tamene and Ashebir Haile. Models Comparative Study for Estimating Crop Water Requirement and Irrigation Scheduling of Maize in Metekel Zone, Benishangul Gumuz Regional State, Ethiopia. Irrigat Drainage Sys Eng 10 (2021): 259.
Copyright: © 2021 Tamene D. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This study was aimed to compare estimation methods of crop water requirement and irrigation scheduling for major crops using different models and compare the significance of models for adoption at different situations in Metekel zone. Crop water requirement and irrigation scheduling of maize in selected districts of Metekel zone were estimated using CropWat model based on soil, crop and meteorological data and AquaCrop based on soil, crop and meteorological data including Co2, groundwater, field management, and fertility status. Model performance was evaluated using Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF). It is observed that the maximum reference evapotranspiration in the study area was found to be 7.1 mm/day in Guba and minimum reference evapotranspiration was 2.9 mm/day in Bullen district. In all cases, the maximum ETo in all districts was fund to in March and the lowest in August. The maximum ETc of maize was found to be 702.4 mm in Guba district and minimum ETc was found to be 572.6 mm in Bullen district using CropWat but the effective rainfall (Pe) for maize were determined as 185 mm respectively in Wembera district. However, using AquaCrop model the maximum ETc of 565 mm was recorded in Guba but 425 mm was recorded as minimum in Wembera district for irrigated maize in the study area. The study revealed that the irrigation scheduling with a fixed interval criterion for maize 10 days with 12 irrigation events has been determined. Moreover, furrow irrigation with 60% irrigation application efficiency was adjusted during irrigation water applications for all districts. The performance of the irrigation schedule and crop response was evaluated by the analysis results in the simulation using different models. It has been observed that there was a strong relationship and a significant relation between the simulated and observed values for validation. Hence, Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF) showed that AquaCrop model well simulated in all parameters considered. AquaCrop model is the most suitable soil-water-crop-environment management model, so future studies should suggest a focus on addressing deficit irrigation strategy with different field management conditions to improve agricultural water productivity under irrigated agriculture for the study area for major crops.

Keywords

Depilation, Irrigation events, AquaCrop, Fixed interval and Deficit Irrigation.

Introduction

Irrigation implies the application of suitable water to crops in the right amount at the right time. Irrigation scheduling is important for developing best management practices for irrigated areas. There is considerable scope for improving water use efficiency of these crops by proper irrigation scheduling which governed by crop evapotranspiration have suggested that the crop coefficient values need to be derived empirically for each crop based on lysimetric data and local climatic conditions [1].

Maize is the world’s third most important cereal crop after wheat and rice grown primarily for grain and secondly for fodder (Nelson, 2005). Seasonal maize water use varies according to the evaporative demand of the atmosphere, and hence according to climate, time of the season when the crop is grown, the life cycle length of the crop, and water availability. The typical seasonal ET of a cultivar of medium-season length grown in a temperate climate at the latitude of 35o to 40o being around 650 mm.

The demand for water has been the main limiting factor for crop production in much of the world where rainfall is not ample. The ever increase in the human population is stimulating the rise in demand for a large quantity of crop yield. Sustaining this population will require increased production of all crops. There is also a limited amount of arable land and the resources to produce food are becoming scarcer. As population rises, less land will be devoted to agriculture, meaning increased production will have to come from increased yields. In Metekel zone, almost all farmers are poor in water resource management and lack of experience and knowledge about how much and when to irrigate efficiently for irrigation water saving-strategies to tackle the shortage of rainfall and dry spell. This results in waterlogging, soil erosion, accumulation of salt, and loss of irrigation water resources. Therefore, there is a need to improve the water use efficiency to obtain more crop production per drop of water with declining irrigation resources and the uncertainty in the temporal and spatial distribution of rainfall. Among many, one of the mechanisms or strategies to improve crop productivity per unit of water under full irrigation is the employment of the aid of models to fill the gaps during dry spells. It has been reported by different scholars that the crop water requirement and irrigation scheduling determined using CropWat. However, the comparative study using the CropWat and AquaCrop model for the determination of crop water requirement and irrigation scheduling of major crops in the study area hasn’t been done yet to the best of my knowledge [2].

The model simulation is a simplification of the field processes, but it attempts to account for the most important factors that influence the model performance. Determination of crop water requirement and irrigation scheduling will provide information that increases water use efficiency and increase the productivity of maize crops in the study area. However, the performance of models varies from one another based on various factors. Therefore, evaluation and identification of the best model for maximizing the efficiency of water use in crop production are unquestionable. Consequently, sustainable and effective utilization of scarce water resources may promote and contribute to poverty alleviation in the area and enhance food security through maximizing crop production of the farmers. The bjective of this study was to compare and evalauate ETo, crop water requirement and irrigation scheduling for maize using CropWat and AquCrop to improve water productivity for sustainable agricultural production under irrigated agriculture.

Materials and Methods

Description of the study area

The study was conducted in Metekel zone of Benishangul Gumuz Regional State, North-West of Ethiopia. It is the largest zone of the region covering an area of 3,387,817 hectares consisting of seven 7 districts: Wombera, Bullen, Manbuk, Dibate, Mandura, Guba, and Pawe Woreda. The topography of the zone presents undulating hills slightly sloping down to low land Plateaus having varying altitudes from 600- 2800 m.a.s.l. and the annual rainfall of the area is 900-1580 mm. About 80% of the zone is characterized by having a sub-humid and humid tropical climate. Its diverse agro-ecology provides the potential for the cultivation of different crops. Farmers practice a mixed crop-livestock production system. Cereals (maize, sorghum and finger millet) and oilseeds (soybean, sesame, and groundnut) are the most important food grains mainly cultivated in the zone. According to the Ministry of Agriculture (MoA) and Agricultural Transformation Agency, the surrounding of Metekel Zone has a wide climatic range within hot to warm moist lowlands and hot to warm -sub-humid lowlands agroecological zones [3].

The annual minimum and maximum temperature of the study area is 20oC and 35oC respectively. The soil type of the study area is characterized by heavy clay soil with initial available soil moisture depletion level range 111- 129 (mm/meter depth) and total available soil moisture level range 222-259 (mm/meter depth) varying with soil depth. a mean infiltration rate is 70 mm/ day and the bulk density is varying from 1.12-1.31 gm/cm3 across the depth of 1.2 meter. Agricultural activities in the study area dominated by mixed crop-livestock production, which accounts 96.2% of the farmers and the rest 3.8% were involved only in livestock production Figure 1.

irrigation-and-drainage-systems-engineering-Location

Figure 1. Location map of the study area.

Crop Water Requirement

Crop and Irrigation Water Requirements using CropWat Model

CropWat 8.0 computed crop water requirement by feeding the computed monthly ETo values together with rainfall, crop type including cropping calendar together with the required soil characteristics of maize. The Kc for every growth stage was adapted from Allen et al. (1998) and then, ETc was calculated by euation (1). The irrigation requirement was calculated using the equation (2).

ETc = ETo ∗ kc            (1)

NIR = ETc – Pe             (2)

Where, ETc = crop evapotranspiration (mm), ETo = reference evapotranspiration (mm), Kc = crop factor, NIR = net irrigation water requirement (mm), ETc = crop water requirement (crop evapotranspiration) (mm), Pe = effective rainfall (mm).

The amount of water applied during an irrigation event (gross irrigation) is equal to the net irrigation required between irrigation and that needed for efficiencies in the irrigation system. In this study, water was assumed to apply with precise measurements. As a result, there was no run-off and the only loss would be deep percolation and evaporation which are expected to be not much in a deficit irrigation practice. Therefore, a higher value of application efficiency (60%) was adopted.

GIR =NIR / Ea                     (3)

Where, GIR = gross irrigation requirement, NIR = net irrigation water requirement and Εa= water application efficiency=60%.

Crop and Irrigation Water Requirements using AquaCrop Model

Considering groundwater table, as no shallow groundwater table, all stress indicators, waterlogging stress, water shortage stress, air temperature stress, soil salinity stress have been considered as zero and considering no specific field management, net irrigation requirement and crop water requirement for furrow irrigation have been calculated. The simulation period has been adjusted and soil water profile at % of RAW considered as an initial condition with no field observation.

To all test crops, crop evapotranspiration has been calculated by multiplying the reference evapotranspiration (ETo) with the crop transpiration coefficient (KcTr) and a water stress coefficient (Ks) which is 1 when water stress does not induce stomatal closure.

Crop transpiration has been calculated by the concept of the following formula

Tr =Ks* KcTr* ETo                (4)

Where, ETo is the reference evapotranspiration, KcTr is the crop transpiration coefficient, Ks is a water stress coefficient which is 1 when water stress does not induce stomatal closure.

The crop transpiration coefficient KcTr is proportional to the green canopy cover (CC):

KcTr=KcTr, x* Kc CC**

Where, KcTr, x is the crop coefficient for maximum crop transpiration (determined by the characteristics that distinguish the crop with a complete canopy cover from the reference grass), and CC* the canopy cover adjusted for micro-advective effects.

Net irrigation requirement: The depletion (% RAW) below which the soil water content in the root zone may not drop (0 % RAW corresponds to Field Capacity). The total amount of irrigation water required to keep the water content in the soil profile above the specified threshold is the net irrigation water requirement for the period. The net requirement does not consider extra water that has to be applied to the field to account for conveyance losses or the uneven distribution of irrigation water on the field.

Irrigation scheduling

Irrigation Scheduling using CropWat model

Irrigation scheduling was worked out using CropWat 8.0 windows by selecting two scheduling criteria: fixing the interval and adjusting the depth to a constant value for no yield reduction and minimum water loss and the 100% readily available soil moisture depletion.

Irrigation schedules using AquaCrop model

Generation of irrigation schedules using AquaCrop have been computed by specify back to field capacity and fixed net application depth criterion and fixed interval and allowable depletion (% of RAW) time criteria.

By selecting the furrow irrigation method, irrigation events (when to irrigated and how much to irrigate have been specified by considering irrigation water quality for maximum dry yield production and water productivity and minimum labor cost (irrigation event). The electrical conductivity (EC) of the irrigation water was used as an input to irrigation scheduling.

Model Calibration and Simulations

After all, input data encoded - climatic, crop, management, and soil characteristics that described or defined the environment in which the crop was developed. Before the simulation, the simulation phase and the initial conditions at the beginning of the simulation were determined. The user can track changes in the soil water and corresponding changes in the crop development, soil evaporation, transpiration, (ET) rate, biomass production, and yield when running simulation results of the simulation were stored in output files in spreadsheet format to retrieve the data for further processing and analysis. Furthermore, program settings permit the user to change default settings and reset to an individual’s default values once more.

Model Calibration for several crops was presented. shown the model performed well. The observed data set from the non-water stress conditions (that is full 100% ETc irrigation treatment) used for model calibration. The observed crop characteristics namely; time to emergence, time to attain maximum canopy cover, time to flowering, and senescence and physiological maturity (in calendar days) were used. After the calibration process, the model was validated from separated other treatment data except for 100% ETc [4].

Performance Evaluation of Models

The output of a model depends on the principle of the model itself and the accuracy of the input data. Evaluation of model performance should include both statistical criteria and graphical display. A model is a good representation of reality only if it predicts an observable phenomenon with acceptable accuracy and precision [5].

Addicott and Whitmor concluded that any one method of measuring discrepancy between model output and observed data alone might be misleading, but several methods used together could summarize the closeness of a model’s estimates and measurements with the observed values. The following statistics and model performance indicators were used to indicate overall model performance: average deviation, root mean square error (RMSE), relative error, model efficiency [6,7].

Model performance was evaluated using the following statistical parameters: prediction error (Pe), Nash-Sutcliffe efficiency index (E), mean absolute error (MAE), root mean square error normalized (RMSEN).

Prediction error (Pe):image            (6)

Where, Si the is predicted value, Oi is observed value.

Root mean square error normalized (RMSEN)

Because RMSE is expressed in the units of the studied variable, it does not allow model testing under a wide range of metro-climatic conditions (Jacovides and Kontoyiannis,1995). Therefore, RMSE can be normalized using the mean of the observed variable (Oi). The Normalized RMSE expressed in percent, will be calculated Loague and Green. as illustrated in (Equation 7). A model can be considered excellent if NRMSE is smaller than 10%, good if between 10 and 20%, fair if between 20 and 30% and poor if larger than 30 (Ahmed, 2014; Yibrah, 2015).

image             (7)

Where, Si is predicted value, Oi is observed value, and N is the number of observations.

Model efficiency

The robustness of the model was assessed with the model efficiency (ME) (Loague and Green 1991).

image         (8)

Where, Si is predicted value, oi is the observed value, N is a number of observations 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. An ideal value of MF is the unit.

Nash-Sutcliffe efficiency index

The Nash-Sutcliffe coefficient of efficiency coefficient (NSE) determines the relative magnitude of the residual variance compared to the variance of the observations. A plot of observed data versus simulated data is that too fits the 1:1 line indicates a perfect match between the model and the observations. Nash-Sutcliffe was as accurate as of the average of the observed data. A negative NSE occurs when the mean of the observations is a better prediction than the model. (Ahmed, 2014; Yibrah, 2015) The Nash-Sutcliffe coefficient of efficiency coefficient (NSE) calculated as (Equation 9). Nash-Sutcliffe is very commonly used, which means that there are a large number of reported values available in the literature (Moriasi, et.al, 2007). However, like NSE is not very sensitive to systematic over-or underestimations by the model [8].

image             (9)

Where, Si is predicted value, oi is the observed value, N is the number of observations and Mo is the average of the observed values.

Results and Discussion

Climate Characteristics of the Study Area

Long-term climatic data of the study area were analyzed and reference evapotranspiration (ETo) was calculated based on the FAO Penman- Monteith method (Allen et al., 1998) and the results are given in the following figure 1.

As shown in Figure 2, the average ETO value simulated using CropWat in Pawe district was found to be 4.50 mm/day. The maximum value of ETO was found to be 6.60 mm/day in March and the minimum ETO was 3.17mm/day in August. The average ETO value simulated using CropWat in Mandura district was 4.51 mm/day. The average ETO value simulated using aqua crops in Mandura district was 4.13 mm/day.The average ETO value simulated using CropWat in Guba district was found to be 4.79 mm/day. The maximum value of ETO was found to be 6.92 mm/day in March and the minimum ETO was 3.57 mm/day in August.

irrigation-and-drainage-systems-engineering-evapotranspiration

Figure 2. Long term evapotranspiration (ETo) of the study areas (1987-2011).

The average ETO values simulated using CropWat in Bullen district were found to be 3.93mm/day. The maximum values of ETO were 5.47 mm/day in March and the minimum was 2.93 mm/day in August using CropWat.The average ETO value simulated using CropWat in Wembera district was found to be 3.97 mm/day. The maximum value of ETO was found to be 5.51 mm/ day in March and the minimum was 3.05 mm /day in August.

As shown in Figure 3, the average ETO value simulated using aqua crops in Pawe was found to be 4.52 mm/day. The maximum value of ETO was found to be 6.80 mm/day in March and the minimum ETO was 3.2 mm/day in August. The relative difference between average ETo values simulated using CropWat and AquaCrop was found to be small which was 0.02 mm/ day. The climate parameters were collected from the Pawe agricultural research center metrology station that was located at a longitude of 36.050 East, the latitude of 11.150 North, an altitude of 1120 meters above sea level.

irrigation-and-drainage-systems-engineering-Comparison

Figure 3. Comparison of CropWat and AquaCrop daily ETo of the study areas.

The maximum value of ETO in Mandura using AquaCrop, was 6.30 mm/ day in March and the minimum ETO was 3.20 mm/day in August. The relative difference between average ETo values simulated using Cropwat and AquaCrop was found to be 0.38 mm/day. The climate parameters were collected from Mandura district metrology station that was located at a longitude of 36.320 East, the latitude of 11.060 North, an altitude of 1161 meters above sea level.

The average ETO value simulated using AquaCrop was found to be in the Guba district was found to be 4.82 mm/day. The maximum value of ETO was 7.1 mm/day in March and the minimum ETO was 3.6 mm/day in August. The relative difference between average ETo values simulated using CropWat and AquaCrop was found to be small which was 0.03 mm/day. The climate parameters were collected from the Guba district metrology station that was located at a longitude of 35.400 East, the latitude of 11.050 North, an altitude of 977 meters above sea level in the Guba district.

The average ETO values simulated aqua crops in Bullen district were found to be 3.93 mm/day. There was no difference between ETO average values simulated using CropWat and Aqua Crops. The maximum values of ETO using aqua crop the maximum values of ETO was 5.6 mm/day in March and minimum was 2.9mm /day in August, The climate parameters were collected from Bullen district metrology station that was located at the longitude of 36.960 East, the latitude of 10.500 North, an altitude of 1323 meter above sea level.

The average ETO value simulated using aqua crops in the Wembera district was found to be 3.62 mm/day. The maximum values of ETO were 5.2 mm/ day in March and the minimum was 3.10 mm /day in August. The relative difference between average ETo values simulated using CropWat and AquaCrop was found to be 0.35 mm/day. The climate parameters were collected from Debre-zeyite metrology station that was located at a longitude of 36.960 East, the latitude of 10.500 North, and altitude of 1323 meters above sea level in Wembera district.

As General, the maximum reference evapotranspiration in the study area estimated using CropWat was found to be 6.92 mm/day in Guba, and minimum reference evapotranspiration was found to be 2.93 mm/day in Bullen district. The maximum reference evapotranspiration in the study areas simulating using aqua crops was found to be 7.1 mm/day in Guba and minimum reference evapotranspiration was found to be 2.9 mm/day in Bullen district.

Crop and Irrigation Water Requirements of maize in project area

Crop and Irrigation Water Requirements of maize using CropWat model

The crop water requirement (ETc) throughout the growing season was then determined based on equation 8.

As shown in Table 1, Since there was no determined crop coefficient, rooting depth, critical depletion, and yield response factor, so far for this area, the FAO recommended values for growth stages are used to calculate CWR and to made irrigation scheduling. The local planting date of the crops had been used for the computation.

Table 1: Crop characteristics and input data used for CropWat.

Crop characteristics Growing stages Total
Initial Development Mid Late
Kc 0.45   1.2 0.85  
Stages 20 35 40 30 125
Rooting depth 0.3   1    
Critical depletion (fraction) 0.55 0.55   0.8  
Yield response factor 0.4 0.4 1.3 0.5 1.25
Crop height   2 (optional)      

As shown in Table 2, the maximum seasonal irrigation requirement of maize, was found to be 690mm in Guba district and minimum irrigation requirement of 393mm in Wombera district. Relatively height amount of the required water was satisfied by rain that occurred in December, January, February and march in Wembera district since this area is located in height altitude and height rainfall area. Seasonal effective rain (Pe) was185mm respectively in Wembera district. In Abshege Woreda, Gurage Zone, Ethiopia, the Crop water requirement of maize estimated using CROPWAT 8.0 for a window with a growing period of 140 days to maturity would require 423 mm depth of water, while 101 mm would be required as supplementary irrigation depth. The total crop water requirement of maize was 535.60 mm in Tepi, Southwest of Ethiopia [9].

Table 2: Simulated ETc and IR of crops in the study areas using CropWat.

District ETC (mm) ER (mm) IR (mm)
Pawe 680.4 12.4 667.5
Mandura 680.3 15.2 664.3
Guba 702.4 10.3 690.8
Bullen 572.6 21.3 539.9
Wembera 576.5 185 393

Crop and Irrigation Water Requirements of maize using the aquacrop model As shown in Table 3, Some maize characteristics used as input for aqua crop model have been taken with minimum calibration from the reference manual developed with contributions of the AquaCrop network in January 2009, the experiments used for calibration and validation crops including maize were generally conducted under high levels of management, with the control treatments aimed at production levels close to the maximum potential achievable in that location. Most of the pepper characteristics have been taken with minimum calibration. Most of the onion characteristics have been also taken with minimum calibration [10].

Table 3: Crop characteristics & input parameters used as input for AquaCrop.

Crop characteristics     Discriptions Input Parameter
Initial canopy Initial canopy cover (%) 0.29
Canopy size seedling (c.m2/plant) 6.5
Plant density (plants/ha) 44,444
Development Maximum canopy cover (%) 90
From day 1 after sowing to emergence (day) 8
Maximum canopy(day) 50
Senescence (day) 95
Maturity (day) 125
Flowering
and yield formation (root/tuber formation)
Length building up of harvest index (day) 52
Duration of flowering (day) 13
From day 1 after sowing to flowering(day), yield formation 68
Root deepening Maximum effective root depth (m) 1.2
From day 1 after sowing to maximum root depth (day) 97
Average root zone expansion (cm/day) 1.1

As shown in Table 4, the maximum net requirement of maize was found to be 673 mm in Pawe district and the minimum net irrigation requirement was found to be 309 mm in Wembera district.

Table 4: Simulated NIR, WP, and DY of maize in the study areas using AquaCrop.

Parameters Districts
Pawe Mandura Guba Bullen Wombera
NIR (mm) 673.1 569 618 548.8 309
ETC (mm) 593.9 502.1 565 484.6 425
DY (ton/ha) 11.349 12.013 12.013 11.738 12.167
WP (kg/m3) 1.97 2.47 2.18 2.51 2.98
P (%) 23 23 21 26 32
ETo (mm) 678.4 570.8 705.3 565.5 467.8
Rain (mm) 12.3 15 12.5 23.5 196.7

Irrigation Scheduling of maize under different districts

Irrigation scheduling of maize using CropWat model

To carry out irrigation scheduling for selected crops using CropWat model has different options. These are irritating at fixed intervals per stage time, irrigate at 100% critical depletion and the refill soil to 100% field capacity depth criteria. However, based on the research evidence and field data available in the study area irrigate at fixed interval per stage time criteria was used. Irrigation efficiency of 60% was selected since main irrigation application methods for the area is surface irrigation especially furrow irrigation.

As shown in Table 5, irrigation scheduling of maize in Pawe using fixed interval (10 days) per stage time criteria and refill soil to field capacity depth criteria had 12 irrigation events. The total gross and net irrigation requirements were 1123.7 mm and 674.2 mm respectively with a yield reduction of 0.0%. As shown in Table 6, irrigation scheduling of maize in Mandura using the fixed interval (10 days) per stage time criteria and refill soil to field capacity depth criteria had 12 irrigation event. The total gross and net irrigation requirements were found to be 1112.4 mm and 667.4 mm respectively with yield reduction of 0.1% (Table 7).

Table 5: Irrigation scheduling of maize in Pawe using irrigate at a fixed interval.

Date Stage NIR
(mm)
GIR
(mm)
Date Stage NIR
(mm)
GIR
(mm)
10 December Initial 40.2 67 8 February Mid 65.6 109.3
20 December Initial 28.3 47.2 18 February Mid 67.7 112.8
30 December Dev 35.4 59 28 February Mid 70.8 118
9 January Dev 46.1 76.8 10 March End 74.8 124.7
19 January Dev 58.6 97.7 20 March End 71.8 119.6
29 January Mid 64.8 108 30 March End 59.5 99.1

Table 6: Irrigation scheduling of maize in Mandura using irrigate at a fixed interval.

Date Stage NIR
(mm)
GIR
(mm)
Date Stage NIR
(mm)
GIR
(mm)
10 December Initial 33.6 56 8 February Mid 65.2 108.6
20 December Initial 26.6 44.3 18 February Mid 67.2 112
30 December Dev 33.6 56 28 February Mid 70.3 117.2
9 January Dev 44.1 73.4 10 March End 75.2 125.3
19 January Dev 56.6 94.3 20 March End 72.6 120.9
29 January Mid 63.7 106.2 30 March End 58.9 98.1

Table 7: Irrigation scheduling of maize in Guba using irrigate at fixed interval.

Date Stage NIR
(mm)
GIR
(mm)
Date Stage NIR
(mm)
GIR
(mm)
10 December Initial 29.7 49.5 8 February Mid 63.8 106.3
20 December Initial 27.9 46.6 18 February Mid 65.8 109.7
30 December Dev 34.2 57.1 28 February Mid 68.5 114.2
9 January Dev 43.8 73 10 March End 72.7 121.1
19 January Dev 54.4 90.6 20 March End 74.2 123.7
29 January Mid 61.9 103.1 30 March End 63.6 105.9

Irrigation scheduling of maize in Guba using the fixed interval (10 days) per stage time criteria and refill soil to field capacity depth criteria had 12 irrigation events and had the total gross and net irrigation requirement of 1100.7 mm and 660.4 mm respectively as shown in table 23.

The yield reduction was high (4.4%) since soil texture of Guba district was sandy as shown in table 7, that need irrigation schedule using short irrigation intervals and small amount of water. So irrigation interval less than 10 days can be use by considering labor cost to reduce yield reduction.

As indicated in Table 8, irrigation scheduling of maize in Bullen using the interval (10 days) per stage time criteria and refill soil to field capacity depth criteria had 12 irrigation events and had the total gross and net irrigation requirements of 927.2 mm and 556.3 mm respectively with no yield reduction.

Table 8: Irrigation scheduling of maize in Bullen using irrigate at a fixed interval.

Date Stage NIR
(mm)
GIR
(mm)
Date Stage NIR
(mm)
GIR
(mm)
10 December Initial 32.4 53.9 8 February Mid 52.3 87.1
20 December Initial 24.1 40.2 18 February Mid 53.6 89.3
30 December Dev 29.5 49.1 28 February Mid 56.7 94.4
9 January Dev 37.4 62.4 10 March End 62.1 103.5
19 January Dev 47.2 78.6 20 March End 60.8 101.4
29 January Mid 51.9 86.5 30 March End 48.4 80.7

As shown in Table 9, Irrigation scheduling of maize in Wembera using the fixed interval (10 days) per stage time criteria and refill soil to field capacity depth criteria had 12 irrigation event and had the total gross and net irrigation requirement of 655.8 mm and 393.5 mm respectively with no yield reduction.

Table 9: Irrigation scheduling of maize in Wembera using irrigate at a fixed interval.

Date Stage NIR
(mm)
GIR
(mm)
Date Stage NIR
(mm)
GIR
(mm)
10 December Initial 26 43.4 8 February Mid 37.2 62.1
20 December Initial 13.6 22.7 18 February Mid 37.5 62.5
30 December Dev 18 30.1 28 February Mid 41.6 69.3
9 January Dev 26 43.3 10 March End 47.6 79.3
19 January Dev 36.9 61.5 20 March End 47.2 78.6
29 January Mid 39.4 65.6 30 March End 38.4 64.1

Research conducted in Vertisol in Metekel Zone, North-West of Ethiopia during the summer seasonal (January first to May fifth) indicated that CWR, IR, NIR and GIR requirements of maize with total growth stages of 125 days were found to be 502 mm,486.8 mm 478.5 mm and 651.1 mm respectively and relatively high yield was recorded using irrigating at fixed interval 14 days per stage time criteria and refill soil to field capacity depth criteria.

Irrigation Scheduling of maize using the AquaCrop model.

Generating irrigation schedules is a practical mode for planning or evaluating a potential irrigation strategy. In this mode, AquaCrop will generate at run time irrigations according to the specified time and a depth criterion.

As shown in Table 10, to generate irrigation scheduling of maize, a fixed interval of 10 days’ time criterion and refill soil to field capacity depth criteria which had 12 irrigation events. The simulation indicated CWR of 655.1, 552.3, 567.5, 534.8 and 337 mm, 11.643, 11.858,11.803, 11.635, and 11.736 t/ha of maize can be produced in Pawe, Mandura, Guba, Bullen, and Wembera respectively. In Bushland the study that was conducted in 1989 shows that crop water requirement of maize simulated using AquaCrop was 598.0 mm in areas where measured crop water requirement of maize was 625.0 mm and in 1990 crop water requirement of maize simulated using AquaCrop was 730.8 mm in areas where the measured value was 778.3 mm. During the ‘driest’ year, seasonal (March to mid-September) rainfall (138 mm) and ETo (682 mm) resulted in irrigation needs of onion in were found to be 286 mm and 360 mm for the sandy and sandy loam soils, respectively.

Table 10: Irrigation scheduling of maize in the study area at a fixed interval.

Irrigation event DAP NAD (mm) ECW (ds/m)
Pawe Mandura Guba Bullen Wembera
1 10December 27.6 43.2 32.8 46.2 38.2 0.4
2 20 December 25.5 32.2 21.7 35.3 21.2 0.4
3 30 December 25.2 29.4 25.2 36.3 18.9 0.4
4 9 January 46.3 43.9 43.8 46.7 23.8 0.4
5 19 January 54.4 49.5 56.6 50.9 26.9 0.4
6 29 January 55.8 52.6 60.2 52.1 30.3 0.4
7 8 February 55.8 54.7 61.6 52.2 30.4 0.4
8 18 February 56.7 56.7 62.9 52.8 31.1 0.4
9 28 February 57.5 58.7 64.5 53.8 30.5 0.4
10 10 March 61.8 60.6 66.9 56.5 36.3 0.4
11 20 March 51.9 46.6 51 41.7 32.1 0.4
12 30 March 26.6 24.1 20.3 19.3 17.5 0.4
IR (mm) 655.1 552.3 567.5 534.8 337  
Rain (mm) 12.3 15 12.5 23.5 196.7  
ETO (mm) 678.4 570.8 705.3 565.5 467.8  
DY (T/ha) 11.883 11.858 11.803 11.635 11.736  
Wp (kg. /m3) 2.21 2.65 2.43 2.69 2.94  

Performance Evaluation of Models

Considering the districts as a number of observations RMSE values of maize when simulating crop water requirement was fund to be 133.5. Considering the number of irrigation events as a number of observations, the magnitude of root means square errors when simulating irrigation scheduling for maize in each irrigation event was found to be 4.09, 4.39, 4.26, 5.17, 3.12 in Pawe, Mandura Guba Bullen, and Wembera respectively annex tanble 1 and 2.

Considering the districts as a number of observations RMSEN values of maize when simulating crop water requirement were found to be 20.74% and lied between 20% and 30 % and the simulation was reasonable. The magnitude of all RMSEN values of maize when simulating irrigation scheduling for maize in each irrigation events were found to 7.18%, 7.88%, 7.74%, 9.08%, 9.13% in Pawe, Mandura, Guba, Bullen, and Wembera respectively and all values lied than 10%, so the simulation is excellent in each district annexed table 2. The simulation is considered excellent if RMSEN is less than 10%; it is good if it comes between 10% and 20%; reasonable when it comes between 20% and 30%, and poor when it is greater than 30%.

Nash-Sutcliffe efficiency index (NSE) values of maize, when simulating crop water requirements was found to be 0.98 closed to one, which means the model simulation was in the acceptable range. The relative magnitude of the residual variance compared to the variance of the observations was small. The magnitude of NSE when simulating irrigation scheduling for Maize in each irrigation event was found to 0.1,0.12, 0.16, -0.44, -0.08 in Pawe, Mandura, Guba, Bullen, and Wembera respectively as annexed in table 1 and 2. All vales were close to one and the simulation was accurate.

The magnitude of model efficiency (MF) values when simulating irrigation scheduling for maize in each irrigation event was found to 0.1,0.12, 0.16, -0.44, -0.08 in Pawe, Mandura, Guba, Bullen,and Wembera respectively. The negative value of Model efficiency indicates overestimation. And positive values indicate underestimation. Ideally, model efficiency (MF) will be zero. The model efficiency of maize when simulating crop water requirements was 0.98. When Pe, approaches zero, they represent positive indicators of model performance and used to evaluate the model prediction error. Pe used to define the robustness of the model as well as to predict the values. Pe values of maize when simulating total crop water requirements were found to be -0.13, -0.26, -0.19, -0.15, and -0.26 in Pawe, Mandura, Guba, Bullen,and Wembera respectively. But Pe values when simulating irrigation scheduling maize in each irrigation event were found to be -0.2, -0.17, -0.14, -0.2, and -0.17 in Pawe, Mandura Guba Bullen, and Wembera respective and annexed in teble 1 and 2.

Conclusions and Recommendations

This study was aimed to compare estimation methods of crop water requirement and irrigation scheduling for major crops using different models and compare the significance of models for adoption at different situations in Metekel zone. It is observed that the maximum reference evapotranspiration in the study area was found to be 7.1 mm/day in Guba and minimum reference evapotranspiration was 2.9 mm/day in Bullen district. In all cases, the maximum ETo in all districts was fund to in March and the lowest in August. The maximum ETc was found to be 702.4mm respectively in Guba district and minimum ETc was found to be 572.6mm in Bullen district respectively using CropWat but the effective rainfall (Pe) were determined as 185mm in Wembera district. However, using AquaCrop model the maximum ETc was recorded for maize 565 mm in Guba but minimum 425 mm, was recorded in Wembera district. The study revealed that the irrigation scheduling with a fixed interval criterion for maize 10 days with 12 irrigation events has been determined. Moreover, furrow irrigation with 60 % irrigation application efficiency was adjusted during irrigation water applications for all districts. It has been observed that there was a strong relationship and a significant relation between the simulated and observed values for validation. Hence, Normalized Root mean square errors (NRMSE), model by Nash-Sutcliffe efficiency (NSE), Prediction error (Pe), and Model efficiency (MF) showed that the model well simulated in all parameters considered.

AquaCrop model is very useful and well simulate for the study area under different climatic conditions. Therefore, this model is recommended due to its merit that a user friendly, easy for an application, accuracy, and robustness and address the conditions where water is a key limiting factor for crop production, climate change, and different field management options to enhance water productivity. Scheduling irrigation water using the AquaCrop model is found to improve water productivity. It is thus advisable to use the AquaCrop model in to the development action at scale through developing appropriate pakcages and extension quidelines.It is recommended that farmers and end-users should adopt fixed irrigation intervals for irrigated maize in the study area to save water, time, labor, and energy during irrigation water application.

References

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