Investigation of Irrigation Water Requirements for Major Crops Using CROPWAT Model Based on Climate Data

Abstract

Water is one of the most important natural resources and is widely used around the globe for various purposes. In fact, the agricultural sector consumes 70% of the world’s accessible water, of which about 60% is wasted. Thus, it needs to be managed scientifically and efficiently to maximize food production to meet the requirements of an ever-increasing population. There is a lack of information on water requirements of crops and irrigation scheduling concerning the Shaheed Benazirabad district, Pakistan. Thus, the present study was conducted to determine the irrigation water requirements (IWR) and irrigation scheduling for the major crops in the Shaheed Benazirabad district, Sindh, Pakistan, using agro-climatic data and the CROPWAT model. Agro-climatic data such as rainfall, maximum and minimum temperature, sunshine hours, humidity, and wind speed were obtained from the NASA website, CLIMWAT 2.0, and world weather However, data about studied crops and soils were obtained from FAO (Food and Agriculture Organization). Analysis revealed that the IWRs per irrigation round for the four major crops—sugarcane, banana, cotton, and wheat—were as 3108.0 mm, 1768.5 mm, 1655.7 mm, and 402.5 mm, respectively. It was observed the IWRs are more sensitive in the hot season because of high temperatures and low relative humidity, and vice versa in the cold season. The use of scientific tools such as CROPWAT is recommended to assess IWRs with a high degree of accuracy and to compute irrigation scheduling. Accordingly, the study results will be helpful for improving food production and supervision of water resources.

1. Introduction

Soil, water, and plants are natural resources that are very important for the survival of human beings and animals [1,2]. Water is a fundamental input influencing guaranteed crop production. Water dissolves mineral nutrients that move in the plant along its stem. At the end of the life cycle of a plant, water is also a constituent of an economic product, which may be a seed, stem, leaf, flower, or fruit. The second-most key environmental issue of the 21st century in the eyes of both scientists and politicians after the issue of climate change is freshwater scarcity [3,4]. In the upcoming years, it seems unlikely that the world water cycle will be able to cope with demands [5,6,7,8]. At present, irrigation purposes account for about 87% of the water consumed and 70% of global water withdrawal [9]. Around 40–45% of the world’s food is produced by irrigated agricultural lands, which comprise less than a fifth of all cropped areas. In the future, it is generally anticipated that irrigated agriculture will have to be significantly extended to feed mounting populations.

One sustainable solution to control demands on water resources and adverse environmental impacts from irrigation is intelligent irrigation, as it can lessen water use without compromising crop yield [10]. Every crop needs specific water, which is why only the optimum quantity of water should be provided to crops for maximum yield [11]. Sufficient information on evapotranspiration, crop water requirements, and net irrigation requirements is essential for the effective planning of these resources [12,13]. The term “net irrigation amount” is basically the difference between actual crop-specific evapotranspiration on the output side and effective precipitation and capillary rise from groundwater (if shallow) on the input side. The term “gross irrigation” considers water losses (e.g., percolation below the root zone and surface runoff from the field in the case of the irrigation water application process) by the efficiency term (e.g., gross irrigation amount at the field border = net irrigation amount/field application efficiency). Numerically, the total irrigation water requirement is the summation of three quantities: consumptive use, losses (conveyance and application), and other exceptional needs. As the world is facing a serious water shortage, concerns about combining water-saving technologies have arisen because of increasing water demand and global scarcity issues [14,15,16]. Every drop of water is important because the existence of life is due to water. Meeting water demand is becoming more and more challenging day by day. Hence, water should be harnessed properly. Using an appropriate irrigation management strategy can shrink the adverse impacts of over-irrigation; on the other hand, equilibrium between a crop’s water requirements and available water can be maintained.

Pakistan, being an agricultural country, highly depends on irrigation, as the amount of precipitation on the agricultural lands in Pakistan is not sufficient. Irrigation provides water to about 18 million hectares of cultivable land [16,17]. Further, the main source of income for 72% of Pakistan’s population is associated with agriculture. Wheat, rice, cotton, banana, and sugarcane are important commercial crops, cultivated to fulfill the sustainable food demands of the country and add great value to the country’s economy. However, increasing demands for freshwater and scouring losses are likely to impact the agricultural water supply. Khan et al. [18] stated that an important strategy for choosing proper water use is to evaluate the current water status around the world. In Pakistan, including the Shaheed Benazirabad district, the farmers normally over-irrigate their fields due to lack of proper knowledge about crop water requirements and with the idea that more water will produce more yield. There are various models available in the literature, e.g., AquaCrop, CROPWAT, APSIM, CropSyst, etc. However, every model has uncertainties and needs desired data as input. However, the CROPWAT model needs less data as input and has universal applicability. Thus, CROPWAT is the best way to develop proper irrigation scheduling for farmers to understand the required quantity of water at the right time for their fields due to changes in climatic conditions. The CROPWAT model is preferred in this study in the determination of the reference evapotranspiration (ET_o), as it is reported to deliver very reliable values on actual crop water use data worldwide. Systematic crop water requirements are essential to ensuring efficient scheduling of irrigation and water management, design of canal capacities, planning of water resources, regional drainage, and research in reservoir operations. CROPWAT is a tool that calculates agricultural water needs [12,19,20,21]. Furthermore, Gabr and Fottouth [22] showed that crop water requirement simulation models compute effective rainfall, reference evapotranspiration, crop evapotranspiration, net irrigation water requirement, gross irrigation water requirement, irrigation scheduling, and crop growth. Globally, the optimal amount of irrigation water has been calculated using modeling approaches [18,23,24]. However, in general, various studies in the literature focus on the importance and strong need for the investigation of crop water requirements with the changing climate, considering the importance of estimating crop water requirements especially for major crops, e.g., in Pakistan, wheat, cotton, banana, and sugarcane. However, the accurate crop water requirement for these crops under climate change conditions are still lacking. Specifically, there is no study available to estimate crop water requirement considering the CROPWAT model as a tool; thus, the existing scheduling techniques used by the farmers of the Shaheed Benazirabad district are outdated and inefficient. It is, therefore, time to revolutionize this by using CROPWAT to determine crop water requirements, organize the respective data, carry out modeling of irrigation water using climatic, crop, and soil data, and allow proper scheduling. Thus, the present study was conducted to determine the irrigation water requirement (IWR) and irrigation scheduling for the major crops in the Shaheed Benazirabad district, Sindh, Pakistan using agro-climatic data and the CROPWAT model.

2. Study Area and Data

2.1. Description of the Study Area

The study area selected for the present study is the Shaheed Benazirabad district, established by the British government, with a latitude of 26°5′53.99″ N and a longitude of 68°24′34.38″ E. It is also called the Shaheed Benazir Abad district, as presented in Figure 1. Geographically, it is the center of the Sindh province of Pakistan, with an area of 4239 square km and a population of 1,435,130. It is situated 50 km from the left bank of the River Indus. The geographical location of the city makes it a major railway and roadway transportation hub in the province. As a nationwide hub of cotton manufacture and one of the largest producers of bananas in Pakistan, it is also famous for its sugarcane, mango, etc. Climatically, the Shaheed Benazirabad district falls in the arid and semi-arid regions, with a maximum temperature of 52 °C [18]. From a hydrological perspective, the study area belongs to the arid and semi-arid region type, with an average precipitation of more than 100 mm. The quality of underground water is brackish and saline.

2.2. Determination of Irrigation Water Requirements and Irrigation Scheduling

In the present study, irrigation water requirements (IWR), and irrigation scheduling for major crops such as sugarcane, banana, cotton, and wheat cultivated in the Shaheed Benazirabad district, Sindh, Pakistan were determined using respective agro-climatic data and the CROPWAT 8.0 model. CROPWAT is a decision-support computer program developed by the Land and Water Development Division of FAO.

2.3. Temperature, Air Humidity, Sunshine Hours, Wind Speed, and Precipitation

Three types of data are required for the CROPWAT model, namely, climatic data, soil data, and crop data. In the present study, climatic data were obtained from three sources: climatic data through the NASA website, climatic data from FAO software CLIMWAT 2.0 [19,20], and climatic data through worldweatheronline.com. As far as crop and soil data are concerned, these are already available in the software. Only the crop and soil data related to the study area were employed using CROPWAT. Climatic data includes minimum and maximum temperature, relative humidity, wind speed, and sunshine hours. The study area is in the warmest part of Pakistan, where the temperature rarely comes near 0 °C, even in the peak winter season. The observed lowest minimum average temperature and highest minimum temperature are 3.574 °C in November and 8.164 °C in June, respectively. However, the minimum and maximum temperatures, with an average value of temperature in the study area for a period of five years between 2017 and 2021, are shown in Figure 2.

The study area is not generally characterized by high humidity. The maximum average air humidity is (45–55%) and the minimum average air humidity is about (29–35%) Sunshine hours are also the main parameter used for estimation of evaporation. The representative sunshine hours for the Shaheed Benazirabad district were taken from CLIMWAT. The seasonal and annual prevailing winds in the study area are mainly western disturbances in the shape of dust storms and continental air, which are the main factors influencing the weather. The precipitation pattern over the study area shows maximum precipitation in the period of hot summer (June to September), with a low precipitation average of 100 mm to 130 mm annually. The precipitation data used in this study were taken from worldweatheronline.com. The respective data on air humidity, sunshine hours, and precipitation in the study area are exhibited in Figure 3.

2.4. Crop and Soil Data for the Study Area

The major cultivable crops in the study area are wheat, cotton, sugarcane, and banana. The date of planting and harvesting of crops considered for the present study are described in

Table 1. Dates of sowing and harvesting of crops recommended by Department of Agriculture, Government of Sindh.

Crop	Date of Sowing	Date of Harvesting
	(Date/Month)	(Date/Month)
Wheat	01/11	10/03
Cotton	15/04	26/10
Sugarcane	15/02	14/02
Banana	01/03	24/01

. The soil in the study area is medium (loam) soil; the same soil is available in the software that was being employed for the present study, as portrayed in Figure 4.

2.5. Reference Evapotranspiration and Effective Rainfall

The reference ET_o is the removal of water from a hypothesized plant of 0.12 m, surface tension of 70 s/m, and albedo of 23%, with no water deficit to evaporation from ordinary grasses and covering the soil and watering it appropriately [21,25,26,27]. The CROPWAT model, for the calculation of ET_o, employs the FAO Pen Monteith equation with the help of measured weather data (Equation (1)) [20,28].

$$\mathrm{ETo}=\frac{0.408\Delta \left(\mathrm{Rn}-\mathrm{G}\right)+\frac{900}{\mathrm{T}+273}{\mathrm{u}}_2\left({\mathrm{e}}_{\mathrm{s}}-{\mathrm{e}}_{\mathrm{a}}\right)}{\Delta +\mathsf{\gamma } \left(1+0.3442\right)}$$

(1)

where R_n = net radiation (MJ/m²/day); G = soil heat flux density (MJ/m²/day); T = mean daily air temperature at 2 m height (°C); ${\mathrm{u}}_2$ = wind speed at 2 m height (m/s); (e_s − e_a) = vapor pressure deficit of the air (kPa); ∆ = slope of the vapor pressure (kPa °C⁻¹); γ = psychometric constant (kPa °C⁻¹).

The fraction of rainfall that is stored in the soil profile and helps in the growth of crops is effective rainfall. In the present study, the USDA Soil Conservation Service method was used to calculate the effective rainfall [20], as described in Equations (2) and (3).

$${\mathrm{p}}_{\mathrm{eff}}=\frac{{\mathrm{p}}_{\mathrm{tot}}\left(125-0.2\times { \mathrm{p}}_{\mathrm{tot}}\right)}{125}{ \mathrm{for} \mathrm{p}}_{\mathrm{tot}}<250 \mathrm{m}\mathrm{m} (\mathrm{per} \mathrm{month})$$

(2)

$${\mathrm{p}}_{\mathrm{eff}}=125+0.1{ \mathrm{p}}_{\mathrm{tot}}{ \mathrm{for} \mathrm{p}}_{\mathrm{tot} }>250 \mathrm{mm} \left(\mathrm{per} \mathrm{month}\right)$$

(3)

where ${\mathrm{p}}_{\mathrm{eff}}$ = effective rainfall (mm); ${\mathrm{p}}_{\mathrm{tot}}$ = total rainfall (mm).

2.6. Crop Water Requirement, Irrigation Water Requirement, and Irrigation Scheduling

The amount of water equal to what is lost from a cropped field by the evapotranspiration is known as the crop water requirement. It is expressed by the rate of ET in mm/day and can be calculated using Equation (4) [19].

$${\mathrm{ET}}_{\mathrm{c}}={ \mathrm{K}}_{\mathrm{c}}\times {\mathrm{ET}}_{\mathrm{o}}$$

(4)

where ${\mathrm{K}}_{\mathrm{c}}$ = crop coefficient (the ratio of the ET_c to the ET_o). This varies by crop and can be obtained from the CROPWAT model.

The irrigation water requirement (IWR) is the amount of water needed to fulfil the crop water requirement after any effective rainfall, for a disease-free crop growing in large fields under non-restricting soil and water conditions and under adequate fertility [29]. Pakistan is an agriculture-based country; agriculture has a remarkable share in the country economy and GDP. However, the country is documented as a water-stressed country by UNO. Thus, it is very imported to evaluate the optimal IWR to enhance crop productivity and boost the country’s economy.

The correct quantity of water to irrigate and the correct moment in time for watering are determined by irrigation scheduling. The development of irrigation scheduling under different administration conditions and water supply plans is performed after the calculation of ET_o, and IWR through the CROPWAT model [19,28,30].

3. Results and Discussion

Data such as type of crop, date of cultivation, and soil type medium (loam) were entered into the CROPWAT and CLIMWAT software, including the country Pakistan and climatic station Shaheed Benazirabad. Once all the data were entered into the software, it calculated the irrigation water requirement and crop irrigation scheduling for major crops cultivated in the study area.

3.1. Reference Evapotranspiration (ET_o)

The reference evapotranspiration (ET_o) for the major cultivated crops—wheat, cotton, sugarcane, and banana—in the district was calculated from the Penman–Monteith equation with the help of agro-climatic data. The reference evapotranspiration ranged from 3.53 mm/day to 11.95 mm/day. The maximum was in June and the minimum was in January, as shown in Figure 5. It was observed that ET_o is high in summer due to the high temperature and decreases in winter due to the low temperature. Further, it was seen that increase in radiation value brings an increase in the ET_o value, with the direct relation shown in Figure 6. The annual mean ET_o was calculated as 7.49 mm. The low relative humidity, high temperatures, and high wind increased evapotranspiration during the dry season [31]. The differences in ET_o values reflect the variation in weather parameters in the study area.

3.2. Effective Rainfall

Using the USDA Soil Conservation Service method, which utilizes the total rainfall value, effective rainfall was calculated. The maximum and minimum values of effective rainfall were found to be 47.1 mm in August and 0.4 mm in December, respectively. Further, the results showed that the effective rainfall was the same as the total rainfall for most of the months except June, July, August, and September, due to the high temperatures and wind speeds in these months.

3.3. Crop Water Requirements (ET_c) and Irrigation Water Requirements (IWR)

Table 2. Irrigation water requirement of wheat.

Month	Decade	Stage	Kc Coefficient	ETc	ETc	Effective Rainfall	Irrigation Required
				(mm/day)	mm	mm	mm
November	1	Initial	0.3	1.6	16	0.9	15.1
November	2	Initial	0.3	1.44	14.4	0.9	13.5
November	3	Initial	0.3	1.33	13.3	0.7	12.6
December	1	Development	0.46	1.89	18.9	0.3	18.6
December	2	Development	0.76	2.82	28.2	0	28.2
December	3	Mid	1.06	4.28	42.8	0.2	42.6
January	1	Mid	1.18	4.18	41.8	0.6	41.2
January	2	Mid	1.18	4.06	40.6	0.9	39.7
January	3	Mid	1.18	4.94	49.4	0.9	48.5
February	1	Late	1.17	4.91	49.1	0.8	48.3
February	2	Late	0.96	4.35	43.5	0.8	42.6
February	3	Late	0.7	3.57	28.6	0.8	27.8
March	1	Late	0.43	2.45	24.5	0.7	23.8
Total					411	8.5	402.5 mm/ crop season

,

Table 3. Irrigation water requirement of cotton.

Month	Decade	Stage	Kc Coefficient	ETc	ETc	Effective Rainfall	Irrigation Required
				(mm/Day)	mm	mm	mm
April	2	Initial	0.35	1.91	19.1	1	18.2
April	3	Initial	0.35	3.45	34.5	1.4	33.1
May	1	Initial	0.35	3.76	37.6	1	36.6
May	2	Development	0.39	4.52	45.2	0.7	44.5
May	3	Development	0.58	7.45	74.5	1.1	73.4
June	1	Development	0.78	9.25	92.5	1	91.5
June	2	Development	0.97	11.75	117.5	1	116.5
June	3	Development	1.16	13.58	135.8	3.6	132.2
July	1	Mid	1.29	14.65	146.5	6.4	140.2
July	2	Mid	1.3	14.31	143.1	8.6	134.5
July	3	Mid	1.3	14.76	147.6	11	136.6
August	1	Mid	1.3	12.4	124	14.5	109.5
August	2	Mid	1.3	11.51	115.1	17.4	97.7
August	3	Mid	1.3	12.39	123.9	15.1	108.8
September	1	Late	1.25	10.68	106.8	12.5	94.3
September	2	Late	1.13	9.46	94.6	11	83.6
September	3	Late	1.01	7.81	78.1	7.6	70.5
October	1	Late	0.89	6.3	63	2.9	60.1
October	2	Late	0.78	4.99	49.9	0	49.9
October	3	Late	0.68	2.41	24.1	0.1	24
Total					1773.5	117.9	1655.7 mm/ crop season

,

Table 4. Irrigation water requirement of sugarcane.

Month	Decade	Stage	Kc Coefficient	ETc	ETc	Effective Rainfall	Irrigation Required
				(mm/day)	mm	mm	mm
February	2	Init	0.8	1.46	14.6	0.5	14.1
February	3	Init	0.4	1.64	16.4	0.8	15.6
March	1	Init	0.4	2.27	22.7	0.7	22
March	2	Development	0.42	2.6	26	0.6	25.4
March	3	Development	0.56	4.42	44.2	0.9	43.3
April	1	Development	0.73	5.93	59.3	1.3	58
April	2	Development	0.89	8.04	80.4	1.7	78.7
April	3	Development	1.05	10.3	103	1.4	101.6
May	1	Development	1.2	12.96	129.6	1	128.6
May	2	Mid	1.34	15.54	155.4	0.7	154.7
May	3	Mid	1.36	17.48	174.8	1.1	173.7
June	1	Mid	1.36	16.14	161.4	1	160.4
June	2	Mid	1.36	16.48	164.8	1	163.8
June	3	Mid	1.36	15.92	159.2	3.6	155.6
July	1	Mid	1.36	15.36	153.6	6.4	147.2
July	2	Mid	1.36	14.93	149.3	8.6	140.7
July	3	Mid	1.36	15.41	154.1	11	143.1
August	1	Mid	1.36	12.94	129.4	14.5	114.9
August	2	Mid	1.36	12.01	120.1	17.4	102.7
August	3	Mid	1.36	12.93	129.3	15.1	114.2
September	1	Mid	1.36	11.61	116.1	12.5	103.6
September	2	Mid	1.36	11.35	113.5	11	102.5
September	3	Mid	1.36	10.47	104.7	7.6	97.1
October	1	Mid	1.36	9.55	95.5	2.9	92.6
October	2	Mid	1.36	8.71	87.1	0	87.1
October	3	Mid	1.36	8.77	87.7	0.3	87.4
November	1	Mid	1.36	7.23	72.3	0.9	71.4
November	2	Mid	1.36	6.37	63.7	0.9	62.8
November	3	Late	1.27	5.63	56.3	0.7	55.6
December	1	Late	1.21	4.95	49.5	0.3	49.2
December	2	Late	1.15	4.3	43	0	43
December	3	Late	1.09	4	44	0.2	43.8
January	1	Late	1.03	3.63	36.3	0.6	35.7
January	2	Late	0.97	3.32	33.2	0.9	32.3
January	3	Late	0.91	3.79	37.9	0.9	37
February	1	Late	0.84	3.54	35.4	0.8	34.6
February	2	Late	0.8	1.43	14.3	0.3	14.0
Total					3245.4	130.1	3108 mm/ crop season

and

Table 5. Irrigation water requirement of banana.

Month	Decade	Stage	Kc Coefficient	ETc	ETc	Effective Rainfall	Irrigation Required
				(mm/Day)	mm	mm	mm
March	1	Initial	0.5	2.84	28.4	0.7	27.7
March	2	Initial	0.5	3.12	31.2	0.6	30.6
March	3	Initial	0.5	3.59	39.5	0.9	38.6
April	1	Initial	0.5	4.08	40.8	1.3	39.5
April	2	Initial	0.5	4.54	45.4	1.7	43.7
April	3	Initial	0.5	4.92	49.2	1.4	47.8
May	1	Initial	0.5	5.38	53.8	1	52.8
May	2	Initial	0.5	5.8	58	0.7	57.3
May	3	Development	0.5	6.46	64.6	1.1	63.5
June	1	Development	0.53	6.3	63	1	62
June	2	Development	0.57	6.9	69	1	68
June	3	Development	0.61	7.12	71.2	3.6	67.6
July	1	Development	0.65	7.31	73.1	6.4	66.7
July	2	Development	0.68	7.53	75.3	8.6	66.7
July	3	Development	0.72	8.24	82.4	11	71.4
August	1	Development	0.77	7.31	73.1	14.5	58.6
August	2	Development	0.8	7.13	71.3	17.4	53.9
August	3	Development	0.85	8.05	80.5	15.1	65.4
September	1	Development	0.89	7.59	75.9	12.5	63.4
September	2	Development	0.92	7.74	77.4	11	66.4
September	3	Development	0.96	7.44	74.4	7.6	66.8
October	1	Development	1	7.05	70.5	2.9	67.6
October	2	Development	1.04	6.69	66.9	0	66.9
October	3	Development	1.08	6.69	69.9	0.3	69.6
November	1	Development	1.12	5.98	59.8	0.9	58.9
November	2	Mid	1.14	5.45	54.5	0.9	53.6
November	3	Mid	1.14	5.05	50.5	0.7	49.8
December	1	Mid	1.14	4.65	46.5	0.3	46.2
December	2	Mid	1.14	4.26	42.6	0	42.6
December	3	Late	1.13	4.57	45.7	0.2	45.5
January	1	Late	1.1	3.89	38.9	0.6	38.3
January	2	Late	1.07	3.67	36.7	0.9	35.8
January	3	Late	1.05	1.56	15.6	0.3	15.3
Total					1895.7	127.1	1768.5 mm/ crop season

present the irrigation water requirements calculated by the CROPWAT model for the crops included in the study area. The total water requirements for different crops in various agro-ecological zones, obtained after the application of the data of the study area in the CROPWAT model, are given in Table 4, Table 5,

Table 6. Combined irrigation water requirements.

Crop	Date of Sowing	Date of Harvesting	Irrigation Required
	(Date/Month)	(Date/Month)	(mm)
Wheat	01/11	10/03	402.5
Cotton	15/04	26/10	1655.7
Sugarcane	15/02	14/02	3108
Banana	01/03	24/01	1768.5

Note: The dates of sowing and harvesting are recommended by the Agriculture Department, Government of Sindh.

and

Table 7. Irrigation schedule for wheat.

Date	15 January	10 March
Day	76	End
Stage	Mid	End
Rainfall (mm)	0	0
Ks (fraction)	1	1
ETa (%)	100	100
Depletion (%)	55	60
Net Irrigation (mm)	191.6	--
Gross Irrigation	273.7	--

. The total water requirements for wheat, cotton, sugarcane, and banana were 411 mm, 1773.5 mm, 3245.4 mm, and 1895.7 mm, respectively. The results showed that the crop water requirements of all the selected crops of the study area were higher during the dry season than in the rainy season, which reflects that the crops grown in the dry season need more water than those grown during the rainy season and require a large amount of water due to the hot climate of Shaheed Benazirabad [18]. This parallels the FAO report [32], which asserts that crops grown in the rainy season need less water than those grown during the dry season. Further, it was observed that during the developing and growing stages, crops also need a large quantity of water, with the greatest requirements in the growing stage compared to the other three stages, due to the high value of reference evapotranspiration in the months encompassed by the growing stage. Depending upon the place, soil type, climate, effective rain, cultivation technique, etc., crops require different quantities of water. It is also a fact that the total water needed for the growth of a crop is not evenly distributed over its entire life span [30].

The irrigation water requirements (IWRs) for the four crops—sugarcane, banana, cotton, and wheat—for the entire growing season were found as 3108 mm, 1768.5, 1655.7 mm, and 402.5 mm, respectively. However, the findings of the present study cannot be compared in a complete manner due to the unavailability of similar studies in the same region. However, the results of this study are comparable to other studies conducted in various parts of the world [18,28,33]. Khan et al. [33] conducted a study to investigate the crop water requirement for the wheat and cotton in Sudan; the results of that study are similar to the results of for wheat and cotton in the present study. This similarity of the results gives strength to our finding. Moreover, the estimated IWR for wheat in this study is similar to recent studies conducted by Khan et al. [12] for Peshawar. Moreover, the results of banana and other crops also parallel recent studies conducted in the same region, Kerala, India [28]. The combined IWRs of selected crops with planting and harvesting dates are described in Table 6.

From analysis of the results obtained through the CROPWAT model, it was observed that the crops with a longer period of growth, such as sugarcane and banana, which employ almost all the months of the year, required a greater amount of water. On the other hand, crops with a shorter period of growth showed a lower irrigation water requirement. Further, the elevated irrigation requirements in the months of the hot season may be explained by the lack of rain combined with high temperatures, which lead to increased evapotranspiration. Additionally, high evaporation and shrinkage in soil moisture during the hottest period with the highest temperature imply the highest agricultural water requirement. Water losses throughout Shaheed Benazirabad district are substantial in irrigation schemes, as water is generally transported to the farmers’ fields through very poorly maintained distribution systems made of earthen canals and ditches, which suffer substantial water loss due to infiltration and seepage.

3.4. Irrigation Scheduling

Knowledge of irrigation schedules improves irrigation management in the field, which includes controlling the amount, timing, and rate of irrigation in an efficient and planned manner. Table 7,

Table 8. Irrigation schedule for cotton.

Date	25-May	19-June	9-July	30-July	25-August	3-October	26-October
Day	41	66	86	107	133	172	End
Stage	Dev	Dev	Mid	Mid	Mid	End	End
Rainfall (mm)	0	0	0	0	0	1.4	0
Ks (fraction)	1	1	1	1	1	1	1
ETa (%)	100	100	100	100	100	100	0
Depletion (%)	66	68	66	67	65	80	28
Net Irrigation (mm)	165.7	236.4	269.2	271.4	265.9	323.4	--
Gross Irrigation	236.8	337.7	384.6	387.7	379.8	462

,

Table 9. Irrigation schedule for sugarcane.

Date	22-April	15-May	2-June	20-June	9-July	30-July	26-August	22-September	23-October	6-December	14-February
Day	67	90	108	126	145	166	193	220	251	295	End
Stage	Dev	Dev	Mid	Mid	Mid	Mid	Mid	Mid	Mid	End	End
Rainfall (mm)	0	0	0	0	0	0	0	0	0.1	0	0
Ks (fraction)	1	1	1	1	1	1	1	1	1	1	1
ETa (%)	100	100	100	100	100	100	100	100	100	100	0
Depl. (%)	65	66	65	67	66	65	67	65	65	65	59
Net Irrigation (mm)	284.9	287.6	283.3	292.4	287.2	284.2	291.1	284.5	283.3	282.9	--
Gross Irrigation	407	410.9	404.8	417.8	410.3	406	415.9	406.5	404.8	404.2	--

and

Table 10. Irrigation schedule for banana.

Date	19-March	5-April	21-April	6-May	20-May	4-June	18-June	2-July	16-July	31-July	19-August	6-September	22-September	9-October	27-October	17-November	12-December	10-January	24-January
Day	19	36	52	67	81	96	110	124	138	153	172	190	206	223	241	262	287	316	End
Stage	Init	Init	Init	Init	Init	Dev	Dev	Dev	Dev	Dev	Dev	Dev	Dev	Dev	Dev	Mid	Mid	End	End
Rainfall (mm)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.1	0.5	0	0	0
Ks (fraction)	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
ETa (%)	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Depl. (%)	55	55	56	56	55	58	56	56	53	53	50	51	49	49	47	47	46	45	18
Net Irrigation (mm)	55	62	68	75	79	88	92	96	96	100	102	110	111	116	118	121	121	117
Deficit (mm)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Loss (mm)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Gross Irrigation	78	87	97	100	112	126	131	137	137	144	146	157	158	165	169	174	173	168
Flow (l/s/ha)	0.4	0.6	0.7	0.82	0.9	0.9	1.1	1.1	1.1	1.11	0.89	1.01	1.15	1.13	1.09	0.96	0.8	0.67

and Figure 7, Figure 8, Figure 9 and Figure 10 illustrate the field crop irrigation schedules for the wheat, cotton, sugarcane, and banana crops cultivated in the study area.

Table 7 and Figure 7, created by CROPWAT show that the gross irrigation requirement for wheat is 273.7 mm and the net irrigation requirement is 191.6 mm. TAM is the total available moisture or the total amount of water available to the crop and RAM is the readily available moisture or the portion of TAM that the plant can extract from the root zone without facing water stress.

Table 8 and Figure 8 show that the gross irrigation requirement for cotton is 2188.6 mm and the net irrigation requirement is 1532.0 mm.

Table 9 and Figure 9 show that the gross irrigation requirement and the net irrigation requirements for sugarcane are 4087.9 mm and 2861.6 mm, respectively.

Table 10 and Figure 10 show the gross irrigation requirement and the net irrigation requirements for banana in the study area as 2469.9 mm and 1728.9 mm, respectively.

4. Conclusions

Analysis revealed that for the entire growing season for the four major crops—wheat, cotton, banana, and sugarcane—IWR was observed as 402.5 mm, 1655.7 mm, 1768.5 mm, and 3108.0 mm, respectively. The findings of the study show that since all crops except the wheat crop (with IWR of 402.2 mm/dec) incorporate the hot season in their life cycle, the irrigation water requirements are high due to the unfavorable value of the parameters that influence reference evapotranspiration (ET_o). Further, an increase in the value of the irrigation water requirements was seen for the crops that have a lifecycle throughout the year, such as sugarcane, with its IWR of 3108.0 mm. Moreover, it was observed that rainfall reduces the irrigation water requirement by a considerable amount. The IWRs for the four main crops investigated in this study will add valuable insights for implementing a water conservation policy for this region. Consequently, by calculating the IWR of crops as well as the scheduling of irrigation, and by recognizing the behavior of weather, this study will be helpful for future researchers and can be used as a guide for farmers to decide the frequency and amount of irrigation for the studied crops.

The present study is based on 5 years of climatic data from the study area. It is recommended that higher accuracy be achieved by using a period of at least 10–20 years. The present study is based on the soil and climatic data of Shaheed Benazirabad district, Sindh, Pakistan; thus, identical studies can be carried out using the different climactic locations of the country.