Issue 1 (201)/2025

ALSE and ACS Style
Raoufi, H.; Abdul Saleem, J. Assessing the impacts of climate change on wheat crop water requirements in Central Afghanistan. Journal of Applied Life Sciences and Environment 2025, 58 (1), 121-142.
https://doi.org/10.46909/alse-581168

AMA Style
Raoufi H, Abdul Saleem J. Assessing the impacts of climate change on wheat crop water requirements in Central Afghanistan. Journal of Applied Life Sciences and Environment. 2025; 58 (1): 121-142.
https://doi.org/10.46909/alse-581168

Chicago/Turabian Style
Raoufi, Homayoon, and Jamily Abdul Saleem. 2025. “Assessing the impacts of climate change on wheat crop water requirements in Central Afghanistan.” Journal of Applied Life Sciences and Environment 58, no. 1: 121-142.
https://doi.org/10.46909/alse-581168

Assessing the impacts of climate change on wheat crop water requirements in Central Afghanistan

Homayoon RAOUFI^1,2* and Jamily ABDUL SALEEM³

¹Faculty of Agriculture, Kunduz University, Afghanistan

²Department of Environmental Planning, Faculty of Environment, University of Tehran, Tehran, the Islamic Republic of Iran

³Department of Plant Protection, Faculty of Agriculture, Kabul University, Kabul, Afghanistan; email: saleemjamilypp@gmail.com

*Correspondence: h.abdullah1400@gmail.com

Received: Jan. 09, 2025. Revised: Apr. 09, 2025. Accepted: Apr. 11, 2025. Published online: May 14, 2025

 

ABSTRACT. Water crises in agricultural production are a serious issue in water-limited ecosystems and farming regions. This study was conducted to estimate crop water requirements, crop irrigation requirements, and irrigation scheduling under climate change in Central Afghanistan using CROPWAT8.0 software. The climatic variables were acquired from local organisations and the general circulation model (GCM) (1990–2020). The temperature and rainfall patterns were evaluated under three scenarios (RCP2.6, 4.5, and 8.5) from 2025 to 2100. CROPWAT8.0 was used to assess reference evapotranspiration, crop water requirements, crop irrigation requirements, and irrigation scheduling based on predicted meteorological conditions under different scenarios. The crop water requirements under RCP2.6, 4.5, and 8.5 increased by 9.74, 9.99, and 10.28 mm/day, respectively, compared to the baseline at 9.07 mm/day. Moreover, crop irrigation requirements would increase by 92.5 (18.46%), 109.7 (21.88%), and 100.7 mm/dec (20.09%) under RCP2.6, 4.5, and 8.5, respectively, compared to the baseline at 62.65 mm/dec. Furthermore, the results showed that wheat needs 3 irrigations in the baseline scenario, while it would need 4 irrigations for future scenarios due to an increase in crop irrigation requirements. The results of this study will be useful for agricultural practices and management.

Keywords: Afghanistan; irrigation scheduling; precipitation; scenarios; temperature.

 

INTRODUCTION

Water and water availability play an important role in plant growth and development. Water resources and availability limit plant production and agricultural sustainability (Li et al., 2022; Pais et al., 2023). Due to the rapid growth of the world’s population, there is a need for more food. On a global scale, water stress and droughts greatly affect food security in irrigated agriculture production areas (Gabr and Fattouh, 2021; Ghimire and Johnston, 2019; Mainuddin et al., 2014). Water stress is a part of the climate crisis, and it affects nearly 80% of the world’s population (Ghimire and Johnston, 2019). By 2050, grain production will be reduced by water stress during the reproductive stages by 35–56% (Bello et al., 2022). If the current situation continues, the available supply of water is expected to be depleted by about 40% by 2030 (Gabr, 2023).

Water shortage is an important variable that limits crop production by impacting the development of the root system and crop physiology processes during crop growing periods in arid and semi-arid regions (Antoniuk et al., 2023; Bello et al., 2022; Zhong and Shangguan, 2014). Crop water conditions, such as the crop water requirement (CWR), may be affected by several climatic variables, such as temperature, rainfall, radiation, wind speed, sunlight, and relative humidity (Kambale et al., 2023; Khan et al., 2019, 2021; UNDP, 2017).

CWR is the amount of water supplied to progress physiological activities during the growing season. CWR is needed to rectify the water consumed by plant transpiration and soil evaporation (Paymard et al., 2019). CWR may be provided by an irrigation system or precipitation to satisfy the water demand for excellent production (Khan et al., 2021). CWR is computed by the reference evapotranspiration (ETo) and crop coefficient (Kc) (El-Rawy et al., 2023; Gabr and Fattouh, 2021). ETo is a key factor for estimating crop evapotranspiration (ETc), CWR, and the crop irrigation requirement (CIR), and it is defined as the rate of evapotranspiration from a grass surface “reference crop” without stress (Alotaibi et al., 2023; Gabr, 2021, 2023; Gurara et al., 2021; Rahmani et al., 2016). ETo is calculated based on climatic and geographical variables (Allen et al., 1998; Gheysari et al., 2006; Truvedu et al., 2018). Moreover, CWR influences other crop parameters, such as CIR and irrigation scheduling. The amount of water provided by the irrigation system to meet the crop’s water requirements (Gabr and Fattouh, 2021) is called CIR. The irrigation schedule determines the amount and time of irrigation. Irrigation can be scheduled based on ETc determination during the growing season (Ko et al., 2009). Afghanistan’s agricultural production requires irrigation due to unreliable or insufficient rainfall during growing seasons in many areas (UNDP, 2017). It is necessary to measure the amount of water required for important plants, such as wheat. CROPWAT 8.0 software was used to calculate ETo, effective rainfall, ETc, CWR, and CIR. CROPWAT8.0 estimates the crop water and irrigation requirements, and scheduling followed the Penman–Monthieth method. CROPWAT8.0 is approved by the Food and Agriculture Organization (FAO) and used in many countries, including Greece (Anadranistakis et al., 2000), Taiwan (Sheng-Feng et al., 2006), Africa (Wahaj et al., 2007), Cameroon (Molua and Lambi, 2006), Ethiopia (Gurara et al., 2021), Pakistan (Khan et al., 2021), India (Kambale et al., 2023; Sharma and Tare, 2021; Veeranna and Mishra, 2017), Iraq (Ewaid et al., 2019), Turkey (Aydin, 2022), the USA (Kang et al., 2009), Zimbabwe (Mhashu, 2007), Iran (Paymard et al., 2019), Egypt (Gabr and Fattoh, 2021; Gabr, 2023), and Saudi Arabia (Chowdhury et al., 2016; El-Rawy et al., 2023), but not in the study area. Crop models have been applied in many studies to design adaptation strategies against climate change under representative concentration pathways (RCPs) (Dahri et al., 2024). To work with the models, some parameters, including climatic, crop, and soil parameters, are required. Concerning the climate data, two types of variables, namely predictand and predictor variables, are required for simulation periods. In this study, predictands, daily observed temperature and rainfall data, were collected from the datasets of national organisations, such as the Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Ministry of Energy and Water (MEW), and the Meteorological Department of Afghanistan (MDA), and predictor data were obtained from the general circulation models (GCMs) for the reference period, 1990–2020. GCMs are the most usable tools for studying future climate change on a large scale under several scenarios (Khalafa et al., 2022; Mukheef et al., 2024). The statistical downscaling model (SDSM) and the Long Ashton Research Station Weather Generator (LARS-WG) were used to downscale and project the future scenarios (2025–2100) under RCP2.6, 4.5, and 8.5 in accordance with the Intergovernmental Panel on Climate Change (IPCC, 2014). These models are effective at projecting climate variables over time (Hassan, 2021). RCPs describe different stages of greenhouse gases and other radiative forcings that might occur in the future. RCP2.6, 4.5, and 8.5 have very low and medium forcing levels and very high baseline emissions, respectively (Wayne, 2013). A parametric regression method was applied to analyse the climatic trends. A combination of data from the FAO, Afghanistan soil catalogues (FAO, 2016, 2019), and CLIMWAT datasets was used for soil and crop parameters. Wheat, a cereal crop that plays a vital role in food security in Afghanistan, was chosen. Crop declines due to climate change have affected both the economy and the livelihoods of people in Afghanistan. Given the lack of studies on the impact of drought on crop water requirements, it was considered necessary to assess how precipitation and temperature influence plant water requirements in the central part of the country. This study aimed to determine the effects of temperature and precipitation variation on CWR in central Afghanistan. To reduce climatic disasters, a sustainable agricultural system is necessary to reduce the negative impacts of climate on crop production. Adaptation strategies, including cultivation and field activities, seed selection, ecological conservation, selecting irrigation systems, and water resources management, support sustainable agricultural systems.

 

MATERIALS AND METHODS

Study area

The study was conducted in the central region of Afghanistan, located between 34.45°N and 69.00°E, at an elevation of approximately 1958.5 m. The climate is semi-arid, with a mean annual temperature of 12.4°C and annual precipitation around 362 mm, mostly occurring in March. Wheat is cultivated from October to November and harvested in June (Figure 1).

Data sources and processing

Daily observed temperature and precipitation data from 2003 to 2020 were obtained from national agencies (MAIL, 2023; MEW, 2023; MDA, 2023). The selected meteorological stations included Karizmir, Kabul-airport, Qargha, and Shakardara.

For climate projections, GCM data from IPCC AR5 (CMIP5) under RCP2.6, 4.5, and 8.5 scenarios were used and downscaled using the SDSM and LARS-WG models. Predictor data from NCEP reanalysis (Zhou et al., 2017) were employed for correlations with observed variables. Concerning the projected data under RCP2.6, 4.5, and 8.5, a combination of data was used to calculate ETo, ETc, Kc, CWR, and CIR using the CROPWAT8.0 model (Figure 2).

Bias correction and trend analysis

To correct systematic errors between observed and simulated data for the baseline period (1990–2020), mean-based bias correction was applied (Gurara et al., 2021; Jha et al., 2021; Munawar et al., 2022). Trend analysis was performed using regression, and model performance was evaluated using the Pearson correlation coefficient (R), coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), normalised root mean squared error (NRMSE), and bias metrics.

CROPWAT 8.0 application

ETo, ETc, CWR, and CIR were estimated using the FAO CROPWAT 8.0 model (Allen et al., 1998). Input parameters included the soil type (sandy loam), Kc, rooting depth, and effective rainfall (FAO, 2019; Gurara et al., 2021). Effective rainfall was estimated using USDA soil conservation equations.

Standard methods were applied to calculate ETo, ETc, and CIR, as described by Allen et al. (1998) and subsequent literature (Aydin, 2022; Gabr and Fattouh, 2021). Only scenario-specific data, local calibration, and integration with stakeholder input (regarding planting/harvest dates) were adapted for the study area.

Effective rainfall (Eff.R)

For field crops, effective rainfall was the sum of precipitation that fell at the site where it could be stored in the root zone and be easily available and directly and/or indirectly useful for crop production (Ali and Mubarak, 2017; Gurara et al., 2021). Effective rainfall was computed using Equation (1) and Ecuation (2) according to the USDA soil conservation method.

Eff. R = P (125 – 0.2 P)/125 for P ≤ 250 mm

(1)

Eff. R = 125 + 0.1 P for P > 250 mm

(2)

where Eff. R is the effective precipitation in mm, and P is the total rainfall in mm during the growing months (Wane and Nagdeve, 2014; FAO, 2022).

 

 

 

Reference evapotranspiration(ETo)

ETo was calculated based on the maximum and minimum temperature, mean wind speed, average humidity, net radiation at the crop surface, and geographical and soil parameters according to the FAO method as described in Equation (3).

where ETo is reference evapotranspiration in mm/day, nR is net radiation at crop surface in MJ m²day⁻¹, G is soil heat flux in MJ m²day⁻¹, T is the mean air temperature in ℃, U2 is the wind speed at 2 m height in m/sec, es is saturation vapour pressure in Kpa, ea is actual vapour pressure in Kpa, es-ea is vapour pressure deficit in Kpa, ∆ is the slope of vapor pressure curve in Kpa/℃, γ is the psychometric constant in Kpa/℃, and 900 is the factorial change in the units (Allen et al., 1998; Aydin, 2022; Chowdhury et al., 2016; El-Rawy et al., 2023; Gheysari et al., 2006; Mainuddin et al., 2014; Truvedu et al., 2018).

Crop water requirement (CWR)

CWR is the amount of water supplied to progress physiological activities during the growing season. It can be provided by rainfall and irrigation to satisfy CWR.

The total amount of ETc throughout the growing season was the CWR, and it depended on ETo and Kc, as calculated using Equation (4) (Gabr, 2023; Gabr and Fattouh, 2021).

ETc = kc × ETo

(4)

where ETc is crop evapotranspiration, ETo is the reference evapotranspiration, and Kc is the crop coefficient (Aydin, 2022; Gabr, 2023; Gabr and Fattouh, 2021; Paymard et al., 2019).

Crop irrigation requirement (CIR)

CIR is the amount of water needed to meet CWR, and it must be supplied by irrigation systems.

The CIR was calculated using Equation 5 (Allen et al., 1998; Gabr, 2023; Gabr and Fattouh, 2021; Gurara et al., 2021; Khan et al., 2021; Zhou et al., 2017).

CIR = CWR − Eff. R

(5)

Irrigation scheduling

Several factors, such as CWR, soil parameters, the effective depth of the root zone, and net irrigation water requirements (NIWR), were used to determine irrigation scheduling. Irrigation scheduling was calculated by CROPWAT 8.0 to develop water supply plans.

 

RESULTS

Climatic data

Table 1 and Table 2 show the average monthly climatic data for RCP2.6, 4.5, and 8.5. The average values of maximum temperature were 23.3, 24.1, and 25.3°C under RCP2.6, 4.5, and 8.5, respectively, compared to the reference scenario at 21.4°C. Moreover, the minimum temperatures were 11.0, 11.8, and 13.2°C under RCP2.6, 4.5, and 8.5, respectively, compared to the baseline at 8.6°C (Table 1).

Reference evapotranspiration (ETo)

Table 3 illustrates the average monthly ETo in mm/day for the baseline and projection scenarios under RCP2.6, 4.5, and 8.5 using CROPWAT8.0.

The average ETo was projected to be 4.22, 4.29, and 4.43 mm/day under RCP2.6, 4.5, and 8.5, respectively, compared to the reference scenario at 4.02 mm/day (Table 4).

Due to increases in temperature, radiation, and sunshine, ETo increased.

Crop coefficient (Kc)

Table 4 shows the Kcs in the growing stages.

The growing stages were divided into the initial (Kc init), development (Kc develop), mid (Kc mid), and end stages (Kc end).

Crop water requirement (CWR)

For RCP2.6, RCP4.5, and RCP8.5 (2025–2100), the mean monthly ETc was 9.74, 9.99, and 10.28 mm/day, respectively, compared to the reference scenario at 9.07 mm/day (Table 5).

 

Table 1
Average monthly temperature and maximum and minimum temperatures for the baseline (1990–2020) and future scenarios (2025–2100)

Month	Tmax (°C)				Tmin (°C)
	Base	RCP2.6	RCP4.5	RCP8.5	Base	RCP2.6	RCP4.5	RCP8.5
Jan	10.2	12.26	13.34	14.28	−0.2	2.36	3.37	4.91
Feb	12.1	14.95	16.06	17.02	1.2	4.84	6.08	7.48
Mar	17.3	19.96	20.72	22.06	5.6	9.46	10.36	11.87
Apr	20.9	24.6	25.48	26.96	8.2	12.16	12.85	14.27
May	25.1	28.05	28.91	30.2	11.5	14.57	15.04	16.25
Jun	29.4	32.06	32.87	34.04	14.6	16.94	17.44	18.59
Jul	32.1	33.32	33.88	35.06	16.7	18.49	19.25	20.43
Aug	31.5	32.83	33.24	34.55	16.1	17.43	18.38	19.68
Sep	27.8	28.43	28.94	30.59	13.2	14.25	15.24	16.73
Oct	22.3	22.91	23.46	25.11	9.2	10.87	11.77	13.32
Nov	16	16.79	17.52	18.82	4.9	6.64	7.42	8.96
Dec	12.1	13.33	14.19	15.22	2	3.71	4.37	5.94
Ave.	21.4	23.3	24.1	25.3	8.6	11.0	11.8	13.2

Tmin, minimum temperature; Tmax, maximum temperature; Base, baseline

 

Table 2
Average monthly rainfall and effective rainfall in baseline and future scenarios in mm

Month	Baseline		RCP2.6		RCP4.5		RCP8.5
	Pre	Eff. pre	Pre	Eff. pre	Pre	Eff. pre	Pre	Eff. pre
January	28.2	26.9	28.7	27.4	35.1	33.1	42.7	39.8
February	52.2	47.9	35.2	33.2	35.8	33.8	39.8	37.3
March	57.7	52.3	37.6	35.3	45.8	42.4	51.9	47.6
April	46.6	43.1	41.8	39	30.6	29.1	46.9	43.4
May	23.7	22.8	20.8	20.1	24.3	23.3	30.3	28.9
June	3.2	3.2	3.9	3.9	3.5	3.5	6.9	6.8
July	3.4	3.4	1.9	1.9	2	2	3.2	3.2
August	6	6	3.5	3.5	5.5	5.4	3.7	3.7
September	3.9	3.9	28	26.7	23	22.2	21	20.3
October	7.4	7.3	25.2	24.2	23.2	22.4	28.3	27
November	21.2	20.5	19.9	19.2	19.8	19.2	20	19.3
December	15.3	15	36.3	34.2	33.6	31.8	38.2	35.9
Total	269	252.3	282.7	268.6	282.1	268.1	332.9	313.1

Pre., precipitation; Eff. pre., effective precipitation

 

Crop irrigation requirement (CIR)

The average monthly irrigation requirement was 62.65, 74.3, 76.3, and 75.3 mm/dec for the reference, RCP2.6, 4.5, and 8.5, respectively (Table 6).

Irrigation scheduling

According to the results, wheat required three irrigations at baseline, whereas it needed four irrigations for future scenarios (Table 7).

 

Table 3
ETo in the baseline and future scenarios under various RCPs

Month	Base	ETo (mm/day)
		RCP2.6	RCP4.5	RCP8.5
January	0.89	0.94	0.96	1
February	1.32	1.44	1.49	1.54
March	2.62	2.84	2.91	3.02
April	3.76	4.15	4.24	4.39
May	5.3	5.74	5.85	6.04
June	7.48	7.99	8.13	8.36
July	8.02	8.28	8.39	8.61
August	6.9	7.1	7.18	7.37
September	5.12	5.19	5.26	5.44
October	3.67	3.74	3.81	3.97
November	2.13	2.2	2.25	2.35
December	1	1.03	1.06	1.1
Average	4.02	4.22	4.29	4.43

 

Table 4
Growing stages, durations, and wheat growth stage coefficient (Kc) for base and future scenarios

Month	G. Stages	Duration (days)	Base	RCP2.6	RCP4.5	RCP8.5
Oct–Nov	init	30	0.7	0.7	0.7	0.7
Nov–Mar	deve	140	0.916	0.914	0.914	0.914
Apr–May	mid	40	1.16	1.16	1.16	1.16
Jun	end	30	0.69	0.6875	0.69	0.6875
Tot./Ave		240	0.87	0.87		0.87

G. stages., growing stages

 

Table 5
Variations in the projected wheat ETc under RCPs relative to baseline values

G. M	Base		RCP2.6		RCP4.5		RCP8.5
	ETc	ETc	ETc	ETc	ETc	ETc	ETc	ETc
	(mm/day)	(mm/dec)	(mm/day)	(mm/dec)	(mm/day)	(mm/dec)	(mm/day)	(mm/dec)
Nov	2.72	25.7	2.81	26.5	2.87	27.2	2.99	28.3
Dec	2.25	23.2	2.32	23.9	2.38	24.6	2.49	25.7
Jan	2.36	24.5	2.48	25.8	2.56	26.6	2.67	27.7
Feb	3.98	36.4	4.1	37.5	4.49	41.1	4.4	40.2
Mar	8.08	84	8.79	91.4	9.01	93.7	9.34	97.2
Apr	13.05	130.5	14.32	143.2	14.66	146.6	15.17	151.7
May	19.17	198.9	20.75	215.3	21.18	219.7	21.84	226.5
Jun	20.99	209.9	22.36	223.6	22.79	227.9	23.37	233.7
Ave.	9.075	91.63	9.74	98.4	9.99	100.9	10.28	103.87
Total		733.1		787.2		807.4		831

G.M., growing months; mm/dec., mm per decade

 

Table 6
Variations of projected wheat CIR under RCPs relative to baseline values

G.M	CIR (mm/dec)
	Base	RCP2.6	RCP4.5	RCP8.5
Nov	17.5	13.5	14.1	15.4
Dec	2.5	0	0.2	0
Jan	0	1	0	0
Feb	0	4.3	7.2	3.6
Mar	19.7	55.9	51.3	49.6
Apr	73.1	104.2	117.3	108.5
May	179.5	195	196.5	197.9
Jun	208.9	219.8	224.3	226.9
Ave.	62.65	74.2125	76.3625	75.2375
Total	501.2	593.7	610.9	601.9

G.M., growing months

 

Table 7
The time of irrigation, NIWR, and GIWR under RCPs for the baseline and future scenarios (1990–2100)

Date	Day		Stage		NIWR (mm)		GIWR (mm)
Baseline
6-May	177		Mid		120.1		171.6
25-May	196		Mid		115.7		165.3
10-Jun	212		End		122.4		174.9
8-Jul	End		End
		Total		358.2		511.8
RCP2.6
15-Apr	156		Dev		108.3		154.8
10-May	181		Mid		116.4		166.3
28-May	199		Mid		119.2		170.3
13-Jun	215		End		132.3		188.9
8-Jul	End		End
		Total		476.2		680.3
RCP4.5
14-Apr	155		Dev		107.6		153.8
9-May	180		Mid		120.3		171.8
27-May	198		Mid		117.8		168.3
11-Jun	213		End		126.5		180.7
8-Jul	End		End
		Total		472.2		674.6
RCP8.5
19-Apr	160		Dev		112.9		161.2
12-May	183		Mid		118.8		169.8
29-May	200		Mid		118.6		169.4
13-Jun	215		End		128.5		183.6
8-Jul	End		End
		Total		478.8		684

NIWR, net irrigation requirements; Gr. Irr., growth irrigation requirement

 

DISCUSSION

Climatic variables

Table 3 illustrates the increase in maximum temperature (T_max) and minimum temperature (T_min) under RCP2.6, 4.5, and 8.5 compared to the baseline. The average increases in Tmax by 2100 were projected as 1.89, 2.65, and 3.92°C under RCP2.6, 4.5, and 8.5, respectively (Table 8). Furthermore, annual rainfall and effective rainfall increased under RCP2.6, RCP4.5, and RCP8.5; it was projected to shift from the wheat-growing months (Jan–Jun) to Sep–Dec (Table 8, Figure 3).

Temperature increases have been reported in the study area by Aich et al. (2017), Hassanyar et al. (2017), UNDP (2017) and NEPA and UNEP (2018).

Reference evapotranspiration (ETo)

The mean monthly ETo values for the reference and future scenarios are shown in Table 3, Table 9, and Figure 4. An increase in ETo from March to July could be explained by the increase in temperature and decrease in rainfall, while the decrease in ETo from August to January could be due to the decrease in temperature and increase in rainfall. The average monthly ETo increased by 4.02, 4.22, 4.29, and 4.43 mm/day for the reference, RCP2.6, RCP4.5, and RCP8.5, respectively (Figure 4; Table 9). The monthly increase in ETo was 4.98, 6.72, and 10.20% according to RCP2.6, 4.5, and 8.5, respectively, compared to the baseline (Figure 5; Table 9).

An increase in potential evapotranspiration in the central zone was projected (UNDP, 2017). Many reports have shown an increase in ETo in other regions: Gabr (2023) reported that ETo would increase for the periods 2023–2080 and 2081–2100 by 5.6 and 10.9%, respectively, under RCP8.5 in Egypt (Gabr, 2023). Similar to Tang et al. (2018), Zhou et al. (2017) predicted an increase in ETo of 4–7% by 2100 in China. In Saudi Arabia, Chowdhury et al. (2016) and El-Rawy et al. (2023) projected an increase in ETo of about 6% by 2050 and 12.0% by 2100, respectively.

 

 

Table 8
T_max and T_min (°C), rainfall, and effective rainfall (average, range, difference, change, and total) for the scenarios under RCPs

	Baseline	RCP2.6	RCP4.5	RCP8.5
	Maximum temperature (°C)
Average	21.4	23.29	24.05	25.32
Range	10.2 (Jan) – 32.1 (Jul)	12.26 (Jan) – 33.32 (Jul)	13.34 (Jan) – 33.88 (Jul)	14.28 (Jan) – 35.06 (Jul)
Difference	–	1.89	2.65	3.92
Change (%)	–	8.83	12.38	18.34
	Minimum temperature (°C)
Average	8.58	10.98	11.79	13.20
Range	−0.2 (Jan) – 16.7 (Jul)	2.36 (Jan) – 18.49 (Jul)	3.37 (Jan) – 19.25 (Jul)	4.91 (Jan) – 20.43 (Jul)
Difference	–	2.39	3.21	4.61
Change (%)	–	27.88	37.44	53.81
	Precipitation (mm/month)
Average	22.41	23.56	23.50	27.75
Range	3.2 (Jun) – 57.7 (Mar)	1.9 (Jul) – 41.8 (Apr)	2 (July) – 45.8(Mar)	3.2 (July) – 51.9 (Mar)
Difference	–	1.16	1.16	5.34
Change (%)	–	5.2	4.98	23.84
Total (mm/year)	269	282.7	282.1	332.9
	Effective rainfall (mm/month)
Average	21	22.38	22.35	26.1
Range	3.2 (Jun) – 52.3 (Mar)	1.9 (Jul) – 39 (Apr)	2 (July) – 42.4 (Mar)	3.2 (July) – 47.6 (Mar)
Difference	–	1.36	1.32	5.05
Change (%)	–	6.46	6.3	24.13
Total (mm/year)	252.3	268.6	268.1	313.1

 

 

 

Table 9
Average, range, difference, and change (%) in ETo, CWR, and CIR for the baseline and future scenarios under RCP2.6, RCP4.5, and RCP8.5

	Baseline (1990–2020)	Future (2025–2100)
		RCP2.6	RCP4.5	RCP8.5
	ETo (mm/day)
Average	4.02	4.22	4.29	4.43
Range	0.89 (Jan)– 8.02 (July)	0.94 (Jan)– 8.39 (July)	0.96 (Jan)– 8.39 (July)	1 (Jan)– 8.61 (July)
Difference	–	0.2	0.27	0.41
Change (%)	–	4.98	6.72	10.2
	CWR (mm/dec)
Average	91.63	98.4	100.9	103.87
Range	23.2 (Dec)– 209.9 (Jun)	23.9 (Dec)– 223.6 (Jun)	24.6 (Dec)– 227.9 (Jun)	25.7 (Dec)– 233.7 (Jun)
Difference	–	15.31	20.97	28.16
Change (%)	–	7.16	9.81	13.18
Total (mm)	733.1	787.2	807.4	831
	CIR (mm/dec)
Average	62.65	74.21	76.36	75.23
Range	0 (Jan)– 208.9 (Jun)	0 (Dec)– 219.8 (Jun)	0 (Jan)– 224.3 (Jun)	0 (Jan)– 226.9 (Jun)
Difference	–	92.5	109.7	100.7
Change (%)	–	18.46	21.88	20.09
Total (mm)	501.2	593.7	610.9	601.9

 

Crop water requirement (CWR)

Compared to the baseline period (1990–2020), ETc was projected to increase in the future (2025–2100). With respect to the results, the variation changes in CWR were predicted to increase during growing months and stages (Figure 6) by 15.31, 20.97, and 28.16 mm/dec under RCP2.6, 4.5, and 8.5, respectively (Table 9). With regard to higher temperatures, solar radiation, and lower rainfall during the months of March, April, and May, the average values of ETc were the highest (Figure 7). In these months, winter wheat requires more water. The total amount of ETc (CWR) was recorded as 733.1 mm, and it was predicted to increase by 787.2, 807.4, and 831 mm under RCP2.6, 4.5, and 8.5, respectively, in the future (Figure 8). Due to the reduction in crop physiological activities at the end of the season, the CWR decreased in June (Paymard et al., 2019).

 

 

 

 

An increase in ETc puts more stress on crop production in dry regions. Increases in the ETc have been reported by studies around the globe. Chowdhury et al. (2016) reported an increase in ETc of 5.3–9.6% in mid-west Saudi Arabia from 2011 to 2050. Paymard et al. (2019) projected an increase in ETc by 26.24% under RCP8.5 in Sabzawar, Iran, by 2085. According to Kambale et al. (2023), CWR is sensitive to both the highest and lowest temperatures, although it is less sensitive to the sunshine duration than it is to temperature and wind speed. According to Shahid (2011), ETc may rise by 5.8% by 2050, and Mainuddin et al. (2014) predicted that, as a result of rising temperatures, ETc would rise by roughly 1–3 and 3–5% by that time. According to Chowdhury et al. (2016), a temperature increase of roughly 1°C is expected in arid environments, increasing the CWR by 2.9%. Jia et al. (2022) noted that the CWR was most sensitive to wind speed (Jia et al., 2022).

Crop irrigation requirement (CIR)

Table 9 and Figure 9 show the CIR for the reference and future scenarios in the growing months. According to the results, CIR is 62.65, with a range of 208.9 mm in June to 0.0 in February and January for the reference scenario. The average CIR was predicted to be 74.21, 76.36, and 75.23 mm/dec for RCP2.6, 4.5, and 8.5, respectively (Table 9). Furthermore, CIR was projected to increase in March, April, and May due to increases in ETo, ETc, and temperature and a decrease in precipitation (Figure 9). The amounts of change in CIR were 92.5, 109.7, and 100.7 mm for RCP2.6, 4.5, and 8.5, respectively, compared to the reference scenario (Table 9). An increase in the temperature, wind speed, sunshine, and radiation and a decrease in precipitation (Figure 3, Table 1, Table 2, and Table 3) caused an increment in ETo, ETc, CWR, and CIR due to an increase in crop transpiration and soil evapotranspiration. According to the baseline, wheat irrigation water demands have been predicted to increase for the central zone by 1.75 BCM (billion M³) by 2056 (UNDP, 2017). Gabr (2023) estimated an increase in CIR for all studied crops, including wheat, by 9.7% under RCP8.5 by 2100. Mainuddin et al. (2014) reported an increase in CIR for crops, such as wheat, of about 8% by 2050.

Irrigation scheduling

Table 7 shows the irrigation timing for the wheat crops cultivated in the study area. The results showed that winter wheat needed to be irrigated three times in the baseline scenario but would need to be irrigated four times under the RCPs. At baseline, it needed to be irrigated three times, on May 6 and 25 and June 10, with an NIR of 358.2 mm. Under RCP2.6, the wheat field would need to be irrigated four times, on April 14 and 10, May 28, and June 13, with a total NIR of 476.2 mm. Wheat would need to be irrigated four times on April 14, May 9 and 27, and June 11 under RCP4.5, with an NIR of 472.2 mm, and on April 19, May 12 and 29, and June 13 June under RCP8.5, with a total NIR of 478.8 mm. The total CIR showed variation among RCP2.6, 4.5, and 8.5 by 593.7, 610.9, and 601.9 mm/dec, respectively, compared to the baseline at 501.2 mm/dec. Moreover, net irrigation would increase in the projection period by 476.2 mm under RCP2.6, 472.2 mm under RCP4.5, and 478.8 mm under RCP8.5, compared to the baseline at 358.2 mm.

 

 

 

 

CONCLUSIONS

Arid regions are most affected by climate change. Climate change negatively impacts cropping patterns and indicators, such as ETc, CWR, CIR, and irrigation scheduling. The results showed that temperatures would increase in the future under all scenarios.

Moreover, it was projected that there will be an increase in precipitation under RCPs by 2100, but it will shift from the growing months (January–June) to September–December.

Furthermore, an increase in average temperature and wind speed and a decrease in precipitation led to an increase in ETo, ETc, and CWR. An increase in CWR led to an increase in CIR, especially for wheat. CIR will increase by 18.46, 21.88, and 20.09% under RCP2.6, 4.5, and 8.5, respectively, compared to the baseline. Moreover, the wheat field required 3 irrigations of 358 mm in the baseline scenario, whereas it would require 4 irrigations in the future, with 476.2, 472.2, and 478.2 mm under RCP2.6, 4.5, and 8.5, respectively.

The findings of this study will help decision makers to choose adaptation strategies against water stress and climate crises.

Furthermore, the projection of CWR will help develop adaptive policies for agricultural water management and operations under climate change. Several strategies, such as water management, water conservation techniques, sowing date adjustment, variety selection, cropping system patterns, irrigation systems, and other methods, can reduce the impact of climate change on CWR.

However, this study only assessed the impacts of climate change on winter wheat water requirements; further assessments of all crops that have an economic value to the country are needed. Moreover, the negative effects of climate change should be assessed across Afghanistan.

 

Author contributions: Conceptualization: RH, JAS; Methodology, Analysis, Histological analysis, Investigation, Resources, Data curation, Writing: RH; Review: JAS. All authors declare that they have read and approved the publication of the manuscript in this present form.

Funding: This study did not receive any external funding.

Conflicts of interest: The authors affirm that no conflicts of interest are associated with this publication.

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