Effects of Biochar on Irrigation Management and Water Use Efficiency for Three Different Crops in a Desert Sandy Soil

Abstract

This paper aimed at investigating if the application of biochar (BC) to desert sand (DS) from the United Arab Emirates (UAE), characterized by a very poor soil-water retention (SWR) and by a very low value of the maximum water available for crops (AW_max), could positively affect soil water balance, by reducing the irrigation needs (V_IRR) and improving the irrigation water use efficiency (IWUE) and the water use efficiency (WUE). The analysis was performed for three crops, i.e., wheat (Triticum aestivum), sorghum (Sorghum vulgare) and tomato (Lycopersicon esculentum). BC was applied to the DS at different fractions, f_BC (f_BC = 0, 0.091, 0.23 and 0.33). Drip irrigation was adopted as a highly efficient water saving method, which is particularly relevant in arid, water-scarce countries. Soil water balance and irrigation scheduling were simulated by application of the AQUACROP model, using as input the SWR measured without and with BC addition. The effect of BC was investigated under either a no-water stress (NWS) condition for the crops or deficit irrigation (DI). The results showed that the application of BC made it possible to reduce the predicted V_IRR and to increase the IWUE under the NWS scenario, especially for wheat and sorghum, with less evident benefits for tomato. When a deficit irrigation (DI) was considered, even at the lowest considered f_BC (0.091), BC counterbalanced the lower V_IRR provided under DI, thus mitigating the yield reduction due to water stress, and improved the WUE. The influence of BC was more pronounced in wheat and tomato than in sorghum. The results evidenced that the application of BC could be a potential strategy for saving irrigation water and/or reducing the effects of drought stress in desert sand. This means that biochar could be used a management option to promote local production and reduce the dependency on food import, not only in the UAE, but also in other countries with extremely arid climatic conditions and large extensions of sandy soils similar to the considered DS.

1. Introduction

Fresh water is an indispensable natural resource, which plays a vital role in the development of any country. Water scarcity originates not only from the physical constraints of available fresh water, but also from competition between different uses and from inefficient use and poor management (FAO, 2008). As a result, water allocation has become one of the most vexing problems faced by policy makers.

Agriculture accounts for roughly 70% of total freshwater withdrawals and for over 90% in the majority of the least developed countries [1]. Without improved efficiency strategies, agricultural water consumption is expected to increase by about 20% by 2050 [2]. As a consequence, the world could face a 40% global water deficit by 2030 under a business-as-usual scenario [3]. Water withdrawal for agricultural purposes accounts for about 75% of all usages in developing countries, and irrigation is widely criticized as a wasteful user of water, especially in the water-scarce regions [4]. Under these critical water availability conditions, a rational management of water is essential to enhance water productivity, i.e., the ratio between the agricultural benefit and the water supplied [5].

Drip irrigation has been increasingly preferred to other irrigation systems in order to save water, especially in arid and water-scarce countries. Drip irrigation makes it possible to increase crop yield (Y) per any drop of water supplied [6,7], thus maximizing the irrigation water use efficiency (IWUE), i.e., the ratio between Y and the supplied irrigation volume (V_IRR) [8].

Deficit irrigation (DI), i.e., application of water below the maximum crop transpiration (T_c) or evapotranspiration (ET_c) requirements, has been proposed as a valuable strategy when water availability represents the factor limiting the crop cultivation and yield [9]. The main goal of DI is to increase crop water productivity by reducing the amount of water applied to the crop [10]. DI makes it possible to reduce the irrigation needs and to divert the water saved for alternative uses, especially if practiced with a proper irrigation scheduling, i.e., with irrigation timings and amounts of irrigation water determined taking into account the processes occurring in the soil-plant-atmosphere (SPA) system. DI, in combination with drip irrigation, can be proposed as an irrigation strategy saving water, further improving crop productivity and water use efficiency [6,7].

A number of models simulating water transport and energy exchanges in the SPA system have been developed [11,12,13,14] and applied to assess irrigation scheduling under limited water availability conditions [15,16,17,18,19]. The application of these models makes it possible to obtain reliable irrigation and management scenarios if accurately measured soil hydraulic parameters are used as input [17]. Comparing the performance of the different simulated scenarios and selecting those providing the best results may help to save the time, the costs and the efforts needed to implement experimental investigations, and can be considered preliminary to field experiments aimed at further evaluating the simulated options and scenarios [17].

AQUACROP [20,21] is a crop growth model developed by the FAO Land and Water Division to address food security and to assess the effect of environment and management on crop production. The water-driven crop growth model used in AQUACROP assumes a linear relationship between yield (Y) and actual evapotranspiration (ET) [22,23]. One of the major advantages of the water-driven module is the opportunity to normalize the water use efficiency (WUE), i.e., the ratio between Y and ET for differences in climate, thus providing a wider applicability for a number of locations characterized by spatial and temporal variability [23,24]. Being a water-driven crop model, crop biomass and harvestable yield are simulated in response to available water (soil moisture and irrigation). Although constructed upon basic and complex biophysical processes, only a relatively small number of parameters are needed to adapt AQUACROP to different cases and crops. Often the integrated default input variables are sufficient and do not require additional fitting [25]. When additional variables are needed, they are mostly intuitive and can easily be determined using simple methods [11,13].

This robust field crop-water balance has been successfully tested for a wide range of crops and regions, and its database is still expanding through worldwide contributions. The crop parameters enclosed in AQUACROP as a result of validation and calibration in a number of water-scarce countries of the world are relative to: (i) irrigated winter wheat canopy cover, biomass and grain yield in the North China Plain [26], in Iran [27], in Italy [28] and in Morocco [29]; (ii) maize yield under variable irrigation in India [30], in Colombia [31] and in Iran [32]; (iii) deficit and full irrigation of tomato in Ghana [33], in Tunisia [28] and in Southern Italy [34]; (iv) drip irrigated cotton grown under full and deficit irrigation in Syria [35]; (v) irrigated cabbage in India [36]; (vi) irrigated potato in Spain [37]; (vi) quinoa in Bolivia [38]; (vii) sunflower in Southern Italy [39]; (viii) sugar beet under climate change conditions in Serbia [40]; (ix) sorghum in Kenia [41] and in Oman [42].

Biochar (BC) is a C-rich organic material, which is produced by the thermal decomposition of plant-derived biomass in partial or total absence of oxygen. Addition of BC to the soil has the potential to improve soil quality and C sequestration, mitigating the excess of C dioxide in the atmosphere [43]. Previous investigations proved that the addition of BC to sandy soils increased soil organic C [44] and had positive effects on soil physical properties such as porosity (θ_s), soil water retention and the maximum water available for crops, AW_max [44,45]. Several studies also showed that the application of BC was effective in reducing salinity stress by improving the soil’s physico-chemical and biological properties directly related to Na removal, Na adsorption ratio and electrical conductivity [46].

Sandy soils lacking sufficient organic matter to sustain microbial activity are widespread in various arid and semi-arid regions of the world, including India, Tunisia and Saudi Arabia, as well as the United Arab Emirates (UAE) [47,48,49]. These soils have a low capacity of water retention [50] and low values of the maximum water available for crops, AW_max, i.e., the difference between field capacity (θ_fc) and wilting point (θ_wp). Without any amendment, the agricultural production capacity of these soils is weak, and therefore a considerable amount of irrigation water is necessary to meet the crop water requirements.

Baiamonte et al. [51] found that the application of BC to desert sand (DS) from the UAE (Al-Foah area, border with Oman) increased soil porosity and enhanced the formation of textural and structural pores, especially increasing the amount of storage pores (pores with diameter D = 9.09 μm). BC also modified the portion of the soil pore size distribution associated with aggregation [52], increasing the soil aggregate stability [53,54]. The specific surface area (SSA) measured by Nitrogen adsorption measurements confirmed that BC modified the internal soil pores’ structure, increasing the number of structural, connected pores [55]. As a consequence, BC increased the values of saturated volumetric water content, θ_s, field capacity, θ_fc, and wilting point, θ_wp, enhancing the soil water retention and increasing the AW_max from a value of 19 mm/m for the DS without BC to values of 62, 103 and 109 when BC fractions, f_bc, equal to 0.091, 0.23 and 0.33, respectively, were mixed with the DS.

These results suggested that the application of BC could be a management option improving the soil’s structural and retention characteristics, as well as the AW_max of the DS. This could be of considerable potential interest not only for the Al-Foah area but also for other arid and/or semi-arid regions characterized by large extensions of sandy soils similar to that considered, with poor structural and retention characteristics.

The objective of this paper was to explore the hitherto little investigated application of BC to DS, as an amendment option improving soil water balance and irrigation management for three different crops, i.e., wheat (Triticum aestivum), sorghum (Sorghum vulgare) and tomato (Lycopersicon esculentum). Soil water balance and irrigation scheduling were simulated by AQUACROP using as input the soil’s hydrological parameters previously measured at f_bc = 0, 0.091, 0.023 and 0.33 [51]. The specific objective was to investigate if under drip irrigation, the application of BC to DS improved the irrigation water use efficiency (IWUE) and water use efficiency (WUE), under conditions of no water stress (NWS) for the crops and under deficit irrigation (DI).

2. Materials and Methods

2.1. Study-Area and Soil Characteristics

The sandy soil was taken from the dunes near Al Foah (long 55°50′E and lat 24°30′N), located north of Al Ain, a city in the Eastern Region of the Emirate of Abu Dhabi, which is one of the seven States of the UAE, with an area of about 64,000 km², most of which covered by Aeolian sand. Agricultural development is limited to a few small-scattered oases, such as the Liwa group and those at Al Ain. The latter, seven in Abu Dhabi State and two, including Buraimi, in Muscat territory, have always been the most important centers of agriculture in the region.

Climate is arid, with very high summer temperatures. The interior desert region has hot summers with temperatures rising up to about 50 °C. Annual rainfalls are very scarce and vary between 15 mm and 78 mm, with January and February being the rainy months.

The desert sand (DS) considered in the AQUACROP application was characterized by a content in clay, silt and sand equal to 2.6, 1.9 and 95.6%, respectively, a low organic matter content (0.54 g/kg) and a Ca and Mg content equal to 12 and 4 mg/kg, respectively. A weak structural development was observed, as usual in single grained sandy soils. The DS was classified as Aridisol according to the USDA [56,57].

2.2. Model Description

The AQUACROP model, developed by the FAO Land and Water Division, is a water-driven model that can be used as a planning and support tool for management decisions in both irrigated and rainfed agriculture [20,58]. The model computes the soil water balance in the root zone according to Allen et al. [59,60], calculating the root zone depletion, D_r, at the end of every day, as:

$$D_{r,i}=D_{r,i-1}-{\left(R-RO\right)}_i-V_{IRR}-C{R}_i+E{T}_i+D{P}_i$$

(1)

where the subscripts i and i − 1 refer to the current day and to the previous day, respectively, R (mm) is the rainfall depth, RO (mm) is the runoff, V_IRR (mm) is the irrigation depth, CR (mm) is the capillary rise, ET (mm) is the actual crop evapotranspiration and DP (mm) is deep percolation. The parametric equations described by Liu et al. [61] are used to calculate CR and DP. The water balance is highly affected by runoff, RO, [62], which in turn is highly influenced by the soil’s hydrological characteristics [63,64]. AQUACROP estimates RO by using the curve number approach [65], and splits the maximum ET component, ET_c, in maximum soil evaporation, E_s (mm), and maximum crop transpiration, T_c (mm), which are estimated as:

$$E_s=K_r\left(1-CC\right)K_{e}E{T}_0$$

(2)

$$T_c=CC K_{cTr}E{T}_0$$

(3)

where ET₀ (mm) is the reference evapotranspiration [59], CC (%) is the crop development value, K_e is the maximum soil evaporation coefficient (dimensionless), K_r is the evaporation reduction coefficient (0–1) and K_cTr is the standard crop transpiration coefficient (dimensionless) when CC = 100%. The actual transpiration, T_a (mm), is calculated as:

$$T_a=CC K_s{K}_{cTr}E{T}_0$$

(4)

where K_s is a water stress coefficient ranging between 0 and 1 [59,66]. Further details on calculating T_a and E_s are provided by Raes et al. [67].

In AQUACROP the ground dry biomass, B (kg ha⁻¹), is computed as the product of the season-cumulated ratio T_a/ET₀ and the adjusted biomass (water) productivity BWP* (g m⁻²), that takes into account the actual atmospheric CO₂ concentration [67], whereas the crop yield, Y (kg ha⁻¹), is predicted by B as:

$$Y=f_{HI} H{I}_{0}B$$

(5)

where HI₀ is the reference harvest index, which indicates the harvestable proportion of biomass, and f_HI is an adjustment factor computed by the model and related to five water stress factors, i.e., inhibition of leaf growth, inhibition of stomata, reduction in green canopy duration due to senescence, reduction in biomass due to pre-anthesis stress and pollination failure.

The main input data required by AQUACROP are:

Climatic data—daily maximum and minimum air temperature, T_max (°C) and T_min (°C), daily rainfall (R), reference evapotranspiration (ET₀) and mean annual CO₂ (ppm) concentration in the atmosphere.

Crop data—crop calendar with reference to the emergence, maximum coverage, maturity and senescence stages; maximum value of K_cTr; minimum and maximum root depths and roots expansion shape factor; the parameters of the canopy cover (CC) curve; the canopy growth coefficient CGC (% degree day⁻¹) and the canopy decline coefficient CDC (% degree day⁻¹); the parameters used to compute yield, BWP* and HI₀ defined above; the water stress coefficients relative to canopy expansion, stomatal closure, early canopy senescence and aeration stress due to water logging.

Soil data—number of layers (maximum 5) and soil layer depth, d (m), and for each layer volumetric water content at saturation, θ_s (m³ m⁻³), field capacity, θ_fc, and wilting point, θ_wp.

Irrigation—irrigation system; scheduling parameters (time and depth criteria), in case of irrigation scheduled by the model, or time and volume of each water supply in case of user-imposed irrigation depth. The irrigation system can be selected as “sprinkler”, “surface” or “drip”. In the latter case, an adjustment coefficient (%) for partial soil wetting can be used to take into account the actual irrigated area.

Field and management data—salinity, soil fertility, mulching and runoff reduction practices.

2.3. Data Collection and Simulation Scenarios

Daily rainfall (R) and daily maximum and minimum air temperature (T_max and T_min), recorded from 1 Jan 2006 to 31 Dec 2007 at Al Buraimi weather station (24.233° lat, 55.783° long, 299 m a.s.l.), were used to calculate the reference evapotranspiration ET₀ [60], using the Hargreaves equation [68], and to carry out the AQUACROP simulations. Al Buraimi is located in Oman at the border with the UAE, and is near to the Al Foah area. Data from the Al Buraimi weather station were used because they fully covered the investigated period. Figure 1 illustrates R and ET₀, characterizing the climatic conditions of the study-area, where only a small number of rainfall events occurred in 2006 (114 mm with 26 rainfall events) and in 2007 (58 mm with 15 rainfall events), and reference evapotranspiration values that achieved about 9 mm/day in the summer season.

Biochar, BC, from forest biomass (Abies alba M., Larix decidua Mill., Picea excelsa L., Pinus nigra A. and Pinus sylvestris L., mixed in equal relative proportions), pyrolyzed at 450 °C for 48 h, had been used to amend the DS and to measure the soil water retention [51]. The BC fractions considered by Baiamonte et al. [51] were: f_BC = 0.0 (soil only), f_BC = 0.091, 0.23, 0.33 and 1.00 (BC only), with the biochar fraction, f_BC, defined as:

$$f_{BC}=\frac{P_{BC}}{P_s+P_{BC}}$$

(6)

where P_BC (g) and P_S (g) are the BC and DS weights, respectively.

Table 1. Experimental soil hydrological parameters [51].

Soil	Layer Depth, Z (m)	θs (cm3 cm−3)	θfc (cm3 cm−3)	θwp (cm3 cm−3)	AWmax (mm m−1)
fBC = 0	0.4	0.409	0.046	0.027	19
fBC = 0.091	-	0.559	0.098	0.036	62
fBC = 0.23	-	0.675	0.152	0.049	103
fBC = 0.33	-	0.724	0.163	0.054	109

reports the saturated water content, θ_s, together with the θ_fc and θ_wp values previously measured for the DS without BC and with BC addition [51] and used as input to simulate the soil water balance and irrigation scheduling under drip irrigation. These hydraulic parameters were determined by the High Energy Moisture Characteristic (HEMC) technique [69,70], and therefore took into account the structural changes occurring in the soil-BC mixtures after BC addition. The importance of using hydraulic parameters determined with measurement techniques detecting even small structural changes as input to physically-based simulation models, in order to obtain reliable model predictions, has been evidenced in previous investigations (17). This also means that the ET values predicted by AQUACROP using these hydraulic parameters can be considered reliable.

The crops chosen for the simulations were wheat, sorghum and tomato, which are of economic interest for many countries all over the world. Production of wheat is negligible in the UAE due to extreme heat, low rainfall and barren desert soil, but increasing the local production could be of interest for the UAE. Abu Dhabi has two large wheat farms at Al Ain, and experimental farms at Rawaya and Mazaid (near Al Ain) were designed to encourage local Bedouins to take up settled farming. As part of a research project, about 100 varieties of wheat used for the production of flour to make bread were cultivated in Al Ain in 2013, and the initial results were positive [68,71].

Sorghum is predominantly grown in semi-arid and arid agro-ecological areas and is considered one of the crops that can also be cultivated for large-scale production, despite the harsh climate conditions of the UAE. Sorghum cultivation is being investigated on a trial basis at the experimental farms of UAE University. Tomato is one of the most widely cultivated crops in the world, including UAE, where the government policies are supporting tomato cultivation, enhancing locally-grown production for the UAE consumers [71].

Table 2. Crop parameters considered in ACQUACROP.

Parameter Description	Units	Crops
		Wheat	Sorghum	Tomato
Time from sowing/transplanting to emergence	days	9	6	5
Time from sowing/transplanting to maximum rooting depth	days	51	65	65
Time from sowing/transplanting to start senescence	days	94	69	111
Time from sowing/transplanting to maturity	days	126	79	119
Time from sowing/transplanting to flowering	days	71	60	40
Base temperature below which crop development does not progress	°C	0	8	7
Upper temperature above which crop development no longer increases temperature	°C	26	30	32
Soil water depletion factor for canopy expansion (Upper threshold)	-	0.2	0.15	0.15
Soil water depletion factor for canopy expansion (Lower threshold)	-	0.65	0.7	0.55
Soil water depletion fraction for stomatal control (Upper threshold)	-	0.65	0.75	0.5
Soil water depletion factor for canopy senescence (Upper threshold)	-	0.7	0.7	0.7
Minimum air temperature below which pollination starts to fail	°C	5	10	10
Maximum air temperature above which pollination starts to fail	°C	35	40	40
Crop coefficient when canopy is full but prior to senescence	%	1.1	1.07	1.1
Minimum effective rooting depth (m)	m	0.1	0.1	0.15
Maximum effective rooting depth (m)	m	0.4	0.4	0.4
Building up of Harvest Index starting at flowering	days	54	19	63

reports the main default parameters implemented in AQUACROP and used as inputs to carry out the simulations for the three considered crops.

The length of crop development stages, T_LCD (day), i.e., the time from sowing/transplanting to emergence, to maximum rooting depth, to the beginning of senescence and to maturity, were adjusted in order to take into account the site-specific conditions [59].

For each crop, a soil water balance was performed daily by using a simulation period between sowing/transplanting and the end of maturity. Since the accuracy of the soil water balance depends on the soil water content (θ) at the starting day, information not available, the first rainy day following sowing/transplanting, characterized by a rainfall depth > 15 mm, was chosen as starting date. This made it possible to start the simulations for all the crops from the same initial soil conditions at the field capacity, θ_fc.

Simulations were carried out in order to investigate the effects (i) of the different f_BC, and (ii) of the different options of BC incorporation into soil layers of different depths, (Z_DS+BC), on soil-water balance, on irrigation efficiency and on the crop response to water.

Incorporation of BC into the soil was assumed to be technically feasible if carried out with plowing, before sowing, as also shown by Moiwo et al. [72].

Although values of Z_DS+BC equal to 30 or 40 cm are likely to be non-sustainable possibilities, because of the considerable amounts of BC that should be used to amend the soil with these options, these values were also considered as a theoretical possibility of incorporating BC into the deepest soil layers. In case of low-cost BC obtained in farms by waste residues, this incorporation could be done during tillage before sowing when soil preparation is carried out, and could be justified by the benefits in terms of the increase in soil fertility and related crop productivity, in the long term [49].

Simulations were carried out by assuming drip irrigation [8,73] under two different management scenarios. In the first scenario, a condition of “no water stress for the crop” (NWS) was simulated by an irrigation scheduling bringing the soil water content at θ_fc, when 65% of the readily available water (RAW) was depleted from the soil. RAW was calculated as a p fraction of AW_max equal to 0.65 [59]. Therefore, for each crop, this scenario determined T/T_max ratios equal to 1, evidencing the effect of BC on the predicted irrigation water needs (V_IRR) and on the irrigation scheduling when the objective was to avoid any water stress for the crop, obtaining the maximum yield, Y.

Instead, the second scenario simulated a condition of water stress for the crops by scheduling a deficit irrigation (DI), supplying the soil-crop system with a total amount of irrigation water, V_IRR, restoring 70% of the maximum crop evapotranspiration, ET_C. Under this DI condition, V_IRR did not vary, for the same crop, at changing f_BC and Z_DS+BC, and the effect of BC on crop yield, Y, and on the WUE could instead be evaluated for each crop.

Table 3. Simulation scenarios for the three considered crops.

Simulation Scenarios	Wheat (T. aestivum)	Sorghum (Sorghum vulgare)	Tomato (Lycopersicon esculentum)
Date of planting/seeding	1 December 2006	24 February 2006	15 January 2006
Start date of simulation	2 December 2006	25 February 2006	24 February 2006
End date of simulation	6 April 2007	14 May 2006	13 May 2006
Length of Crop Development stage, TLCD (days)	126	79	119
Desert sand (DS) and Biochar (BC) depths
DS (cm)	-------------------------------- 40 --------------------------------
ZDS+BC (cm)	----------------------- 40 30 20 10 -----------------------
ZDS (cm)	------------------------ 0 10 20 30 -----------------------
Irrigation management scenarios
Deficit irrigation, DI, (Number-Depth in mm)	38-5	33-8	37-8
No water stress, NWS, (Time and Depth Criteria)	65% of RAW depleted, irrigation depth back to the field capacity

reports some information related to the simulated scenarios. The DS option refers to soil water balance simulations carried out for the soil without BC, whereas the “Z_BC+DS” and Z_DS are the different options of incorporating the three BC fractions (f_BC = 0.091, f_BC = 0.23 and f_BC = 0.33) into soil layers of different depths.

The performance of each scenario, with the options of BC incorporation, on the soil-crop system and on irrigation management, was assessed by considering the outputs provided by AQUACROP, as well as by calculating the irrigation water use efficiency index, IWUE (kg m⁻³), as:

$$IWUE=100\frac{Y}{V_{IRR}}$$

(7)

where Y (t ha⁻¹) is the crop yield and V_IRR (mm) is the total irrigation volume.

For the NWS scenario, the dimensionless ratios V_IRR/V_IRR,DS and N_IRR/N_IRR,DS, obtained by dividing the total irrigation volumes, V_IRR, and the number of irrigations, N_IRR, by the corresponding values obtained simulating only sand without BC, were considered.

For the DI scenario, the ratios between the actual transpiration, T, the maximum transpiration, T_max, the yield, Y, the maximum yield, Y_max, the irrigation water use efficiency, IWUE, and the water use efficiency, WUE, were analyzed.

3. Results and Discussion

With reference to the three crops and to the NWS scenario, for each of the considered f_BC (f_BC = 0.091, 0.23 and 0.33), The main outputs of the soil water balance simulated by AQUACROP for the different Z_DS+BC, including the results obtained for the DS, without BC, are reported in

Table 4. Soil layering for different biochar fractions, f_BC, and main outputs obtained using AQUACROP for the three considered crops. Data refer to a soil-water balance simulation with a “no water stress irrigation” management (NWS).

Soil Layer	ZDS+BC (cm)	ZDS (cm)	ET0 (mm)	VIRR (mm)	T (mm)	E (mm)	T/Tmax (-)	#IRR (-)	hIRR (mm)	Bmass (t/ha)	Hi (%)	Y (t/ha)	WUE (kg/m3)	IWUE (kg/m3)
Wheat
DS	-	40	299.6	251.3	240.9	20.3	1	54	4.7	14.5	48	6.96	2.59	2.77
fBC 0.091	40	-		216.4		16.3		18	12.8				2.64	3.22
	30	10		226.0		16.9		21.0	10.8				2.63	3.08
	20	20		235.1		18.8		27	8.7				2.61	2.96
	10	30		243.5		19.8		36	6.8				2.61	2.86
fBC 0.23	-	40		214.6		17.6		12	19.5				2.62	3.24
	30	10		224.4		16.9		14	16.0				2.62	3.10
	20	20		234.9		20.3		19	12.4				2.59	2.96
	10	30		235.2		19.6		27	8.7				2.60	2.96
fBC 0.33	40	-		205.8		17.8		11	20.4				2.62	3.38
	30	10		220.0		18.8		13	16.9				2.61	3.16
	20	20		234.4		20.0		18	13.0				2.59	2.97
	10	30		234.1		20.0		26	9.0				2.60	2.97
Sorghum
DS	-	40	402.5	343.8	293	43.7	1	63	5.5	17.1	45	7.10	2.17	2.11
fBC 0.091	40	-		309.2		27.2		25	12.9				2.20	2.27
	30	10		315.8		28.8		26	12.1				2.19	2.21
	20	20		328.9		33.6		31	10.6				2.18	2.15
	10	30		331		35.8		46	7.2				2.14	2.13
fBC 0.23	40	-		302.3		25.7		16	20.1				2.22	2.32
	30	10		308.8		25.7		17	18.2				2.18	2.24
	20	20		326		29.8		24	13.6				2.19	2.15
	10	30		327.8		36		31	10.6				2.14	2.14
fBC 0.33	40	-		297.3		26.4		15	21.2				2.20	2.35
	30	10		303.8		27.4		17	19.0				2.22	2.32
	20	20		316.9		29.4		22	14.4				2.18	2.21
	10	30		331.1		36.8		30	11.0				2.14	2.11
Tomato
DS	-	40	397.1	394.3	343.5	44.3	1	75	5.3	12.4	63	7.84	2.03	1.99
fBC 0.091	40	-		369.0		28.7		35	10.54				2.11	2.13
	30	10		379.4		33.3		44	8.62				2.09	2.07
	20	20		383.5		35.4		54	7.10				2.07	2.05
	10	30		383.4		35.0		63	6.09				2.08	2.05
fBC 0.23	40	-		364.3		25.8		25	14.57				2.13	2.15
	30	10		367.0		27.5		28	13.11				2.12	2.14
	20	20		376.2		30.6		35	10.75				2.10	2.09
	10	30		384.6		37.2		55	6.99				2.07	2.04
fBC 0.33	40	-		364.6		25.5		24	15.19				2.13	2.15
	30	10		366.0		26.9		27	13.56				2.12	2.14
	20	20		375.4		30.7		34	11.04				2.10	2.09
	10	30		383.5		36.2		53	7.24				2.07	2.05

. The reference evapotranspiration, ET₀ (mm), during the simulation period, the total irrigation volume, V_IRR (mm), predicted by the model, the actual crop transpiration, T (mm), which was equal to the maximum (T = T_max), the actual soil evaporation, E (mm), the number of irrigations, #IRR, and the average irrigation water volume, h_IRR (mm), the crop biomass, B_mass (t ha⁻¹), the cumulative Harvest Index, H_i (%), the crop yield, Y (t ha⁻¹), the water use efficiency, WUE (kg m⁻³), and the irrigation water use efficiency, IWUE (kg m⁻³), with the latter calculated per Equation (7), are also reported in Table 4.

As can be seen in Table 4, BC positively affected the soil water balance, by decreasing the amount of V_IRR necessary to fully meet the crop water requirement and to obtain the maximum Y, with V_IRR values that decreased at increasing f_BC and Z_DS+BC, compared to DS.

The positive effects of BC on soil water balance were the consequence of the improved retention characteristics, which in turn were the consequence of the improved structural and aggregation soil properties due to BC addition. BC increased the number of pores with diameter = 9.09 μm (storage pores), improved the aggregate stability and increased the number of internal pores [51]. Since AW_max increased at increased f_BC, indicating a greater ability of the soil to store water as a consequence of BC addition, this explains why lower V_IRR values were predicted when BC was applied at higher f_BC and application depths (Z_DS+BC).

These results indicated that the addition of BC to the DS can be considered a water saving strategy. The decrease in the V_IRR values was the consequence of the increase in soil porosity and water retention due to BC [51], as can be illustrated by analyzing the temporal variation of the volumetric water content, θ, and of the irrigation scheduling obtained by AQUACROP for the DS without BC and after BC addition.

Figure 2a–c shows that, for the three crops, when the soil water balance was simulated for the DS, without BC, θ was almost constant during the entire simulation period and close to the value of 0.046, corresponding to the DS field capacity, θ_fc (Table 1). Instead, when BC was applied to the DS at f_BC = 0.23 and Z_DS+BC = 30 cm (Figure 2d–f), a larger variability occurred in the θ values predicted before and after irrigation. This was the consequence of the increased field capacity, θ_fc (from 0.046 to 0.152 cm³), wilting point, θ_wp (from 0.027 to 0.049 cm³), and AW_max, determined by the BC application (Table 1).

The increase in AW_max from 19 mm/m to 103 mm/m after BC addition at f_BC = 0.23 indicated a considerably enhanced soil ability to act as a reservoir for water and to provide a higher amount of water to the crops. This explains why the simulation carried out with AW_max = 103 mm/m, compared to that carried out with AW_max = 19 mm/m, determined an irrigation scheduling predicting an amount of V_IRR equal to 224.4 mm, for wheat, against the V_IRR of 251.3 mm obtained for the DS. For sorghum, the predicted V_IRR was equal to 308.8 mm, against the value of 343.8 mm, and for tomato the predicted V_IRR was equal to 367.0 mm, against the value of 394.3 mm. Thus, for the NWS scenario, and for f_BC = 0.23 and Z_DS+BC = 30 cm, the reductions in the V_IRR values were equal to 10.7%, 10.2% and 6.9% for wheat, sorghum and tomato, respectively, with the minimum effect on tomato and comparable effects on wheat and sorghum. These results indicated that, under the climatic conditions explored in this investigation, the addition of BC to the DS made it possible to save water, showing the positive role of BC in improving irrigation management for the considered DS and for the three crops.

A further positive effect of BC was the reduction in the number of the irrigation events scheduled by the model, #IRR (14 against 54 for wheat, 17 against 63 for sorghum and 28 against 75 for tomato), with these results indicating that a certain amount of energy can be saved when irrigation is carried out after BC application.

For wheat, similar V_IRR values were obtained, for the same Z_DS+BC (30 cm), with f_BC = 0.091 (V_IRR = 226.0 mm) and f_BC = 0.33 (V_IRR = 220.0 mm). Other options implying lower f_BC and Z_BC+DS values also determined reductions in the V_IRR and #IRR values (Table 4). However, also in these other cases, reductions in the V_IRR and #IRR values due to BC addition were always maximal for wheat, followed by sorghum and by tomato. For wheat, the reductions in V_IRR ranged from 18% (for f_BC = 0.33 and Z_DS+BC = 40 cm) to 3.1% (for f_BC = 0.091 and Z_DS+BC = 10 cm), for sorghum from 13.5% (f_BC = 0.33 and Z_DS+BC = 40 cm) to 3.7% (f_BC = 0.091 and Z_DS+BC = 10 cm) and for tomato from 7.5% (f_BC = 0.33 and Z_DS+BC = 40 cm) to 2.8% (f_BC = 0.091 and Z_DS+BC = 10 cm). These results indicated that (i) the maximum BC benefit was found at the maximum considered depth of 40 cm, agreeing with the experimental results of Moiwo et al. [72], who found an increasing BC benefit with increasing application depth, (ii) wheat was the crop showing the maximum benefit from BC application and tomato the crop having the minimum benefit, with sorghum in the middle, and (iii) for wheat, lower f_BC than those required for sorghum and tomato would be enough to obtain comparable V_IRR reductions, indicating that BC was more efficiently used by wheat.

BC increased the irrigation water use efficiency (IWUE) for the three crops, with the maximum IWUE obtained for f_BC = 0.33 and Z_DS+BC = 40 cm (Table 4). However, the effect of BC on IWUE was maximal for wheat (from 2.77 kg m⁻³ to 3.38 kg m⁻³) and negligible for sorghum and tomato. This result is in agreement with the previously observed maximum effect of BC on the V_IRR values observed for wheat.

For each crop and for the different f_BC and Z_DS+BC options, Figure 3 illustrates the dimensionless ratios V_IRR/V_IRR,DS (Figure 3a–c) and #IRR/#IRR_DS (Figure 3d–f), obtained by dividing the V_IRR and the #IRR by the corresponding DS without BC, as a function of the considered Z_DS+BC. The lowest V_IRR/V_IRR,DS and #IRR/#IRR_DS indicate more efficient water and energy saving solutions, either in terms of f_BC or of Z_DS+BC. These figures can be useful to select different possible f_BC and Z_DS+BC options even for f_BC and Z_DS+BC values not explored in the simulations.

The f_BC and Z_DS+BC options could be further considered in terms of feasibility and sustainability according to cost-benefit analyses taking into account site-specific factors, such as the cost and the availability of BC, the cost of water and of energy needed for irrigation, as well as the benefits of amending the DS by BC in terms of V_IRR, #IRR and IWUE.

In the UAE, the addition to sandy soils of BC obtained from palm dates significantly improved plant growth in maize and soil productivity [74], since there are about 40 million palm trees and each tree generates annually about 15 kg of biomass waste, with a total of 600 million kg of green waste. The use of on-farm produced BC as amendment could be a sustainable practice enhancing the soil water retention [75] and increasing the IWUE [76], thus helping to save water.

If BC can be produced from locally abundant feedstock, the low cost of BC could justify the amount of BC to be used to amend the soil with higher f_BC and/or Z_DS+BC. An example of the use of BC from date palms as a strategy improving the soil water retention of desert sand in Saudi Arabia, increasing the yield of wheat, was recently reported by Alotaibi and Shoenau [49].

Figure 3 also shows that, in general, similar V_IRR/V_IRR,DS and #IRR/#IRR_DS ratios were obtained for Z_DS+BC = 10, 20 cm, indicating that the application of BC into the first 10 cm could be sufficient in order to gain some benefits, except for wheat (at f_BC = 0.091), sorghum (at f_BC = 0.33) and tomato (at f_BC = 0.23 and 0.33), for which Z_DS+BC = 20 cm was necessary to further reduce V_IRR.

For the three crops, Table 4 also shows that in the NWS scenario, no effects of BC occurred on the biomass, B_mass, on the H_i index or on the crop yield, Y. These expected results are due to the fact that no water stress was induced on the crops, and as a consequence the yield, Y, was always maximal. Therefore, negligible effects were also observed on the WUE, because of the slight influence of the evaporation component, E (Table 4), on ET, obtained by varying f_BC and Z_DS+BC.

With reference to the “deficit irrigation”, DI,

Table 5. Soil layering for different biochar fractions, f_BC, and main outputs obtained using AQUACROP for the three considered crops. Data refer to a soil-water balance simulation with a “deficit irrigation” management (DI).

Soil Layer	ZDS+BC (mm)	ZDS (cm)	ET0 (mm)	VIRR (mm)	T (mm)	E (mm)	T/Tmax (-)	tws/tLCD (-)	#IRR (-)	hIRR (mm)	Bmass (t/ha)	Hi (%)	Y (t/ha)	WUE (kg/m3)	IWUE (kg/m3)
Wheat
DS	-	40	299.6	190	170.5	17.4	0.185	0.20	38	5	11.1	13.2	1.5	0.76	0.77
fBC 0.091	40	-			191.7	17.9	0.321	0.14			12.3	42.3	5.2	2.41	2.74
	30	10			186.5	18.2	0.347	0.17			12.0	40.1	4.8	2.28	2.53
	20	20			181.9	18.4	0.345	0.18			11.7	37.2	4.4	2.11	2.30
	10	30			176.2	18.3	0.349	0.21			11.4	25.7	2.9	1.46	1.54
fBC 0.23	40	-			209	18.6	0.375	0.10			13.2	45.9	6.0	2.57	3.18
	30	10			201.2	19.1	0.374	0.13			12.8	44.8	5.7	2.52	3.01
	20	20			190.2	20.6	0.350	0.15			12.2	44.2	5.4	2.48	2.85
	10	30			181.3	18.7	0.345	0.18			11.7	41.3	4.8	2.33	2.54
fBC 0.33	40	-			212	18.7	0.435	0.10			13.3	46.2	6.1	2.57	3.23
	30	10			202.7	19.6	0.367	0.12			12.9	45.2	5.8	2.53	3.06
	20	20			192.4	20.3	0.333	0.14			12.3	44.1	5.4	2.47	2.86
	10	30			182.2	19.9	0.330	0.17			11.7	42.5	5.0	2.39	2.62
Sorghum
DS	-	40	402.5	264	171.7	37.3	0.394	0.49	33	8	9.8	13.9	1.4	0.65	0.52
fBC 0.091	40	-			226.6	32.5	0.619	0.35			13.8	18.3	2.5	0.97	0.96
	30	10			223.1	32.5	0.600	0.35			13.6	18.1	2.5	0.96	0.93
	20	20			217.4	33	0.609	0.37			13.1	18.9	2.5	0.99	0.94
	10	30			199.9	35.4	0.592	0.39			11.9	18.2	2.2	0.92	0.82
fBC 0.23	40	-			245.1	35.1	0.643	0.29			14.8	24.0	3.5	1.26	1.34
	30	10			236.7	35.1	0.586	0.29			14.4	20.5	2.9	1.08	1.11
	20	20			228.4	35.7	0.599	0.33			13.8	21.4	3.0	1.12	1.12
	10	30			208.7	36.7	0.632	0.38			12.5	19.1	2.4	0.97	0.90
fBC 0.33	40	-			248.1	36.1	0.646	0.28			15.0	26.2	3.9	1.38	1.48
	30	10			239.2	36.1	0.593	0.27			14.5	21.6	3.1	1.14	1.18
	20	20			228.6	36.5	0.612	0.34			13.9	20.3	2.8	1.06	1.07
	10	30			209.3	36.9	0.634	0.38			12.6	19.9	2.5	1.01	0.95
Tomato
DS	-	40	397.1	296	273.1	24.1	0.557	0.24	37	8	10.3	41.6	4.3	1.44	1.45
fBC 0.091	40	-			292.1	25.2	0.642	0.22			11.0	59.9	6.6	2.07	2.22
	30	10			286.1	23.4	0.563	0.19			10.9	58.0	6.3	2.03	2.13
	20	20			280.7	23.5	0.604	0.24			10.7	55.9	6.0	1.96	2.02
	10	30			276.8	23.6	0.579	0.24			10.6	54.6	5.8	1.91	1.95
fBC 0.23	40	-			305.3	26.4	0.663	0.18			11.3	61.7	7.0	2.11	2.36
	30	10			295.7	24.8	0.557	0.16			11.1	61.4	6.8	2.13	2.31
	20	20			287.9	24.8	0.557	0.18			10.9	60.1	6.6	2.09	2.22
	10	30			279.3	24.2	0.595	0.24			10.6	57.3	6.1	2.01	2.06
fBC 0.33	40	-			306.3	26.9	0.656	0.17			11.4	61.8	7.0	2.11	2.38
	30	10			297.3	25.7	0.570	0.16			11.2	61.5	6.9	2.13	2.33
	20	20			288.6	25.7	0.563	0.18			10.9	60.5	6.6	2.1	2.23
	10	30			279.7	24.4	0.598	0.24			10.7	57.7	6.2	2.02	2.08

reports the main outputs of the soil-water balance simulated by AQUACROP for the three considered crops and for the previously considered f_BC and Z_DS+BC values.

In particular, DI was applied by supplying to each crop an irrigation volume, V_IRR, calculated as 0.7 ET_C, and setting a number of irrigations (#IRR) of fixed depth, h_IRR (mm), uniformly distributing V_IRR in the simulation period. According to this irrigation scheduling (Table 3), the pairs (#IRR, h_IRR) satisfying the values of the pre-calculated V_IRR were equal to (38, 5), (33, 8) and (37, 8) for wheat, sorghum and tomato, respectively. The supplied V_IRR were equal to 190 mm, 264 mm and 296 mm for wheat, sorghum and tomato, respectively.

As can be seen in Table 5, the application of BC increased the T/T_max ratio, at increasing f_BC and Z_DS+BC, with the T/T_max calculated by considering only the T/T_max < 1 values. The lowest T/T_max were obtained for wheat (average of all the values equal to 0.356), and the highest for sorghum (0.614), with tomato in the middle (0.595). These results indicated that a greater water stress (WS) condition occurred for wheat compared to sorghum and tomato.

For the three crops, the productivity parameter, B_mass, increased at increasing f_BC and Z_DS+BC, with the highest B_mass obtained for sorghum, followed by wheat and tomato. However, the H_i values were maximum for tomato (H_i between 61.8% and 54.6%), followed by wheat (between 46.2% and 25.7%). Instead, for sorghum, H_i values were much lower (between 26.2% and 18.1%) than those obtained for wheat and tomato, thus indicating that the effect of water stress (WS) negatively impacted sorghum in terms of H_i.

The low H_i values obtained for sorghum can be explained by the shorter H_i building up period (Table 2), during which T/T_max lower than 1 occurred with a higher frequency than for wheat and tomato, as can be seen by considering the ratio (t_ws/t_LCD) between the number of days, t_ws, during which T/T_max < 1, with respect to the length of the crop development stage, t_LCD (Table 2). Indeed, as reported in Table 5, the t_ws/t_LCD ratios obtained for sorghum were higher than those for wheat and tomato, thus explaining the lowest H_i and Y values obtained for sorghum, compared to wheat and tomato, notwithstanding its highest T/T_max.

As can be seen in Table 5, for the three crops, the maximum positive effect of BC occurred when BC at f_BC = 0.33 was considered to be applied up to the maximum considered depth, Z_DS+BC = 40 cm. In this case, for wheat, Y increased from 1.5 t/ha (for the DS) to 6.1 t/ha, with this latter value being comparable to that of the NWS scenario (Y = 6.96 t/ta) and indicating that BC mitigated the yield reduction due to water stress. For tomato, Y increased from 4.3 t/ha (for the DS) to 7 t/ha, against a value of 7.84 t/ha for the NWS condition, indicating a similar beneficial effect of BC application. Instead, for sorghum, Y increased from 1.4 t/ha (for the DS) to 3.9 t/ha, against a value of 7.1 t/ha for the NWS scenario, indicating less benefits of BC application than for wheat and for tomato.

Figure 4a–c illustrate the ratios Y/Y_max, as a function of Z_DS+BC. For wheat (Figure 4a), BC increased the Y/Y_max values at increasing f_BC and Z_DS+BC, thus decreasing the Y reduction (1−Y/Y_max) from 78.4% for the DS to values ranging between 12.3% (for f_BC = 0.33 and Z_DS+BC = 40 cm) and 58.3% (for f_BC = 0.091 and Z_DS+BC = 10 cm).

For sorghum (Figure 4b), the Y/Y_max values were lower than those obtained for wheat, with reductions in Y decreasing from 80.3% for the DS to values ranging between 45.1% (for f_BC = 0.33 and Z_DS+BC = 40 cm) and 69.0% (for f_BC = 0.091 and Z_DS+BC = 10 cm).

Tomato (Figure 4c) was the crop less affected by water stress under DI conditions, with a reduction in Y equal to 45.1% for the DS, compared to the NWS scenario, much lower than those obtained for wheat (78.4%) and for sorghum (80.2%). For tomato, 1−Y/Y_max decreased from 45.1% (DS) to 10.7% (for f_BC = 0.33 and Z_DS+BC = 40 cm) and to 26.0% (for f_BC = 0.091 and Z_DS+BC = 10 cm). These results demonstrated that with reference to the mitigating effect of BC on the Y reductions, wheat was the crop receiving the maximum benefits from BC application, followed by tomato, with sorghum showing the lowest benefits.

Figure 4d–f illustrates IWUE as a function of Z_DS+BC. The figures can be useful to check the effect of the different f_BC and Z_DS+BC values on the considered IWUE, which depends on Y and on V_IRR. The maximum IWUEs were obtained for wheat, followed by tomato, with lower IWUEs for sorghum even at the highest f_BC and Z_DS+BC.

When the WUE was considered (Table 5, Figure 4g–i), the maximum WUE was obtained for wheat and tomato, and the minimum for sorghum. These results depended on the lower actual ET predicted for wheat (average value 209.5 mm) compared to tomato (average value 309.8 mm) and to sorghum (average value 257.1 mm), with maximum Y obtained for tomato, followed by the minimum for sorghum. These results confirmed that, under DI, wheat was the crop providing the maximum WUE, and tomato the second possible best option, while cultivation of sorghum slightly benefited from BC application and could be inadvisable under deficit irrigation.

These outcomes could be of interest considering the economic importance of cultivating wheat and tomato in the UAE and in many other countries of the world.

For the three crops, Figure 5a,b illustrate the comparison between the WUE and IWUE obtained with the DI and NWS scenarios, either without BC and after BC addition, at f_BC = 0.091 and Z_DS+BC = 10 cm, which is the option among those explored, involving the minimum amount of BC. Values of f_BC lower than 0.091 were not explored in the simulations because the results previously obtained showed no effects on porosity, soil water retention and AW_max of the DS when f_BC = 0.014 was tested [51].

For the three crops, when the NWS scenario was considered, the effect of this f_BC and Z_DS+BC on IWUE and on WUE was negligible. Instead, under DI, BC increased the WUE and the IWUE, compared to the values obtained for the DS without BC, counterbalancing the lower irrigation amount, V_IRR, supplied with the DI. The maximum increase in IWUE and WUE as an effect of BC addition was found for wheat, with lower effects on tomato than on wheat, and negligible effects on sorghum.

These results showed that even at the lowest considered f_BC, among those considered, BC addition could be a useful strategy when DI is practiced for cultivation of wheat and tomato, under the extremely arid conditions of the considered study area and/or similar climatic areas. Our results agree with those obtained by Bagheri et al. [77] in Iran, indicating that the use of biochar can be a successful strategy for improving water use efficiency and reducing the effects of drought stress under extremely arid climates and for soils characterized by very poor soil water retention, as the desert sand considered in this investigation.

Although these results could be considered reliable, because based on accurately measured hydraulic properties as input of the hydrological AQUACROP component, and on suitable crop parameters, a field investigation aimed at validating some of the best options provided by the model should be the next step to be pursued.

4. Conclusions

This investigation showed that the application of biochar to a desert sand for three crops, i.e., wheat, sorghum and tomato, positively affected the soil water balance and the irrigation scheduling simulated by the AQUACROP model, using as input accurately measured soil hydraulic parameters representing the physical and structural condition of the soil without and with BC addition.

The results indicated that the application of biochar could be proposed as a strategy mitigating water and energy saving consumptions when no water stress conditions were considered, in order to obtain the maximum crop yield. Wheat was the crop showing the maximum benefits from biochar application in terms of reduction in the amount of irrigation volume, of increase in the irrigation water use efficiency and of decrease in the number of predicted irrigations. Biochar showed beneficial effects also for sorghum and tomato but to a lesser extent than for wheat.

When a deficit irrigation was simulated, the application of biochar counterbalanced the lower amount of irrigation volume supplied under deficit irrigation, partially mitigating the crop yield reduction due to the water stress conditions, especially for wheat and for tomato, with a less pronounced effect for sorghum. Under deficit irrigation, the application of biochar could also be a useful management option to promote the cultivation of wheat and tomato in the UAE, as well as in other countries with similar climatic and soil conditions. This could help to increase the local production, thus reducing the impact of water shortage on food security and the dependency on food import.