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Article

Evaluation of the Long-Term Water Balance in Selected Crop Rotations with Alfalfa in a Soil-Heterogeneous Lowland Region of the Czech Republic

by
Renata Placatová
1,*,
Vladimír Papaj
1,
Petr Fučík
1,
Jiří Brázda
1,
Lukáš Pacek
2 and
Pavel Tlustoš
2
1
Research Institute for Soil and Water Conservation, Žabovřeská 250, Zbraslav, CZ-156 00 Prague, Czech Republic
2
Department of Agroenvironmental Chemistry and Plant Nutrition, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Kamýcká 129, Suchdol, CZ-165 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(8), 1692; https://doi.org/10.3390/agronomy14081692
Submission received: 26 June 2024 / Revised: 26 July 2024 / Accepted: 30 July 2024 / Published: 31 July 2024
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
Versions Notes

Abstract

:
The Czech Republic has diverse soil conditions, which cause notable differences in crop water balance (WB). To assess the long-term crop WB and crop water stress (CWS) intensity in rainfed conditions, four conventional eight- and ten-year crop rotations (CRs) with perennial forage (alfalfa), cereals, oilseeds, root crops and legumes were proposed for a heterogeneous lowland soil region (six texture classes) in eastern Bohemia. Two of the CRs were selected irrespective of the WB (eight-year, C-8; ten-year, C-10), and the other two were designated according to soil water resources and crop water requirements (CWRs) as water-saving (W-S) and water-demanding (W-D) for this region. All CRs had a negative WB on average (i.e., CWRs exceeded the available water resources), with varying degrees of CWS (categories 1 (low) to 4 (high)). The W-S CR reduced the WB deficit relative to the other CRs by omitting maize, sunflower and sugar beet and including sorghum, and expanded the proportion of the area not affected by CWS (categories 1–2) to 33% for predominantly loamy soils. In contrast, categories 1–2 in the C-8, C-10 and W-D CRs represented only 15%, 14% and 7% of the area, respectively. Other areas were significantly affected by CWS (categories 3–4) and showed a high risk of yield reduction. These results may help in implementing sustainable farming systems that consider environmental perspectives related to agricultural water use in Central Europe.

1. Introduction

In agricultural systems, water availability is essential for determining the crop yield and overall productivity. The availability of water resources and their efficient use is crucial for sustainable agriculture, especially in rainfed regions. Here, aquifer storage and food security are already under stress, which is likely related to climate change [1,2]. In Central European conditions, Meitner et al. [3] estimated the drought-induced crop yield losses for Czechia in the years 2017 and 2018 for 17 different crops using machine learning in combination with remote sensing and ground observation data to be 30–60%. For southern Germany, the significant change expected in agro-climatic indicators with profound implications for crops by 2100 is discussed in [4], including the proposal of a comprehensive range of adaptation measures. For conditions in Southern and Central Europe, [5] highlighted the need for more comprehensive support for improved irrigation, changes to cropping systems and the revision of environmental regulations due to the projected warmer and drier climate. In Hungary, [6] quantified a decrease in maize yields of 11.1–12.4% per year between 1981 and 2010 due to the increasing average temperature coupled with decreasing rainfall. To ensure the sustainability of rainfed agriculture and optimize the use of limited water resources, it is essential to manage water efficiently under both existing and anticipated constraints. Among the options to address, the implementation of water-saving crop rotations (CRs), drought-tolerant cultivars and/or soil tillage are under consideration, all of which involve the sequential planting of different crops in a particular field over a defined period with corresponding soil tillage [5,7,8]. Crop rotations can help to optimize water use efficiency by diversifying the crop types and their water requirements, thus reducing the risk of groundwater overuse and water stress, improving overall crop water balance (WB) and maintaining crop yields [9,10,11]. The water-saving CRs for rainfed farming in the European temperate climate usually include sorghum, perennial forage crops or legumes [3,4,6]. Sorghum, as a C4 plant, provides a higher photosynthetic yield and with its tightly meshed root system, buried at depths of up to 2 m, thrives efficiently in both warm and dry conditions [12,13]. Alfalfa is often mentioned as a drought-tolerant legume (among other perennial legumes) due to its deep rooting system. However, it is an opportunistic water user that is best suited to soils with a high-water reserve. In contrast to species adapted to drought stress, it exhibits low stomatal closure in the early stages of drought [10,14]. To our knowledge, not many studies have quantified the effect of the inclusion of water-saving crops on water use efficiency for longer periods. Inserting diversified crops into CRs can decrease annual crop evapotranspiration (ETc) by 8–24% compared to a simplified winter wheat–summer maize rotation [15]. In a seven-year study conducted in Kansas on silt loams, [16] found that forage sorghum grown in a conventional wheat–forage sorghum–fallow rotation was a crucial factor for 23–30% higher pre-season soil water storage compared to a conventional winter wheat–double forage sorghum cropping system. In the northern Great Plains of the USA, [17] quantified the effect of diversified CRs in relation to a 23–118% increase in pre-sowing soil water storage compared to continuous wheat cultivation.
Following the literature findings, we hypothesize that the modification of CRs can reduce crop water deficits, showing which soils are the most vulnerable in the study region. Thus, the objectives of the study were (i) to compare four conventional CRs with alfalfa (including water-demanding and water-saving CRs) based on a 10-year (2011–2020) crop WB and the occurrence of crop water stress (CWS) and (ii) to assess the potential for mitigating the WB deficit in a soil-heterogeneous region of eastern Bohemia.

2. Materials and Methods

2.1. Characteristics of the Study Area

For the study objective, a circle-shaped area with a radius of 20 km around the town of Dašice in eastern Bohemia (Polabska lowland, Pardubice district) was selected. The visualization of all study results presented in the figures below refers only to agricultural land with soil blocks larger than 0.5 ha. The region is dominated by flat lowlands (average altitude of 261.7 m a.s.l. and slope of 1.54°, Table 1). Descriptive statistics of meteorological data (annual air temperature, sums of precipitation and reference evapotranspiration ETo) for the period 2011–2020 are shown in Table 1. The ETo was determined according to the Penman–Monteith FAO-56 method [18]. All meteorological data were taken as averages from four meteorological stations (Chrudim, Pardubice, Poběžovice u Holic, Hradec Králové) operated by the Czech Hydrometeorological Institute (CHMI) in the form of decadal totals (ETo and precipitation) or decadal averages (air temperature). The interpolation of these data was performed using a regression kriging method based on elevation, slope, roughness and terrain orientation on a 500 × 500 m grid. Meteorological data change in a north–west-to-south–east direction, i.e., precipitation increases (Figure 1) but air temperature and ETo decrease (Figure 2) in that direction.
The region is markedly soil-heterogeneous, as shown by the spatial distribution of texture classes and soil types in Figure 3 and Figure 4. Texture classes and soil types were determined on the basis of a polygon layer created by digitizing data from the Comprehensive Soil Survey (CSS), which was carried out in former Czechoslovakia in 1960–1972 on all agricultural land, where 393,000 soil probes were sampled (of which 5457 were in the study area). Texture classes differ in soil hydrolimits (field capacity (FC), permanent wilting point (PWP) and available water capacity (AWC), i.e., the amount of soil water available for crops, also known as the water-holding capacity [19]). The saturation of the AWC (SAWC) with water during the year is also influenced by texture classes (Table 2). In all soil types (Reference Soil Groups [20]), a deep (60–120 cm) to very deep (above 120 cm) soil profile prevails (95.1% of the area). A moderately deep soil profile (30–60 cm) is present in 4.1% of the area (Cambisols, Regosols, Luvisols, Phaeozems, Kastanozems), and 0.8% of the area includes soil types with a shallow soil profile of up to 30 cm (Cambisols, Regosols, Luvisols).
Crops grown in the study area correspond to regional soil and climatic conditions that predetermine the division of agricultural land in the Czech Republic into agricultural growing areas (AGAs). The study area is dominated by sugar beet AGAs and to a lesser extent, by potato AGAs. Cereals (wheat, barley, triticale, grain maize), forage crops (alfalfa, red clover, silage maize, sorghum), oilseeds (winter rape, sunflower, poppy), root crops (sugar beet) and legumes (soybean, pea) are grown there. To test WB and crop water deficiency, four conventional CRs with perennial forages represented by alfalfa recommended by the Crop Research Institute in Prague [21] were selected (Table 3). Two of them (an 8-year CR, C-8, and a 10-year CR, C-10) were selected as characteristic of the sugar beet AGA, regardless of water consumption, and the other two were selected as water-saving (W-S: higher soil water availability, lower crop water requirements (CWRs)) and water-demanding (W-D: lower soil water availability, higher CWRs).

2.2. Water Balance Calculation

In this study, we quantified the WB from just the crop’s point of view, i.e., the overland flow and deep percolation—common parameters of water balance formulas—were omitted, and the crop-available soil WB was further evaluated [22,23]. The WB for the selected crops/CRs of the study area was based on the difference between available water resources (precipitation, soil water supply at the beginning of the growing season, capillary rise from shallow groundwater) and average CWRs over the 2011–2020 period. Calculations were performed for the entire growing season of all selected land blocks >0.5 ha included in the Land Parcel Identification System (4560 land blocks with a total area of 560.57 km2). The average area of a land block was 12.3 hectares.
Meteorological data in the WB calculations (ETo, air temperature and precipitation) were interpolated to daily values (i.e., differences of decades divided by 10). Equation (1) was used to calculate the WB and is based on the method for a crop supplemental irrigation requirement according to Czech technical standard (CTS) no. 75 0434 [24], but with modifications to some of the terms (as presented further on):
WB = r1 × α × P + r2 × ASWS + AARG − CWR
where
  • WB: water balance [mm];
  • r1: reduction coefficient for adjusting α for the terrain slope >10%;
  • α: coefficient of precipitation availability depending on the soil type;
  • P: precipitation [mm];
  • r2: reduction coefficient for adjusting ASWS depending on the soil type and
    terrain slope;
  • ASWS: available soil water supply at the beginning of the growing season [mm];
  • AARG: available amount of rising groundwater [mm];
  • CWR: crop water requirement [mm].
The precipitation (P) was multiplied by the coefficient α, which expresses the water infiltration process in relation to the texture classes (sandy = 0.60, loamy sands = 0.65, sandy loams = 0.70, loamy = 0.75, clay loams = 0.70, clayey = 0.60) and slope. The coefficient α on parcels with a slope >10% was reduced by r1 = 0.80 [25].
Available soil water supply (ASWS) at the beginning of the growing season was calculated for each crop in CRs as
ASWS = AWC × (SAWC/100) × RD
where
  • AWC: available water capacity [vol. %];
  • SAWC: saturation of AWC with water [%];
  • RD: the maximum effective rooting depth of the crop [dm].
AWC was based on the difference between FC and the PWP, which were derived by pedotransfer functions from the typical mass percentage of soil particles <0.01 mm [26], identified for a given texture classes of topsoil during the CSS (Table 2). The average SAWC was obtained for topsoil as raster layers for four dates (Table 2), originating from the AVISO model run by CHMI based on the MORECS model [27,28]. The AVISO model uses AWC values from the CSS and the original Penman–Monteith equation to determine potential evapotranspiration from the grass reference surface [29].
For winter crops, ASWS was applied on 31 August or 30 September for the post-sowing period and on 1 April for the start of physiological activity in the spring. For spring crops, ASWS was applied on 1 or 30 April, depending on the sowing date. The volume of the ASWS was furthermore reduced by a coefficient r2 depending on the terrain slope and the content of clay particles [24].
The available amount of rising shallow groundwater (AARG) for crops was obtained from the tables in [24] for the months of the growing season and texture classes on the basis of the spatial pattern of the shallow groundwater level (GWL). However, the AARG values given in the aforementioned CTS are unrealistically high, and therefore these were reduced based on the water-holding capacity of the texture classes, as recommended by the CTS authors [30]. The shallow GWL was derived by kriging the CSS data to obtain a grid layer of shallow GWL at a spatial resolution of 100 × 100 m. Where no aquifer was measured in soil probes, a fixed value of 180 cm was used, which, based on the experience of the CTS authors, is the most likely mean value for these conditions.
Some of the parameters of available water resources used in Equation (1), e.g., α, r1, r2, as well as the AARG values, are based on the results of the scientific activities of the Research Institute for Irrigation Management, which carried out extensive experimental work throughout Czechoslovakia in the 1960s to 1980s. The knowledge gained from this work was subsequently used in the development of CTS no. 75 0434 and its previous version.
The CWR over the entire growing season was the sum of ETc, which is optimal for crop development, and was determined by multiplying the ETo and crop coefficient Kc (tabulated values in [18]). Kc varied during the growing season, from the initial phase of the crop (Kcini, planting/sowing to cover about 10% of the area), through the middle phase (Kcmid, full coverage until the beginning of maturity), to the final phase (Kcend, beginning of maturity until harvest). For spring crops, the Kcini values were used for a defined period, and then the values were linearly interpolated until Kcmid was reached, and after a certain time, they started to decrease linearly to Kcend. For winter crops in the sowing year, Kcini was used from sowing to the threshold air temperature that terminated CWR, and in the harvesting year, CWR began to be calculated using Kcini after the minimum growth temperature had been reached. Spring barley, used as a cover crop for alfalfa, was harvested for green fodder during the Kcmid phase in May. Between each alfalfa cutting, all three Kc phases were applied.

2.3. Categories of CWS

To distribute the calculated WB of all crops into four categories with different water availability, its absolute values were converted from mm to vol.% (divided by ten), corresponding to the amount of water for full FC saturation. Category 1 presented a positive WB; that is, the amount of available water for crops was higher than the CWRs. All other categories (2–4) had a negative WB and were defined by comparing the converted WB with soil hydrolimits. Categories 2–3 were related to the crop-dependent depletion of readily available soil water, expressed as a minimum saturation of AWC (SAWCmin, Table 4), which entered the calculation of the soil hydrolimit as the point of decreased availability (PDA):
PDA = PWP + SAWCmin/100 × AWC
where
  • PDA: point of decreased availability [vol. %];
  • PWP: permanent wilting point [vol. %];
  • SAWCmin: minimum saturation of AWC after depletion of readily available soil water [%].
Category 2 was assigned to the converted WB corresponding to the soil moisture between FC and PDA, i.e., without the presence of CWS and without negative effects on crop development and yield. On the other hand, CWS occurred in categories 3–4 due to a decrease in soil moisture below PDA. Category 3 indicated a water deficit equivalent to soil moisture lower than PDA but higher than the PWP, and category 4 represented severe CWS with soil moisture dropping below the PWP.
For CRs, there were only three categories of CWS (1–2, 2–3, 3–4), which were calculated for each land block as averages over all crops grown.
The computation of individual categories of CWS was performed with the PostgreSQL 14.10 database system with an extension of spatial information: PostGIS 3.2.
Table 4. Means and standard deviations of the water balance (WB) components (see Equations (1) and (3)) and categories of CWS (incl. their area) for each crop involved in the selected CRs.
Table 4. Means and standard deviations of the water balance (WB) components (see Equations (1) and (3)) and categories of CWS (incl. their area) for each crop involved in the selected CRs.
CropsWB
(mm)		r1.α.P
(mm)		r2.ASWS
(mm)		AARG
(mm)		CWR
(mm)		SAWCmin
(%)		Categories of CWS
M. ± s.d.1 (%)2 (%)3 (%)4 (%)
SB/A−52 ± 74.0311 ± 30.252.6 ± 12.658.6 ± 36.7474 ± 15.4602.3 ± 1.026.942.119.611.3
A (1st)−121 ± 85.3322 ± 29.984.1 ± 20.161.5 ± 38.5589 ± 21.1552.8 ± 0.95.839.732.521.9
A (2nd)−40 ± 64.7221 ± 18.784.1 ± 20.152.6 ± 30.9399 ± 14.5452.0 ± 0.932.148.513.85.6
WR−88 ± 56.8287 ± 26.950.1 ± 12.321.7 ± 17.5446 ± 15.8502.5 ± 0.85.454.328.012.2
WW−103 ± 53.8256 ± 22.963.6 ± 15.122.1 ± 17.7445 ± 35.0452.6 ± 0.81.359.525.114.1
Sorg−116 ± 42.7220 ± 23.626.7 ± 8.612.5 ± 13.3374 ± 12.2402.7 ± 0.80.554.630.714.2
Sug−141 ± 53.6271 ± 28.326.7 ± 8.625.2 ± 21.9464 ± 16.8453.0 ± 0.80.930.443.725.0
SM−129 ± 42.1208 ± 18.933.3 ± 10.736.0 ± 10.7407 ± 13.6603.1 ± 0.60.013.068.318.7
SB−139 ± 35.3165 ± 15.442.1 ± 10.010.7 ± 16.1357 ± 9.2553.1 ± 0.50.17.873.019.1
Sun−158 ± 50.1256 ± 26.247.7 ± 14.012.3 ± 13.3475 ± 16.2453.2 ± 0.70.022.146.531.4
GM−186 ± 49.4256 ± 27.433.3 ± 10.712.4 ± 13.4487 ± 14.2553.4 ± 0.60.02.955.341.8
Pea−177 ± 38.7183 ± 16.342.1 ± 10.011.2 ± 16.7414 ± 12.9553.4 ± 0.50.02.062.335.7
M.: mean, 1st: first year, 2nd: second year.

2.4. Statistical Analysis

The Kruskal–Wallis test (one-factor nonparametric ANOVA) was used to test the effect of texture classes on the WB (for all crops and CRs) and categories of CWS (for all CRs). Within each texture class, this test was also used to identify the effect of CR on the WB or the categories of CWS. The same test was used to determine changes in the volume of available water for crops in the topsoil among texture classes for four different dates (1 April, 30 April, 31 August and 30 September). The Kruskal–Wallis test was used due to the violation of the assumptions of using one-factor ANOVA (non-normal distribution of model residuals and violation of homoskedasticity) identified by the Shapiro–Wilk and Levene’s tests, which were not met even after the transformation of the dependent variables. For multiple comparisons of differences between texture classes, Dunn’s test was used to adjust p-values using the Bonferroni method. All statistical tests were performed in the R platform (version 4.3.0) for statistical computing [31] at the probability level of α = 5%.

3. Results and Discussion

The WB components and categories of CWS for each crop and four CRs are shown in Table 4 and Table 5. Although all crops and CRs had a negative water balance on average, with different categories of CWS, the introduction of water-saving CR proved to be effective for water deficit reduction. Thus, the inclusion of water-saving crops or other measures to decrease crop water requirements is very important for successful crop development [32]. Only 0.9–3.3% of CRs’ areas had a positive WB with available water resources exceeding CWRs (Figure 5, Figure 6, Figure 7 and Figure 8). Alfalfa, winter rape, sorghum and winter wheat had 46–81% of the area in categories 1 and 2, i.e., they were not affected by water stress, which was the reason for their inclusion in the water-saving CR. The suitability of these crops for water-saving CR resulted not only from the combination of more easily available water resources (alfalfa with deep root depth) and lower CWR (sorghum) but also from an important role assigned to low SAWCmin, i.e., the high ability of the crop to deplete soil water (found in all four crops).
Alfalfa has been reported to be one of the most productive and most effective forage crops for soil and water conservation and soil nitrogen fixation [33], but with controversial hydrological effects. In agreement with our results, [34] reported that alfalfa is a water-efficient crop and attributed this to its high canopy photosynthesis rate relative to water requirements (10 g CO2/kg water per day before cutting). The high soil water availability for alfalfa is related to the deep root system (>1 m) and good water infiltration into the soil [35]. Alfalfa may be able to access deep groundwater, as its roots grow to a depth of 2–4 m [36]. This would further improve the WB deficit, but this phenomenon was not accounted for in our calculations. Winter wheat in rotations with maize, grain sorghum and soybean was found to have the highest available soil water after harvest, indicating, in agreement with our results, the lowest WB deficit of the respective crops [37]. Winter rape is reported as highly sensitive to drought stress [38], but its moderately higher ASWS mitigated the WB deficit.
For all other crops (sugar beet, silage/grain maize, spring barley, sunflower, pea) the proportion of categories 1 and 2 dropped below 31% (even <3% for grain maize and pea), and the WB deficit ranged from 129 to 186 mm. Similarly, a strong WB deficit associated with growing water-demanding sugar beet has been reported in Slovakia [39], as a water deficit of 70–130 mm to meet the CWR was found as early as 1961–1990. Sunflower is also highly water-demanding, but due to its well-developed root system and ability to resist temporary wilting, it is at the same time reported as a drought-tolerant crop [40]. Pea is known as a crop that increases soil fertility by supplying nitrogen from the atmosphere, and according to [41], it is also water-efficient, as it requires less water to grow than cereals. However, our study showed a strong negative impact of pea on the WB deficit, significantly larger than that of cereals. Likewise, maize is a water-demanding crop and should be replaced by drought-tolerant sorghum in dry areas [42]. The yield of silage sorghum in areas of severe water shortage is the same as that of silage maize [43]. For this reason, sorghum was included in the water-saving CR in our study [11]. Its ability to deplete the available soil water more intensively (lower SAWCmin,) decreased the average category of CWS compared to silage maize (Table 4). This means that the area of sorghum in categories 3 and 4 was 45% compared to 87% for silage maize.
The use of conventional C-8 and C-10 is particularly suitable from the perspective of WB in the south-eastern part of the region (Svitava Upland) and the least suitable in the central and north-western parts (Figure 5 and Figure 6). Omitting maize, sunflower and sugar beet and including sorghum in the water-saving CR reduced the water deficit and expanded the proportion of areas not affected by CWS (i.e., category 1–2) to 33% (Table 5), i.e., the entire southern part of the region with predominantly loamy soils (Figure 7). In contrast, the representation of category 1–2 in the water-demanding CR was only in 7% of the area in the south-eastern part of the region, while in the remaining parts, the crops were exposed to varying degrees of water stress (categories 2–3 and 3–4, Figure 8).
Texture classes differing in hydrolimits (Table 2) had a significant effect on the WB and categories of CWS. Sandy and loamy sands provide not only the lowest water-holding capacity (i.e., FC and AWC), as reported by [44,45], but also the lowest volumes of ASWS and AARG, resulting in an extreme WB deficit and the categories with the strongest degree of CWS (3–4), even in water-saving CR (Table 6). Conversely, the highest water-holding capacity was provided by loamy and clay loams, as reported by [46], which guarantees these soils have the lowest WB deficit and categories of CWS. These texture classes are also characterized by the highest precipitation availability (coef. α in Equation (1)) and the volume of water from ASWS and AARG. Clay loams and clayey soils have a higher FC than loamy soils but also a higher sensitivity to CWS (i.e., PWP, Table 2). The different water-holding capacities of the texture classes also affect the temporal changes in the volume of water available to the crops. The highest volume of water was available on 1 April, with the greatest differences among texture classes, and the lowest on 31 August, with the least differences among texture classes (Figure 9). The volume of water available to the crops had the following trend as a function of water-holding capacity: clay loams ≥ loamy > clayey ≥ sandy loams > loamy sands > sandy.
Only two pairs of texture classes were identified that showed no differences in the WB, i.e., loamy sands and clayey soil (for all crops and CRs) and clay loams and sandy loams (for winter rape, winter wheat, silage maize and all CRs). Similarly, there were no significant differences in categories of CWS between clay loams and sandy loams for C-10, W-S and W-D (Table 6). As an explanation, crops grown on sandy loams and loamy sands have a higher ASWS availability on fields with slopes of 1.15–5.7° due to higher hydraulic conductivity [47], which is expressed by the coefficient r2 in Equation (1), and conversely, these soils have a lower volume of AARG and ASWS compared to clay loams and clays [48]. Thus, the influences of these pairs of texture classes on the WB are equal.
The greatest influence on the change in the WB and categories of CWS was the selection of CRs on soils with the highest AWC, i.e., loams, clay loams and sandy loams. On these soils, the effect of water-saving CR on the reduction in WB deficit and categories of CWS was most pronounced (Table 6). On loamy soil with water-saving CR, the proportion of areas in the CWS 1–2 category was significantly higher (57.2%) compared to the other soil textures (sandy loams = 31.6%, clay loams = 17.2%, loamy sands = 1.5%, sandy and clayey = 0%). The other CRs had a significantly lower representation of category 1–2 on loamy soil (C-8 = 26.6%, C-10 = 23.9%, W-D = 13%) and even lower on other texture classes (sandy, loamy sands, clayey = 0%, clay loams = 4.3–6.4%, sandy loams = 5.6–14.5%). The highest water availability on loamy soil was evidenced by the fact that the water-demanding CR had a lower WB deficit and category of CWS than the water-saving CR on all other texture classes (Table 6).
In summary, the better conditions for crop production from the perspective of WB in the south-eastern and southern parts of the region were not only linked to better precipitation availability and lower ETc but also to soils with better water availability (loamy, clay loams and sandy loams). In contrast, in the central and north-western part of the region, loamy sands and sandy soils with a low water-holding capacity predominate. Here, however, the effectiveness of the proposed water-saving CR was not confirmed, and other solutions need be found. Highly diversified CRs allow, among other things, for increased water use efficiency and soil water supply in water-stressed agricultural areas [12,49] by rotating crops with shallow and deep roots, which maximizes the soil moisture use at different depths and leads to soil water complementarity between crops [10,50]. Tillage before sowing/planting also significantly affects the amount of soil water available for crops [51]. For example, a no-tillage approach increases the storage of precipitation as available soil water at planting by approximately 20 mm for wheat and 30 mm for sorghum compared to stubble-mulch tillage [52]. Incorporating cover crops into CRs has a positive effect on soil properties but a negative effect on long-term WB. For example, as reported by [9], nearly 120 mm higher evapotranspiration could be found for five-year rotations (winter wheat, spring barley, silage maize, winter wheat, winter rape) with cover crops compared to rotations without cover crops for a lowland site in the future (2051–2080), which could affect groundwater recharge, especially for sites with fine-textured soils.

4. Conclusions

The reduction in crop WB deficit and CWS intensity through the implementation of water-saving CRs presented in this study was strongly dependent on soil texture classes. For soils with the lowest water-holding capacity (sandy and loamy sands), the improvement in crop WB through water-saving CRs was minimal, as water stress intensity remained in the highest categories (3–4). For clayey soils, the water deficit was partially reduced by water-saving CRs, but categories 1–2 (without CWS) were not present. For sandy loams and clay loams, the use of water-saving CRs reduced both crop water deficit and water stress intensity, with areas without CWS accounting for 31.6% and 17.2% of the land, respectively. The most effective use of water-saving CRs was on loamy soils with the highest AWC, where categories 1–2 covered 57% of the area. Other CRs that did not account for water savings had significantly lower areas unaffected by CWS. There were no such areas on sandy, loamy sand and clayey soils, and relatively low proportions on sandy loams and clay loams (4–15%). The highest proportion of areas unaffected by CWS was also found for loamy soils (13–27%). These findings are useful for farmers, watershed managers and policymakers regarding crop water requirements, effective planning, setting incentives and subsidies and the application of sustainable agricultural water management in light of their current and future roles, considering environmental perspectives in Central Europe.

Author Contributions

Conceptualization, R.P., V.P., P.T. and L.P.; methodology, R.P. and V.P.; software, V.P. and J.B.; validation, R.P., V.P. and J.B.; formal analysis, R.P., P.F., P.T. and L.P.; investigation, R.P. and V.P.; resources, V.P. and J.B.; data curation, V.P. and J.B.; writing—original draft preparation, R.P., P.F. and P.T.; writing—review and editing, R.P., P.F. and P.T.; visualization, R.P. and V.P.; supervision, R.P., P.F., P.T. and L.P.; project administration, L.P. and V.P.; funding acquisition, L.P., P.T. and V.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Technology Agency of the Czech Republic (project no. TK03010098), by the Ministry of Agriculture of the Czech Republic (projects no. QL24010263 and RO0223) and by the State Environmental Fund of the Czech Republic (Norway Grants, RAGO, project no. 3211100014, Amálie Pilot Farm—application of the Smart Landscapes concept).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors thank the anonymous reviewers for their very valuable comments.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 01692 g001
Figure 1. Spatial distribution of mean annual precipitation from 2011 to 2020 in the study area (marked on the map of the Czech Republic).
Figure 1. Spatial distribution of mean annual precipitation from 2011 to 2020 in the study area (marked on the map of the Czech Republic).
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Figure 2. Spatial distribution of mean annual reference evapotranspiration (ETo) from 2011 to 2020 in the study area.
Figure 2. Spatial distribution of mean annual reference evapotranspiration (ETo) from 2011 to 2020 in the study area.
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Figure 3. Spatial distribution of texture classes (incl. % area) of the study area.
Figure 3. Spatial distribution of texture classes (incl. % area) of the study area.
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Figure 4. Spatial distribution of soil types (incl. % area) of the study area.
Figure 4. Spatial distribution of soil types (incl. % area) of the study area.
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Figure 5. Spatial distribution of categories of crop water stress (CWS) and water balance for C-8 (see Table 3).
Figure 5. Spatial distribution of categories of crop water stress (CWS) and water balance for C-8 (see Table 3).
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Figure 6. Spatial distribution of categories of CWS and water balance for C-10 (see Table 3).
Figure 6. Spatial distribution of categories of CWS and water balance for C-10 (see Table 3).
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Figure 7. Spatial distribution of categories of CWS and water balance for W-S (see Table 3).
Figure 7. Spatial distribution of categories of CWS and water balance for W-S (see Table 3).
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Figure 8. Spatial distribution of categories of CWS and water balance for W-D (see Table 3).
Figure 8. Spatial distribution of categories of CWS and water balance for W-D (see Table 3).
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Figure 9. The volume of available water in the topsoil for crops in different texture classes and on different dates. Letters indicate significant differences between texture classes according to Dunn’s test (texture classes with the same letters are not significantly different).
Figure 9. The volume of available water in the topsoil for crops in different texture classes and on different dates. Letters indicate significant differences between texture classes according to Dunn’s test (texture classes with the same letters are not significantly different).
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Table 1. Descriptive statistics for selected topographic and meteorological data (2011–2020).
Table 1. Descriptive statistics for selected topographic and meteorological data (2011–2020).
ParameterAltitude
(m a.s.l.)		Slope
(°)		Air Temperature (°C)Precipitation (mm)ETo
(mm)
Mean261.71.5410.7580.8730.7
Median250.41.2810.8583.7732.4
s.d.47.81.010.2837.425.0
Min210.90.39.3505.0660.4
Max464.78.111.1684.8785.4
a.s.l.: above sea level, s.d.: standard deviation.
Table 2. Means of soil hydrolimits and saturation of available water capacity (for four dates averaged over the period 2011–2020, incl. s.d.) in different texture classes.
Table 2. Means of soil hydrolimits and saturation of available water capacity (for four dates averaged over the period 2011–2020, incl. s.d.) in different texture classes.
Texture Classes/Soil HydrolimitsSandyLoamy SandsSandy LoamsLoamyClay LoamsClayey
FC (cm3/cm3)0.1620.2410.3040.3680.3970.389
PWP (cm3/cm3)0.0620.0910.1180.1540.1850.210
AWC (cm3/cm3)0.1000.1500.1860.2140.2120.179
SAWCApr 1 (cm3/cm3)0.62 ± 0.120.61 ± 0.100.63 ± 0.100.63 ± 0.090.65 ± 0.080.67 ± 0.09
SAWCApr 30 (cm3/cm3)0.38 ± 0.140.37 ± 0.130.40 ± 0.110.41 ± 0.090.42 ± 0.100.44 ± 0.11
SAWCAug 31 (cm3/cm3)0.25 ± 0.080.24 ± 0.070.24 ± 0.070.24 ± 0.060.25 ± 0.070.28 ± 0.09
SAWC Sept 30 (cm3/cm3)0.34 ± 0.070.34 ± 0.070.32 ± 0.080.33 ± 0.060.34 ± 0.060.36 ± 0.09
AWC: available water capacity, FC: field capacity, PWP: permanent wilting point, SAWC: saturation of AWC.
Table 3. Sequence of crops in selected crop rotations (CRs).
Table 3. Sequence of crops in selected crop rotations (CRs).
Year/CRs1st2nd3rd4th5th6th7th8th9th10th
Conventional 8-year (C-8)AAGMSBSMWWSugSB/A
Conventional 10-year (C-10)AAWWSBSunSBWRWWSMSB/A
Water-saving (W-S)AAWWSBWRWWSorgSB/A
Water-demanding (W-D)AAGMSBPeaWWSMSB/A
A: alfalfa, GM: grain maize, SB/A: alfalfa undersown into spring barley, SB: spring barley, SM: silage maize, Sorg: sorghum, Sug: sugar beet, Sun: sunflower, WR: winter oilseed rape, WW: winter wheat.
Table 5. Means and standard deviations of the WB components (see Equations (1) and (3)) and categories of CWS (incl. their area) for selected CRs.
Table 5. Means and standard deviations of the WB components (see Equations (1) and (3)) and categories of CWS (incl. their area) for selected CRs.
Crop RotationsWB
(mm)		r1.α.P
(mm)		r2.ASWS
(mm)		AARG
(mm)		CWR
(mm)		SAWCmin
(%)		Categories of CWS
M. ± s.d.1–2 (%)2–3 (%)3–4 (%)
C-8−114 ± 55.9252 ± 22.152.5 ± 13.334.9 ± 21.3453 ± 16.0532.79 ± 0.6815.057.727.3
C-10−107 ± 55.1245 ± 30.356.3 ± 13.730.8 ± 19.9439 ± 16.7522.74 ± 0.7513.664.322.2
W-S−95 ± 57.4255 ± 20.958.4 ± 14.232.7 ± 22.5441 ± 17.8492.57 ± 0.7532.947.219.9
W-D−118 ± 54.1241 ± 20.754.4 ± 13.533.2 ± 20.7446 ± 15.5542.84 ± 0.727.162.230.7
Table 6. Means of the WB and categories of CWS for texture classes of selected CRs. Letters indicate significant differences between CRs according to Dunn’s test (texture classes with the same letters are not significantly different).
Table 6. Means of the WB and categories of CWS for texture classes of selected CRs. Letters indicate significant differences between CRs according to Dunn’s test (texture classes with the same letters are not significantly different).
Texture ClassesSandyLoamy SandsSandy LoamsLoamyClay LoamsClayey
Crop RotationsWB
(mm)		CWSWB
(mm)		CWSWB
(mm)		CWSWB
(mm)		CWSWB
(mm)		CWSWB
(mm)		CWS
C-8−209 a4.00 a−157 a3.49 a−105 a2.64 b−75 b2.27 b−104 b2.54 a−148 a3.11 ab
C-10−202 b4.00 a−150 b3.45 a−98 b2.56 c−68 c2.21 c−98 c2.48 b−142 a3.07 cb
W-S−193 c4.00 a−142 c3.33 b−85 c2.38 d−54 d2.01 d−86 d2.28 c−132 b2.96 c
W-D−211 a4.00 a−161 a3.52 a−109 a2.70 a−80 a2.33 a−109 a2.58 a−152 a3.18 a
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Placatová, R.; Papaj, V.; Fučík, P.; Brázda, J.; Pacek, L.; Tlustoš, P. Evaluation of the Long-Term Water Balance in Selected Crop Rotations with Alfalfa in a Soil-Heterogeneous Lowland Region of the Czech Republic. Agronomy 2024, 14, 1692. https://doi.org/10.3390/agronomy14081692

AMA Style

Placatová R, Papaj V, Fučík P, Brázda J, Pacek L, Tlustoš P. Evaluation of the Long-Term Water Balance in Selected Crop Rotations with Alfalfa in a Soil-Heterogeneous Lowland Region of the Czech Republic. Agronomy. 2024; 14(8):1692. https://doi.org/10.3390/agronomy14081692

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Placatová, Renata, Vladimír Papaj, Petr Fučík, Jiří Brázda, Lukáš Pacek, and Pavel Tlustoš. 2024. "Evaluation of the Long-Term Water Balance in Selected Crop Rotations with Alfalfa in a Soil-Heterogeneous Lowland Region of the Czech Republic" Agronomy 14, no. 8: 1692. https://doi.org/10.3390/agronomy14081692

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