# Assessing crop water deficiency for regional adaptation to climate change

Séverin Yvoz, Martin Lechenet, Philippe Amiotte-Suchet, Thierry Castel,

Elisa Betting, Marjorie Ubertosi

► To cite this version:

Séverin Yvoz, Martin Lechenet, Philippe Amiotte-Suchet, Thierry Castel, Elisa Betting, et al. Assessing crop water deficiency for regional adaptation to climate change. Agricultural Water Management, 2025, 314, pp.109525. 10.1016/j.agwat.2025.109525. hal-05063692

HAL Id: hal-05063692

https://institut-agro-dijon.hal.science/hal-05063692v1

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# Assessing crop water deficiency for regional adaptation to climate change

Séverin Yvoz<sup>a,b,*</sup> <sup>10</sup>, Martin Lechenet<sup>b</sup>, Philippe Amiotte-Suchet<sup>c</sup>, Thierry Castel<sup>c</sup>, Elisa Betting<sup>d</sup>, Marjorie Ubertosi<sup>a,**</sup> <sup>10</sup>

## ARTICLE INFO

### Keywords:

Water balance model

Evapotranspiration

Precipitation

Soil water-holding capacity

Crop coefficient

Yield response to water

Growth stage

## ABSTRACT

The increase in crop water stress is one of the most critical effects of climate change and threatens farm production and food security both locally and globally. The adaptation process of the entire food system must be grounded in a local quantitative assessment of future water requirements and the consequences for production losses if those requirements are not met. Thus, we developed a high-resolution crop water requirement model which integrates climate, soil and crop characteristics at a daily timestep. Our prospective study for 2100 (using the RCP8.5 climate scenario) at the regional scale (Bourgogne-Franche-Comté, BFC, in Eastern France) reveals a projected 8% decrease in average grain production and a 14% reduction in forage production. However, crop response to climate change vary both spatially and temporally. Summer crops are expected to bear a more marked impact from water stress, with an increase in variability over the years, amplifying the risk for human and animal food security. Other aspects of climate change, such as heat stress, late-spring frost, or an increase in pest pressure, could further impact yields. We emphasize the necessity of addressing climate change adaptation at the regional system level through multi-actor collaboration. Our reproducible approach serves as a rational starting point for designing new strategies based on a combination of adaptive responses at different scales, spanning from the field to the regional scale.

## 1. Introduction

Climate change, driven by anthropogenic greenhouse gas emissions, has led to an overall increase in mean air temperature, altering the regional water cycle and crop production performance (Laurent et al., 2023). With high confidence, the Intergovernmental Panel on Climate Change predicts that agricultural production will be negatively impacted by the reduction in the water available to plants due to climate change (Caretta et al., 2022). The decrease in water availability for plants directly results from an increase in evapotranspiration combined with a more uneven distribution of precipitation (Teuling et al., 2013). The frequency of drought is expected to double by 2050, which will considerably damage crop yields and threaten food production in most cultivated areas worldwide (Zampieri et al., 2017).

Studies evaluating the impact of climate change on water deficiency and crop yield have shown that these effects may vary between regions, with potentially positive local impacts (Mohammadi et al., 2023; Zhang

and Cai, 2013; Zhu et al., 2022). Moreover, inter-annual variability in crop production is expected to increase under climate change (Challinor et al., 2014), thereby heightening uncertainty for farmers regarding future yields. To provide more precise guidance to farmers, it is therefore essential to assess the impacts of future climate change on crop water balance and yields at a finer spatial scale. Along with soil and climatic conditions, crop type determines the spatial distribution of water requirements and water deficiency over time (Doorenbos and Kassam, 1979; Kapsambelis et al., 2022). The regional scale is thus well suited for assessing the consequences of climate change on water balance and crop production and is also relevant for designing and implementing local strategic adaptations.

Here, we estimate the potential effects of climate change on crop water deficiency, relative yield loss, and food production by focusing on the Bourgogne-Franche-Comté region (BFC), which is located in central-eastern France. This location was chosen following the request of local agricultural stakeholders to assess the potential impacts of climate

<footnote><sup>a</sup> Institut Agro Dijon, INRAE, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Agroécologie, Dijon Cedex F-21065, France</footnote>

<footnote><sup>b</sup> Union de sociétés coopératives agricoles Alliance BFC, Service R&D, 4 Boulevard Beauregard, Longvic 21600, France</footnote>

<footnote><sup>c</sup> Biogéosciences, UMR 6282 CNRS, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, 6 Boulevard Gabriel, Dijon 21000, France</footnote>

<footnote><sup>d</sup> Direction Départementale des Territoires de Côte-d'Or, 57 Rue de Mulhouse, Dijon 21000, France</footnote>

<footnote>* Corresponding author at: Institut Agro Dijon, INRAE, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Agroécologie, Dijon Cedex F-21065, France.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: s.yvoz@alliance-bfc.coop (S. Yvoz), marjorie.ubertosi@institut-agro.fr (M. Ubertosi).</footnote>
change to better anticipate and prepare for the adaptation of farming
systems. Indeed, the BFC region has limited available water resources, as
it lies at the head of three main river basins in France (Loire, Rhône and
Seine), causing water to rapidly flow downstream. In addition, many
areas are characterised by shallow soils overlaying karstic subsoils,
which limit water retention capacity. We simulate water deficiency and
relative yield losses for 14 main crops in the BFC from 2000 to 2100
utilizing a simple spatially distributed water balance model incorpo-
rating soil characteristics, historical and future daily climate data
(RCP8.5), crop water requirements, and crop spatial distribution within
the BFC. To evaluate regional-scale food production, we converted crop
yields into energy equivalents (Lechenet et al., 2017). Our goal was to
assess how different crops will respond to climate change in terms of
both water deficiency and relative yield losses in the future. Addition-
ally, we aim to estimate and discuss the impacts of climate change on
total crop production and its inter-annual variability in the BFC
(Stocker, 2014; Zhu et al., 2022).

## 2. Material and methods

### 2.1. Study area

Our study focused on the Bourgogne-Franche-Comté (BFC) region in central-eastern France. This region covers 47,783 km², representing 8.8 % of the French territory. The region is characterized by a large alluvial plain (the Saône Plain) surrounded by medium mountain areas from the northeast to the southwest (the Vosges, the Jura and northern Alps mountains and the Massif Central), by calcareous plateaus in the north and north-west and by rolling hills and plateaus in the central and eastern parts of the region. The crop distribution is mainly determined by these subregions, with grasslands in the medium mountains, winter crops in the rolling hills and plateaus, and summer crops concentrated in the plain. The maximum elevation is 1495 m in the Jura massif. The climate is generally semicontinental, with an average temperature of 10.5°C during the 2000-2019 period (minimum of 2.3°C in January and maximum of 19.4°C in July). The annual rainfall averages 970 mm per year and is quite uniform throughout the year, although October-November (autumn) and May (spring) are generally wetter than other months. However, we observed significant differences in terms of rainfall quantity between the plains (~700 mm) and the medium mountains (~1400 mm). As this region is at the head of three main river basins in France (Loire, Rhône and Seine), water resources are generally transient with fragmented aquifers, making most parts of the BFC susceptible to drought periods.

### 2.2. Data collection

#### 2.2.1. Soil water-holding capacity

The French National DoneSol database (Grolleau et al., 2004) iden-
tifies 1360 homogeneous land treatment units in the BFC, from which
the proportions of clay, silt, sand, coarse elements, organic matter and
thickness were extracted. Based on these data, textural classes were
identified for each soil horizon using the methodology developed by
Jamagne (1967). The soil water-holding capacity for these land treat-
ment units was then estimated using the methodology developed by
Bruand et al. (2002), considering field capacity at –100 hPa water po-
tential. The presence of coarse elements in the soil was not integrated
into the soil water-holding capacity estimation, even though some
studies have suggested that they can contribute to it (Tetegan et al.,
2011), due to a lack of information on their composition in the database.

#### 2.2.2. Daily climate data

To estimate water deficiency over the historical period 2000–2019,
we utilized daily climate data obtained from the 8 × 8 km SAFRAN
product (Météo France Data Services WWW Document, 2022). Specif-
ically, we collected daily cumulative rainfall and reference crop

evapotranspiration (ET<sub>ref</sub>), which were calculated using the
Penman-Monteith formula (Penman, 1948) for 857 grid points within
the BFC. The SAFRAN model employs an optimal interpolation (OI)
technique (Quintana-Seguí et al., 2008), which adjusts background
values from the ARPEGE model (Pailleux et al., 2021) against nearby
observed data using a linear regression weighted by distance.

To simulate future water deficiency in response to climate change, we used simulated daily climate data generated by the ARPEGE model, applied under the IPCC A2 emission scenario (Intergovernmental Panel on Climate Change Ed. 2000) (RCP8.5), which was been downscaled to an 8 × 8 km resolution (same grid as the SAFRAN product) using the Weather Research and Forecasting Model (WRF) driven by ERA-INTERIM reanalysis data (Boulard et al., 2017; Xu et al., 2012). These dynamically downscaled data are particularly relevant for the BFC and have been utilized in numerous prior studies on climate change impacts (Boulard et al., 2016; Brulebois et al., 2018). We chose to focus on the RCP8.5 scenario to assess the potential impacts of climate change in the worst-case scenario. While recent studies suggest that RCP8.5 may no longer represent the most probable trajectory due to ongoing mitigation efforts, it remains a valuable tool for stress-testing agricultural systems and evaluating adaptation needs under severe climate conditions. However, it is worth noting that alternative data or emission scenarios could also be utilized.

To identify 20-year periods with no shift in average temperature, we applied a frequentist change-point approach to the average annual temperature in the BFC (Brulebois et al., 2015). Our analysis revealed two shifts in the average annual temperature increase by the end of the century, occurring in 2048 and 2072 (Supplementary Fig. 1). Consequently, we defined three future periods of climate analysis: a short-term period (2030–2049), a mid-term period (2050–2069) and a long-term period (2080–2099).

#### 2.2.3. Homogeneous pedoclimatic units

As the daily water balance depends on the daily rainfall, daily ET<sub>ref</sub> and soil water-holding capacity, it is imperative to delineate areas within the BFC with homogeneous pedoclimatic conditions to calculate the daily water balance. To achieve this goal, we conducted a spatial intersection between 857 climate units of 8 × 8 km and 1360 land treatment units in the BFC. To minimize the creation of overly small pedoclimatic areas, we eliminated areas that were less than 48 ha when separating two land treatment units due to the constraints of climate cells. In such instances, the climate data from the neighbouring square were assigned to the smaller area. This approach resulted in the identification of 8478 pedoclimatic units within the BFC. The pedoclimatic units cover from 0.37 to 6155 ha with a median size of 320 ha (first and third quartiles of 138 and 703 ha respectively, Supplementary Fig. 2).

#### 2.2.4. Daily crop coefficients

We estimated crop water requirements using the single crop coeffi-
cient ($K_c$) proposed by the FAO (Allen et al., 1998). For the 14 main
crops in the BFC, including grassland, winter wheat, winter barley,
winter oilseed rape, grain and forage maize, spring barley, sunflower,
soybean, winter peas, spring peas, triticale, grain and forage sorghum,
we collected $K_c$ at the seedling stage, flowering stage and harvest from
FAO references (Allen et al., 1998). The periods associated with the
seedling, vegetative, flowering, and fruiting stages (sowing dates and
growth stage durations) of the 14 crops in the BFC context were esti-
mated based on regional expert assessments. This enabled us to establish
the daily distribution of $K_c$ from sowing to harvest (Supplementary
Table 1). In cases where no crop was present (between the crop harvest
and next crop sowing), we defined the daily $K_c$ as 0.52 (Lemaire et al.,
2006).

#### 2.2.5. Crop surface in each pedoclimatic unit

To estimate crop water deficiency at the regional scale, it is necessary
to determine the area occupied by the 14 studied crops in each
pedoclimatic unit. Based on the Land Parcel Identification System database (the French RPG), we calculated the average area covered by crops in each pedoclimatic unit during the 2016–2020 period.

## 2.3. Water balance simulation

### 2.3.1. Two reservoirs model of water balance

The water balance was estimated daily using the methodology developed by Jacquart and Choisnel (1995). The simulation started the first of January of the year preceding the study period, and outputs from the initial year were discarded (one-year spin-up period) for initialization of the model. Initially, the soil water-holding capacity was divided into two reservoirs: R1 represents easily accessible water and equals 40 % of the soil water-holding capacity, while R2 represents water less accessible to crops and comprises the remaining 60 % of the soil water-holding capacity. Subsequently, at a daily timestep, the following calculations were applied:

1. We estimated the crop evapotranspiration under standard conditions (ET<sub>c</sub>) of crops by multiplying ET<sub>ref</sub> by K<sub>c</sub>.

2. If the water available in R1 exceeded ET<sub>c</sub>, then the actual evapotranspiration of the crop under non-standard conditions (ET<sub>c act</sub>) equalled ET<sub>c</sub>, and we subtracted ET<sub>c act</sub> from R1. Otherwise, we assumed that the crop extracted water from R2, but only partially with a coefficient equal to the filling rate of R2, and we subtracted ET<sub>c act</sub> from R2. Therefore, the less filled R2 is, the smaller the ET<sub>c act</sub> is compared to the ET<sub>c</sub>.

3. The daily water deficiency was defined as the difference between the ET<sub>c</sub> and ET<sub>c act</sub> (Allen et al., 1998).

4. Finally, we added daily rainfall to the soil water reservoir, prioritizing R1. If both R1 and R2 were full, we considered that daily rainfall ran off or drained.

This two-reservoir model of water balance was applied to the 8478 pedoclimatic units and four 20-year periods for the 14 main crops. Each crop was evaluated individually, assuming no rotation between crops, which does not reflect the real scenario in our study case.

### 2.3.2. Yield response to water deficiency

For the 14 main crops, we converted water deficiency at each growth stage into relative yield loss by multiplying the proportion of water deficiency (1-(ET<sub>c act</sub>/ET<sub>c</sub>)) by the coefficient of yield response to water (Doorenbos and Kassam, 1979) (K<sub>y</sub>) estimated for the four growth stages (Supplementary Table 1). The relative yield loss during the year was defined as the mean relative yield loss of the four growth stages, weighted by its proportion of crop evapotranspiration under standard conditions (ET<sub>c</sub>) compared to the entire life cycle (Eq. 1; see Supplementary Method 1 for details on method selection). The yield loss was then calculated by multiplying the relative yield loss by the yield potential. The yield potential of the 14 crops (Supplementary Table 2) was estimated for eight pedoclimatic areas of the region and corresponds to the observed (2016–2022) farmer field yields for which zero relative yield losses were calculated (see Supplementary Method 2 for details).

$$Relativeyieldloss_{total} = \sum_{i=1}^{4} (Relativeyieldloss_{stage i}) \times \frac{ET_{c i}}{\sum_{i=1}^{4} ET_{c i}} \quad (1)$$

To compare the total production of the 14 main crops in the BFC, we converted the estimated yields in terms of food production. Food production was calculated by multiplying the crop production in the BFC (in metric tons) by the energy content of each crop product (in GJ.t<sup>-1</sup>, Supplementary Table 3), estimated using the higher heating value (Feedipedia et al., 2022; Lechenet et al., 2017).

## 2.4. Analyses of simulation outputs

All the statistical analyses were conducted using R software version 4.2.2 (R Core Team, 2022).

For each growth stage, crop, and pedoclimatic unit, two coefficients were calculated: the stress coefficient (K<sub>s</sub>), which is the ratio between the ET<sub>c act</sub> and ET<sub>c</sub>, and the relative yield loss multiplying K<sub>s</sub> by K<sub>y</sub>. Subsequently, the annual K<sub>s</sub> and relative yield loss were computed at the annual scale (Eq. 1) for each crop and pedoclimatic unit. The crop yield for each crop and pedoclimatic unit was estimated by multiplying the crop yield potential by 1 minus the relative yield loss.

At the pedoclimatic unit scale, the area of the 14 main crops the land used for agriculture was utilized to estimate the K<sub>s</sub>, relative yield loss and food production of the agricultural landscape. The water requirements and food production in the BFC were determined as the sum of the water requirements and food production of all pedoclimatic units, weighted by their cropped area.

For the three future periods, changes in stress coefficient and relative yield loss were defined as the differences between the values of the future and historical (2000–2019) periods. The yield change was calculated as the percentage difference between the future and historical yields (Eq. 2).

$$Yieldchange = 100 \times \frac{(Futureyield - Historicalyield)}{Historicalyield} \quad (2)$$

To assess the impact of water stress induced by climate change on the inter-annual variability of our 14 crops, we computed the standard deviation of K<sub>s</sub>, relative yield loss, and food production for each pedoclimatic unit, crop, and for the three future periods. We analysed the effects of crop type, period, and their interaction using a mixed linear model implemented with the lmer function from the [lme4] R package. The significance of these effects was evaluated using a type II ANOVA conducted with the ANOVA function from the [car] R package. Additionally, comparisons within factors were estimated using the [emmeans] R package.

To examine the spatial distribution of inter-annual variability in K<sub>s</sub>, relative yield loss and food production, we computed the average value for each year and pedoclimatic unit across the 14 crops, weighted by their respective areas. Subsequently, we calculated the standard deviation over the 20 annual values of each time-period. Furthermore, we assessed the effects of landscape area (see Supplementary Method 2 for details), period and their interaction by employing a mixed linear model.

## 2.5. Sensitivity of the water balance model

In our modelling approach, we made several simplifications regarding i) the incorporation of coarse elements in the estimation of soil water holding capacity, ii) the representation of crop succession and iii) the adjustment of crop growing periods. We evaluated the sensitivity of our water balance model to these assumptions, as outlined in Supplementary Method 3. In summary, the adjustment of crop growing periods is the only factor that significantly impacts the results, with both the sowing date and the length of the growing cycle having a significant effect on the results of the crop water balance (p-value < 0.001 with 80640 observations). However, in the absence of precise indications regarding future changes in sowing dates and growth stage durations, we decided to retain historical values for the four studied periods.

# 3. Results

## 3.1. Crop sensitivity to water deficiency

Our study focused on 14 main crops representing 95 % of the regional cultivated area. For each year of the historical period (2000–2019) and each pedoclimatic unit of the BFC (8478 units), we
estimated the stress coefficient ($K_s$, Supplementary Fig. 3). In the agricultural landscape, in one quarter of the field area, $K_s$ was less than 75 % for the entire crop cycle and less than 65 % at the flowering stage. However, we showed that sunflower, spring barley, and spring peas are more susceptible to water deficiency than are winter wheat, winter barley, oilseed rape and grassland (Fig. 1). Compared with the bottom quartile of the distribution, grassland was the least sensitive crop at all growing stages, with a $K_s$ of 82 %. The $K_s$ values of summer (grain and forage maize, sunflower, soybean and sorghum) and spring crops (spring barley and spring peas) are between 59 % and 65 % (73 % for sorghum), while the $K_s$ values of winter crops (winter wheat, winter barley, oilseed rape, triticale and winter peas) are between 68 % and 74 % (Supplementary Fig. 3). Summer crops are mainly impacted at the flowering stage, while winter and spring crops are more affected at the fruiting stage (Supplementary Fig. 3). The interannual variability in $K_s$ was significantly greater for summer crops (standard deviation (sd) > 0.130) than for winter crops (sd < 0.096). It should also be noted that

oilseed rape is more sensitive to water deficiency than other crops at the seedling stage, with 10 % of the situations having a $K_s$ lower than 72 %, threatening its good establishment. These results confirm that spring and summer crops are more subject to water deficiency, highlighting the need to assess the associated relative yield losses.

For each year (2000–2019) and each pedoclimatic unit, we estimated the relative yield losses (compared to the optimal yield) in the BFC (Supplementary Fig. 3). At the agricultural landscape scale, one-quarter of the field area shows relative yield losses greater than 18 %, with 12 % occurring at the flowering stage. However, spring crops, sunflower and maize are more impacted than winter crops (Fig. 1). For sunflower and maize, one-quarter of the field area shows relative yield losses greater than 31 %, contrasting with 12–19 % for winter crops, 18 % for grassland and 21–26 % for spring crops (Supplementary Fig. 3). The relative yield losses of spring and summer crops (except for sorghum and spring peas) mainly occur at the flowering stage, while the winter cereal relative yield losses are evenly distributed between the flowering and

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Stress coefficient and relative yield loss. Average stress coefficient ($K_s$, in blue) and relative yield loss (in brown) at the regional scale for 14 main crops and four studied periods. Error bars represent the standard deviation over the 20 years of each period.</figcaption></figure>
fruiting stages. Summer crops, except sorghum, also have significantly greater interannual variability in relative yield losses (sd > 0.104) than winter crops (sd < 0.063).

We observed high regional differences in grain and forage crop production (Figs. 2a and 3a), as well as in the Ks and the distribution of relative yield losses (Supplementary Fig. 4a). Ninety percent of the mean annual crop production values by pedoclimatic unit are between 5.0 and 8.1 t·ha⁻¹ of wheat equivalent for grain crops and between 5.5 and 9.5 t·ha⁻¹ of grass dry matter for forage crops. Similarly, 90 % of the pedoclimatic units had mean Ks values between 70 % and 96 %, leading to a mean annual relative yield loss ranging from 4 % to 23 % (Supplementary Fig. 4a). The interannual variability in food production also varies spatially, with 90 % of the interannual coefficient of variation (cv) ranging from 5.6 % to 14.9 % across the 8478 pedoclimatic units.

## 3.2. Evolution of food production in the 21st century

Even though the decrease in the average production in the BFC in the short term (2030–2049) appears to be very low for grain (-0.9 %) and forage crops (-1.5 %), the evolution of water stress consequences will make it substantial in the long term (2080–2099). With respect to grain crops, production in wheat equivalent is expected to decrease by 7.7 % (Figs. 2b-d and 3b-d) in response to a decrease in the stress coefficient Ks (from 80 % to 70 %) and an increase in the relative yield loss (from 13 % to 19.5 %) (Fig. 1 and Supplementary Figs. 4a, b, 5 and 6). For forage crops, production in dry matter equivalent is expected to decrease by 14 % in response to a decrease in the stress coefficient (with Ks varying from 88 % to 76 %) and an increase in relative yield loss (from 12 % to 24 %). Globally, we estimated an average annual yield loss of 12 % by the end of the century, representing a loss of 440 kt of wheat equivalent plus 1313 kt of grass dry matter equivalent each year (Fig. 4).

The link between the stress coefficient and relative yield loss depends on crop type and the stage at which water stress occurs. Indeed, summer crops are predicted to suffer a yield loss increase in the short term, while grassland, spring and winter crops will suffer a significant yield loss increase only in the long term (Fig. 1). Moreover, summer crops seem to be more impacted by climate change at the flowering and vegetative stages, while grassland, spring, and winter crops are impacted mainly at the fruiting and flowering stages (Supplementary Figs. 5 and 6).

Crop production exhibits various spatial and temporal responses. For grain crops, 90 % of the pedoclimatic units exhibit relative yield losses between 5 % and 26 % in the short term and between 10 % and 33 % in the long term. For forage crops, 90 % of the pedoclimatic units exhibit a relative yield loss between 3 % and 27 % in the short term and between 11 % and 38 % in the long term. The interannual variability in relative yield loss will increase by the end of the century for spring and winter crops (sd from 6.3 to 6.9), while it will remain stable for grasslands and decrease for summer crops (sd from 14.1 to 9.1).

## 3.3. Changes in water deficiency

Water deficiency is the difference between the annual maximal evapotranspiration (ETc) and the annual actual evapotranspiration (ETc act) for a given crop. For the 2000–2019 period, maize, sunflower and soybean were the crops that exhibited the greatest water deficiency, with an average deficit of more than 105 mm·yr⁻¹ per year in the BFC, while winter cereals and grassland suffered deficits of less than 80 mm·yr⁻¹. Conversely, sorghum had the lowest deficiency, with only 62 mm·yr⁻¹ per year on average in the BFC. Our simulations indicate that water deficiency is projected to increase for all crops in the future. By the end of the century, the water deficiency of maize and sunflower could exceed 220 mm·yr⁻¹ per year on average, with that of soybean reaching 195 mm·yr⁻¹. Even grasslands and winter cereals are expected to face

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Distribution and evolution of grain crop production. a) Distribution of historical grain crop production (2000–2019) in the Bourgogne-Franche-Comté region in tons per hectare of wheat equivalent and changes in grain crop production due to water deficiency in three future periods: b) short term (2030–2049), c) mid term (2050–2069) and d) long term (2080–2099). The values in each pedoclimatic area are weighted by the proportion of each crop.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Distribution and evolution of forage crop production. a) Distribution of historical forage crop production (2000–2019) in the Bourgogne-Franche-Comté region in tons per hectare of grass equivalent and changes in forage crop production due to water deficiency for three future periods: b) short term (2030–2049), c) mid term (2050–2069) and d) long term (2080–2099). The values in each pedoclimatic area are weighted by the proportion of each crop.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Average annual energy production (in GJ) per crop in the BFC, considering the historical agricultural land-use (based on the average crop areas observed during the 2016–2020 period). Winter crops are drawn in a blue palette, spring crops in light green and summer crops in a red palette.</figcaption></figure>

high water deficiency by the end of the century, with more than 142 mm.yr⁻¹ on average at the regional scale. In contrast, sorghum is expected to have the lowest water deficiency, with an average value of 102 mm.yr⁻¹.

Currently (2000–2019), the total water deficiency of 14 main crops amounts to 1.7 billion m³ per year on average in the BFC (Fig. 5). This represents 19 % of the total deep drainage (i.e., water drained out of the

soil) on the arable area of the BFC each year. This total water deficiency could increase by 6 % in the short-term and by 40 % in the mid-term and could double in the long-term future, reaching 3.5 billion m³ (Fig. 5) and representing 30 % of the total deep drainage on the arable area of the BFC. At the regional scale, the inter-annual variability in water deficiency appears to increase by the end of the century (i.e., sd of 1.0 billion m³ per year currently to 1.4 billion m³ in the long-term future).
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Total annual water deficiency. The total annual water quantity shortcomings for the 14 main crops compared to the total annual amount of water that escapes via deep drainage over the arable area of Bourgogne-Franche-Comté in four periods. Error bars represent the standard deviation over the 20 years of each period.</figcaption></figure>

## 4. Discussion

Our study confirms that summer crops in the BFC currently exhibit greater water deficiency than winter crops, which is consistent with previous studies (Łabędzki and Bąk, 2017; Raz-Yaseef et al., 2015). Summer crops also exhibit greater inter-annual variability in water deficiency than winter crops due to their greater sensitivity to drought (Daryanto et al., 2017), as their growing cycle occurs during summer, which is the hottest and driest season. Summer crops are thus more sensitive to the soil water-holding capacity, so these crops are currently cultivated in pedoclimatic areas with the highest soil water-holding capacity or with irrigation capabilities (Van der Velde et al., 2010). Shallow soil areas have the lowest food production and the greatest contrast between winter and summer crops, as the water-holding capacity and filling dynamics are important factors determining the impact of drought on yield (Kang et al., 2009; Li et al., 2021). Sorghum is the only summer crop we studied with a different pattern and exhibits a relative yield loss similar to that of winter crops, owing to its transpiration efficiency and stay-green capacity during drought (Beyene et al., 2015).

Water stress due to climate change is expected to impact summer crops even in the short term, leading to a yield decline of more than 14% by the end of the century. The significant decrease in the inter-annual variability in yields we highlighted for summer crops hides a dramatic increase in the average annual water stress and yield losses: yields for summer crops are predicted to stay consistently lower. For winter crops, even if yield losses and stress coefficient do not significantly change in the short term, we identified a negative impact of water stress in the long term, leading to a 5% reduction in yield and an increase in inter-annual yield variability. This indicates that the increase in the simulated rainfall will initially benefit winter crops but will not be sufficient to offset the increase in water requirements (Liu et al., 2021) by the end of the century. Our results show that summer crops are mainly impacted by water stress at the flowering stage. This is consistent with the findings of Ceglar et al. (2016), who analysed the inter-annual crop yield variability in France and showed that maize yield is mainly affected at the flowering stage, while the impact of meteorological variables is more dispersed across the growing season for winter wheat. This tendency is also observed in our study for winter and spring crops. In particular situations, such as in mountainous regions, Torriani et al.

(2007) demonstrated an increase in winter wheat production due to climate change, but this was only valid for this specific crop.

Although increased water stress will be a major cause of production losses by the end of the century (FAO, 2022), other factors will also significantly affect crop production. First, climate change could increase yield loss due to temperature stress (Liu et al., 2021; Yang et al., 2017; Zhao et al., 2017), which may amplify the impact of water stress (Pradhan et al., 2012). Moreover, plant growth can be negatively impacted by certain temperatures, disrupting biological functioning (Porter and Gawith, 1999). Additionally, excess water can negatively impact yield and can be as detrimental as water deficiency for winter wheat (Van der Velde et al., 2012). Conversely, temperature increases could mitigate the negative impact of water deficiency by reducing or shifting crop growing periods (Baum et al., 2020; Lamichhane, 2021). Similarly, changes in crop cycles, modifications of sowing date, and cultivar selection could also have positive effects (Minoli et al., 2022; Toreti et al., 2022), although the benefits may vary locally and between crops. Shortening the crop growing period could also be an opportunity to grow a second crop each year, but water stress could hinder the sowing of this second crop (Zhu et al., 2022). Finally, the increase in CO₂ concentration could enhance plant functioning and thereby water efficiency in biomass production (McGrath and Lobell, 2013; Olesen and Bindi, 2002). Therefore, other impacts of climate change, changes in crop biological cycles, and technical adaptation could be integrated into the simulation to assess the extent to which they could negatively or positively affect crop production (Farooq et al., 2023; Rezaei et al., 2023).

In this study, we assumed fixed crop calendars across all four time periods. However, climate change may affect the duration of crop growth stages, and farmers may adjust their practices accordingly (e.g., changes in sowing or harvesting dates), potentially mitigating some of the projected impacts. We also used average crop coefficient (K_c) values from the FAO database (Allen et al., 1998), although these values can vary depending on local pedoclimatic conditions and the crop cultivars used (Petry et al., 2024). The accuracy of our results could be improved by accounting for potential changes in crop phenology and farmer adaptation strategies, as well as by using K_c values better tailored to pedological conditions and crop cultivars of BFC.

According to our results, 1.5 billion m³ would be needed each year in the BFC to maintain current food production levels. At the regional
scale, this represents 5 % of the cropped area converted to water storage (assuming 3 m deep reservoirs) and 3.2 % of the annual precipitation, which is technically, environmentally and socially impossible to implement. Changes of rainfall distributions could exacerbate conflicts between stakeholders over water access (Flörke et al., 2018; Rosegrant and Ringler, 1998). Inter-seasonal water storage and farm adaptations (Bodner et al., 2015) cannot solely form the basis of food-securing strategies in response to climate changes. Instead, these strategies must involve a combination of local transformations involving a wide range of stakeholders and the establishment of new local food supply chains. Depending on the context, food security strategies could involve alternative water sourcing (e.g., the reuse of treated wastewater), the adoption of new technologies to improve irrigation efficiency, the introduction of more drought-tolerant cultivars or alternative crops, and the implementation of agricultural practices aimed at reducing crop water requirements or enhancing soil water retention capacity. This necessitates high cooperation for maximum relevance and sustainability at the territorial level.

## 5. Conclusion

Our study projects a 12 % reduction in crop production by the end of the century due to a significant increase in water deficiency, which could double. The effects of climate change will vary spatially, temporally, and among different crops. Summer crops are expected to be affected in the short term, while spring and winter crops will face impacts in the long term. Additionally, interannual yield variability is anticipated to increase, especially for winter crops, posing a threat to food security. Effective adaptation to climate change will require a combination of local strategies, including water storage, crop and cultivar selection, sowing date adjustments, and soil health management, involving a wide range of stakeholders that should work together. This will require collaboration among all stakeholders involved in climate change and water management, extending beyond the agricultural sector.

## Funding

This work was supported by the French research stimulus plan "France Relance" and the agricultural cooperatives that are members of the agro-environmental platform Artemis in Bourgogne Franche-Comté.

### CRediT authorship contribution statement

**Amiotte-Suchet Philippe:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.
**Castel Thierry:** Writing – review & editing, Validation, Resources, Methodology.
**Betting Elisa:** Writing – review & editing, Methodology, Formal analysis.
**Ubertosi Marjorie:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Funding acquisition, Conceptualization.
**Yvoz Séverin:** Writing – original draft, Methodology, Formal analysis.
**Lechenet Martin:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We thank F. Martin for his review of the article and his relevant suggestions. We also thank H. Martin and M. Mimeau for their regional expert assessments of the crop growing cycles.

## Author contributions

M.U., P.A.S. and M.L. conceived the project; M.U., T.C. and M.L. contributed to the data collection; all the authors contributed substantially to the methodology development; S.Y. and E.B. analysed the data; and S.Y. wrote the paper, with substantial input from all the authors.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agwat.2025.109525.

## Data Availability

Data will be made available on request.

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