# Exploring the environmental performance of agricultural land planning scenarios over time in a context of climate change

Nicolas Rogy, Agata Sferratore, Nicolas Géhéniau, Arnaud Hélias, Eléonore Loiseau

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Nicolas Rogy, Agata Sferratore, Nicolas Géhéniau, Arnaud Hélias, Eléonore Loiseau. Exploring the environmental performance of agricultural land planning scenarios over time in a context of climate change. Cleaner Environmental Systems, 2026, 20, pp.100397. 10.1016/j.cesys.2026.100397. hal-05491523

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# Exploring the environmental performance of agricultural land planning scenarios over time in a context of climate change

Nicolas Rogy<sup>a, *</sup>, Agata Sferratore<sup>d</sup>, Nicolas Géhéniau<sup>e</sup>, Arnaud Hélias<sup>b, c</sup>,
Eléonore Loiseau<sup>b, c</sup>

## ABSTRACT

Agricultural areas are faced with the dual challenge of reducing their environmental impacts while adapting to climate change. Adaptations rely on strategies such as irrigation or cultivated crop diversification, whose environmental performance over time is affected by climate change. Such environmental performance can be studied by combining Territorial Life Cycle Assessment (T-LCA) with crop modeling. However, the identification of optimal planning scenarios within the multiplicity of adaptation possibilities that could be assessed hinder the use of classical scenario analyses that could be costly in computing time and not rigorous enough to cover every scenario. Therefore, a methodology coupling experimental design with T-LCA and crop modeling is conceived to investigate the environmental performance of various agricultural planning scenarios in a climate change context. The usefulness of this methodology is proven through a theoretical case study of an agricultural territory using different crop combinations, with or without irrigation, within the context of the worst-case IPCC scenario of climate change (SSP5-8.5). Environmental performance is quantified with eco-efficiency. Eco-efficiency means values and variabilities are calculated over the next 30 years to identify scenarios maintaining highest performance over time. The proposed innovative coupling enables a full investigation of the environmental performance of agricultural land management scenarios integrating different modalities. This study demonstrates that under low interannual variability of fertilisation practices, irrigation and crop diversity contribute to the stabilisation of environmental performance related to the impacts on climate change. Further developments are required for a more robust modelling of agro-ecosystem functioning for application to real-world cases.

## 1. Introduction

New farming practices and organisational structures have become vital to reduce the environmental impacts of agricultural territories, and adapt them to climate change (Whitfield et al., 2018). Advanced methods are required for quantifying the environmental performance of these emerging land planning scenarios in order to support decision-making (Loiseau et al., 2012). Territorial Life Cycle Assessment (T-LCA) is an adaptation of the standardized LCA framework used to assess the environmental performance of mesoscale systems, such as territories (Loiseau et al., 2013). To consider territorial multifunctionality, T-LCA proposes to evaluate two types of indicators (contrarily to standard LCA, which only quantifies environmental impacts): i) a set of services provided by a given land planning scenario, and ii) the induced environmental impacts. Both indicators can be further combined into a single metric, called eco-efficiency, as defined by (Seppäläa et al., 2005). This ratio (i.e. ratio of the service provided by

the territory with a single environmental impact) is used to compare the environmental performance of land planning scenarios that do not offer similar levels of services. Hence, for a given unit of impact (e.g. 1 tonne of CO2 eq.), the scenario providing the highest level of service (e.g. job creation or added value) is identified. The T-LCA framework was specifically implemented to identify environmental hotspots of local food systems (Lulovicova and Bouissou, 2024), and to compare the environmental impacts of contrasting territorial scenarios in terms of biomass use (for energy or livestock feed) (Borghino et al., 2021), or irrigation strategies (Rogy et al., 2022). While these applications provide an initial overview of the environmental impact of land planning scenarios, they do not take the spatio-temporal dynamics of the studied territories into account. To further develop this approach, Nitschelm et al. (2016) developed a methodology for carrying out spatialised T-LCA for agricultural territories, including both data collection and impact computation. Ding and Achten (2022) proposed an operational method for a spatio-temporal dynamic T-LCA approach, using both

<footnote><sup>a</sup> IFP Energies Nouvelles, 1 et 4 Avenue de Bois Préau, 92852 Rueil-Malmaison, France</footnote>

<footnote><sup>b</sup> ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>c</sup> Elsa, Research Group for Environmental Lifecycle and Sustainability Assessment, Montpellier, France</footnote>

<footnote><sup>d</sup> Société Du Canal de Provence et D'Aménagement de La Région Provençale, CS 70064, Le Tholonet, Cedex 5 Aix-en-Provence, 13182, France</footnote>

<footnote><sup>e</sup> BRL Ingénierie, 1105 Avenue Pierre Mendès France BP 94001, Nîmes, F-30001, France</footnote>

<footnote>* Corresponding author.
E-mail address: nicolas.rogy@ifpen.fr (N. Rogy).</footnote>
agent-based modelling and Geographical Information System (GIS). After applying it to assess the environmental impact of promoting bio-energy crops in an agricultural region in Wallonia (Belgium), they emphasised the importance of considering the biophysical context as well as human interactions over time and space. The environmental performance of agricultural areas can also be affected by environmental changes. Climate change resulting from increasing atmospheric CO₂ concentrations and temperature in addition to alterations in precipitation patterns, can lead to crop yield changes (Mbow et al., 2019). Rogy et al. (2024) combined T-LCA with a crop model in order to assess the spatio-temporal effects of climate change on the eco-efficiencies of agricultural land planning scenarios securing water supply. This latter study reported how irrigation can secure crop yields from water stress induced by climate change in certain parts of the world. This could improve environmental performance in scenarios where irrigation strategies are implemented. This is valid as long as the benefits in terms of yields compensate for the additional impacts generated by the implementation of an irrigation infrastructure, particularly regarding water and energy use. The agricultural territories under study were only covered by two different types of crops: maize and wheat. However a choice of crops that are more climate-change-appropriate could also be part of adaptation strategies (Belmin et al., 2023). Furthermore, the environmental performance was solely assessed through the evolution of mean eco-efficiency, without accounting for variability over time, and considering only one service provided, i.e., food production, without taking the multifunctionality of agricultural territories into account. Finally, Rogy et al. (2024) performed sensitivity analyses on their modelling by modifying one modelling parameter at a time (e.g. the fertilising power of CO₂ on crops or the amount of water supply), whereas it would be interesting to study their variation at the same time, given that the effects of their interactions can have a significant impact on the results. For example, a higher concentration of CO₂ in the atmosphere reduces crop transpiration and therefore the water demand, which could have significant consequences for their environmental performance (Vanuytrecht et al., 2011).

In the context of climate change, there is a lack of in-depth exploration of the environmental performance of agricultural land planning scenarios over time, according to various choices in terms of agricultural practices (e.g. crop selection and use of irrigation), and services provided by agricultural territories, while accounting for the interactions between the different possibilities of agricultural practices. To address this issue, the present paper aims at developing a method combining the approach of Rogy et al. (2024) with the Design of Experiments (DoE) framework. The DoE framework is often used for sensitivity analysis of computer experiments, as it allows for an adequate representation of the results of complex modelling using a minimum amount of parameters, therefore reducing the time required for the simulations (van Schepdael et al., 2016). In comparison with classical scenario analyses which would cover a limited number of studied agricultural planning scenarios, or classical sensitivity analysis which would modify one modelling parameter at a time, the DoE allows for an in-depth study of the environmental performance of every possible configuration of agricultural lands and practices. This in-depth study could be performed here by building a mathematical model of the experimental response (the variable explained, i.e. in this study, the mean or variability of eco-efficiencies evaluated for different provided services) to be derived, as a function of experimental factors (the explanatory variables, i.e. land planning options with various irrigated or rainfed crops) (Gauchi, 2016). In LCA studies, DoE has previously been used to identify the factors that minimize the environmental impacts of a system, in order to find the optimal solution from an environmental point of view. For example, this applies to a grinding process (Silva et al., 2015), to different blends of fossil fuels for water desalination (Aleisa and Heijungs, 2020) or to biodiesel production (Corral-Bobadilla et al., 2022). DoE can also be used for performing a sensitivity analysis with a wide range of parameters in LCA, as in the work of Khang et al. (2017) who

used Latin hypercube DoE for the sensitivity analysis of an LCA of bio-diesel. Note that these case studies were limited to the scale of an industrial process (Patouillard et al. (2020) applied DoE to minimize the number of runs of a computer program when assessing the uncertainty associated with regionalization in a consequential LCA study of alternative transportation scenarios at the French level. However, the temporal dynamics of the environmental performance of the various scenarios were not investigated and the choice of an optimal scenario was not included in the aims of the study. Finally, the DoE framework has never yet been used together with T-LCA to assess the environmental performance of multiple configurations of land planning scenarios.

The method developed in the paper was implemented using a theoretical case study to identify combinations of land planning options that could achieve optimal environmental performance over time. Section 2 describes the innovative approach based on T-LCA, crop modelling and DoE that was developed in this work. It also describes the theoretical case study based on a 700 ha agricultural area in Southern France, which was used as a proof of concept for the methodology. Section 3 reports the main results of the theoretical case study, and details how to analyse the DoE model outputs. Section 4 concludes with a discussion on the main contributions of the proposed approach, its limitations regarding modelling assumptions and its potential applicability to other case studies.

## 2. Material and methods

### 2.1. Overview of the general approach applied to a theoretical case study

Fig. 1 illustrates the main steps of the methodology combining T-LCA, crop modelling and DoE and according to the four main phases of LCA standards (ISO, 2006a; ISO, 2006b).

DoE is used from the goal and scope definition phase onwards to define the exploration domain of the land planning scenarios being studied according to the objectives of the study. These scenarios are based on a set of experimental factors to be tested. Firstly, the experimental factors need to be defined in order to determine the type of DoE to be performed. This depends on the number of factors, their type (discrete or continuous) and their level of dependence (Gauchi, 2016). Once the type of DoE has been defined, a mathematical model is postulated and the DoE is created. This includes the number of experiments required to cover the experimental domain (Gauchi, 2016), i.e. all the land planning scenarios to be simulated.

The crop model is used in the LCI phase in order to account for climate change effects on the performance of land planning scenarios of agricultural territories. This enables the computation of dynamic LCIs that consider the variations over time in yields as well as water and fertilizer in a context of climate change, using the T-LCA method.

Finally, two types of indicators are computed: (i) services provided and (ii) environmental impacts, for a specific agricultural land planning scenario. In turn, these indicators are used for computing the experimental response of the DoE, i.e. the means and variabilities of eco-efficiency ratios, and for evaluating the coefficients of the mathematical model. The statistical validity of the model is also assessed. In the case the model is not valid, a different model should be tried and a different DoE constructed. Finally, the experimental domain can be explored and the coefficients of the mathematical model can be interpreted.

A theoretical agricultural area of 700 ha located in southern France, near the Mediterranean coast is used as a proof of concept. The studied irrigation infrastructure is an Inter-Basin Water Transfer (IBWT) system that imports water from an unstressed area (Rogy et al., 2022), i.e. an area where the quantity of water available per inhabitant or per hectare of arable land is high (Yang and Pang, 2006). The IBWT consists in a 100 km buried pipeline with a pumping station supplying water at a rate of 3 m³ s⁻¹. It is assumed that there are no water leaks in the pipeline and that the IBWT is designed to supply an entire region with water.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Main steps of the integrated approach used to explore the environmental performance of agricultural land planning scenarios in a context of climate change.</figcaption></figure>

Therefore, only part of the impacts of the IBWT is allocated to the ter-
ritory as a function of the amount of water for irrigation required by the
crops. Water can only be supplied to all crops cultivated on the theo-
retical territory through sprinkler irrigation.

## 2.2. Goal and scope definition

2.2.1. Objectives of the study

The objective of the study is to use a theoretical territory to develop and demonstrate the benefits of a method based on the combining of DoE, crop modelling and T-LCA, by carefully exploring the performance of multiple agricultural land planning scenarios in a climate change context. This theoretical territory could be occupied by a variety of crops bearing different characteristics such as nutrient contents, water requirements, yields, energy contents or selling price. To address this variability, four crops are investigated, involving maize and wheat (the two most commonly cultivated crops, globally) (FAO, 2021), dry beans (a protein crop (Brick et al., 2022)) and sunflowers (one of the main oilseeds cultivated globally (Pilorgé, 2020)).

Only the impact on climate change is subsequently used for limiting the number of eco-efficiency ratios at the end. Further investigation is needed to include other impact categories such as water scarcity, eutrophication, and land use change, which are crucial in irrigation-related studies.

The environmental performance of land planning scenarios for the studied theoretical territory is assessed over a 30-year period, i.e. from 2026 to 2055, based on the worst-case climate scenario according to the Intergovernmental Panel on Climate Change (IPCC), i.e. the Shared Socioeconomic Pathways 5–8.5 (SSP5-8.5) scenario. This scenario is an unlikely worst-case scenario and not representative of a business as usual situation (Hausfather and Peters, 2020). Nevertheless, it was still selected for this theoretical case study as it should have the most negative impact on agriculture. This will help to illustrate the benefits of combining T-LCA, crop modelling and DoE. It is not an issue, since the aim of the study is not to make realistic predictions of the future performance. The SSP5-8.5 scenario used was extracted from the NEX-GDDP-CMIP6 dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange and distributed by the NASA Center for Climate Simulation (NCCS) (Thrasher et al.; Thrasher et al., 2022).

2.2.2. System boundaries and services provided

A cradle-to-territorial gate perspective is adopted for the T-LCA,
hence including all upstream activities related to territorial agricultural
production. As proposed by (Nitschelm et al., 2016), this also excludes

environmental impacts of processes located outside of its geographical
boundaries, induced by the consumption or processing of the agricul-
tural production of the studied territory. These system boundaries refer
to the concept of total responsibility for territorial systems as defined by
(Eder and Narodoslawsky, 1999).

The three main functions, described by Tendall and Gaillard (2015) and provided by agricultural systems, are selected for services provided by the theoretical territory: land management and occupation, food production and economic profit.

2.2.3. *Design of Experiment*

According to the goal and scope of the study, the effects of two types
of experimental factors are examined: i) whether or not to use irrigation,
and ii) the choice of crop variety for land occupation.

The adequate type of DoE depends on characteristics of the experi-
mental factors in this study. The experimental factors related to land
occupation are of a continuous nature, i.e. they are related to the pro-
portion of wheat, maize, sunflower and dry beans covering the agri-
cultural area. These experimental factors are constrained by the fact that
their total sum is equal to one. Therefore, they are not independent, and
are thus called mixing factors (Goupy, 2000). The experimental factor
related to irrigation is independent from the mixing factors since it is not
part of the blend composition of the cultivated crops. The experimental
factor linked to irrigation is called a process factor (Gozálvez-Zafrilla
et al., 2013). This process factor is assumed to be discrete, as the crops
are either rainfed or irrigated. Therefore, the chosen DoE method is a
crossed mixture process design (MPD) (Zolgharnein et al., 2017). It is a
combination between one DoE method involving mixing factors with
another involving process factors (Czitrom, 1989).

In this study, eight different experimental responses (i.e. 8 distinct
DoE and mathematical models) are evaluated, as the mean and vari-
ability are assessed for four eco-efficiency ratios, i.e. one ratio for each of
the four services provided by the land planning scenarios of the terri-
tory. In terms of environmental impact, only the impacts on climate
change are evaluated. Each DoE comprises different experiments, i.e.
one experimental response evaluated for specific values of experimental
factors, to be performed in order to explore the experimental domain of
the studied experimental response. To this aim, the specific values ob-
tained for an experimental factor during an experiment (the levels of the
experimental factor) need to be chosen (Gauchi, 2016).

Firstly, for the mathematical models, the experimental responses are
considered to follow a combination between a second order model for
the mixing factors (Czitrom, 1989) and a first order model for the pro-
cess factor (Lawson, 2015). As demonstrated in section 1.1 of the Sup-
plementary Information (S.I.), both of these 2nd order and 1st order
models are combined in the 3rd order model with 20 coefficients, summarized in Eq. (1) and fully described in Eq. S.(4) of the S.I., to express the responses as a function of both mixing and process factors (Lawson, 2015).

$$h = \begin{aligned} & \text{Effects of single crops} + \\ & \text{Effects of the interactions of pairs of crops} + \\ & \text{Effects of the interactions of single crops and irrigation} + \\ & \text{Effects of the interactions of pairs of crops and irrigation} \end{aligned} \quad (1)$$

Where Effects of single crops mathematical formula is described in Eq. S. (6) of the S.I.; Effects of the interactions of pairs of crops in Eq. S.(7) of the S.I.; Effects of the interactions of single crops and irrigation in Eq. S.(8) of the S.I.; and Effects of the interactions of pairs of crops and irrigation in Eq. S.(9) of the S.I.

Secondly for the DoE, the experimental designs involving mixing factors are assumed to be simplex-centroid designs (SCD) with four components and fifteen experiments, i.e. four for one-component mixtures and eleven for two-component, three-component and four-component mixtures equally shared (Goupy, 2000). Therefore, as demonstrated in Table S. 1 of the S.I., the mixing factors comprise five levels (i.e. 1, 1/2, 1/3, 1/4 and 0). The process factor is assumed to have two levels, i.e. presence or absence of irrigation. Therefore, experimental designs involving the process factor are assumed to be two level full factorial designs with two experiments, i.e. 2¹ full factorial designs (Czitrom, 1989). In Eq. (1), mixing factor values ranged between 0 and 1, as they are continuous, whereas the values of the process factor are 1 in presence of irrigation and -1 in its absence, since it is a discrete measurement. As shown in Fig. 2, the resulting experimental domains comprise five dimensions, i.e. one for each factor. For each DoE, it is necessary to carry out the thirty experiments displayed in Table S. 1 of the S.I.

## 2.3. LCI

### 2.3.1. Computing annual crop yields and water needs with AquaCrop

AquaCrop calculates crop yields and water requirements in a specific location for each studied crop according to four computation steps (Vanuytrecht et al., 2014): i) development of the canopy cover, ii) evapotranspiration, iii) the amount of above-ground biomass produced, and iv) the edible yield. The key limiting factor of crop growth in AquaCrop is water availability. In a climate change context, AquaCrop accounts for the benefit of increased concentration of CO₂ in the atmosphere, i.e. CO₂ fertilisation, on crop yields. An important limitation in AquaCrop is that nitrogen or phosphorus needs are not accounted for.

For the DoE of this study, total annual yields and water needs of each experiment are computed by summing the individual yield and water requirement per hectare of each different crop cultivated on the different parts of the agricultural area. AquaCrop inputs, i.e. climate, crop, soil and initial parameters, are computed with the methodology described in depth in Rogy et al. (2024). For irrigated crops, it is assumed that they do not undergo water stress, i.e. water is supplied in sufficient quantity until field capacity (FC), when 50 % of FC is depleted. AquaCrop computations do not need to be repeated several times in the DoE because they are deterministic (Jourdan, 2005).

### 2.3.2. Other data and assumptions for LCI modelling

LCI modelling is based on the methodology of Rogy et al. (2024) which relies on the coupling between the T-LCA framework and a crop model to compute dynamic and spatialised Life Cycle Inventory (LCI) for agricultural territories in the context of climate change. AquaCrop is used to compute annual data on yields and water requirements for the different crops. Other data for computing crop LCIs can be found in existing literature and databases. Since AquaCrop does not model nitrogen, nor phosphorus cycles, specific assumptions are made to compute fertiliser input and field emission data. Fertilisers are assumed to be supplied according to the nutritional composition of the crops in

<figure><img src="image_3.png" /><figcaption>Fig. 2. Construction of the Design of Experiment (DoE) by combining an experimental design for mixing factors, i.e. Simplex-centroid design with 4 constituents (I), with an experimental design for process factor, i.e. a 2¹ full factorial design (II). The resulting design can be described as the experimental design for mixing factors applied to each point of the experimental design for process factor (III) (Goupy, 2000).</figcaption></figure>
order to maximise the annual production of edible biomass under stress-free conditions, i.e. no weeds, fertiliser or water stress. This likely leads to an overestimation of the fertiliser supply and of the emissions of nutrients to the environment. Indeed, nutrients already present in the soil due to air deposition and to fertiliser residues from preceding crop seasons are neglected. Unlike other crops in this study, dry beans possess the ability to fix nitrogen, therefore reducing the required amount of fertiliser (Reinprecht et al., 2020). For this specific type of crop, values for nitrogen, phosphorus and potassium supplies are obtained from an LCA study (Tidåker et al., 2021). The LCI data for fertilisers are sourced from the World Food LCA (Nemecek et al., 2019) and the Ecoinvent 3.9.1 (Wernet et al., 2016) databases. For field emissions, the emission models of Rogy et al. (2024) are also used, based on acknowledged methodologies, i.e. AGricultural Emissions Calculator – LCI (AGEC-LCI) (Viveros Santos et al., 2020), World Food LCA DataBase (WFLDB), Agribalyse (Koch and Salou, 2014) as well as Ecoinvent (Nemecek and Schnetzer, 2012). The models are displayed in Table S. 2 of the S.I. The remaining agricultural activities, e.g. mechanical works or pesticide use, maintained the value of their respective crop LCI in the Agribalyse 3.1 database as indicated in Table S. 3 of the S.I. (Koch and Salou, 2014). Finally, the LCI of the water infrastructure is built according to the LCI for IBWT of Rogy et al. (2022).

## 2.4. Indicator computation

Two main types of indicators are computed for each simulation step (i.e. yearly): i) services provided, and ii) environmental impacts. For land management service, the indicator is the total amount of land occupied (LO), and this remained constant for each simulation (i.e. 700 ha). For food production services, the total mass of edible agricultural products (MAP) produced by each land planning scenario is computed by AquaCrop. Crops yield for both rainfed and irrigated conditions are displayed in Table S4 and Table S5 of the S.I. respectively. For the different crops, the produced food energy data (PE) are sourced from Feedipedia (INRAE) and displayed in Table S6 of the S.I. Finally, for economic profit, the agricultural turnover (AT) of the territory is calculated using the market prices of crops in France for the year 2019, which are displayed in Table S. 7 of the S.I. (Agreste, 2019, 2022; Comte, 2019).

The environmental impacts are assessed with the IMPACT World + methodology (Bulle et al., 2019). The characterisation factors are from the IPCC fifth assessment report (Myhre et al., 2013).

Finally, to account for temporal dynamics, eco-efficiency ratios undergo further processing. Firstly, the mean of the eco-efficiency is investigated by calculating an average over the study period (Dardonville et al., 2022). This mean captures the level of environmental performance of a land planning scenario over the study period. Secondly, variability is assessed using the coefficient of variation of eco-efficiency over the study period (Li et al., 2019). This variability is used for assessing the evolution of the environmental performance on a yearly basis.

## 2.5. Interpretation

### 2.5.1. Evaluation of the mathematical model coefficients and statistical validation

Multilinear regressions are applied for evaluating the mathematical model coefficients of Eq. (1) related to each experimental response. For each model, the thirty DoE experiments are used for calculating the twenty coefficients, thus leaving ten degrees of freedom to assess the statistical validity of the model. The latter is performed through an analysis of variance (ANOVA). Each 20-coefficient model is then simplified through backward elimination, i.e. coefficients are eliminated one by one while verifying that the model would not lose its predictive characteristics (Derksen and Keselman, 1992). The coefficients with the highest probability to be null according to a partial F-test are removed

first, while statistics relative to the overall qualities of models are monitored, i.e. R², adjusted R², Fisher's F, the residual variance and Mallows' Cₚ (Goupy, 2000).

### 2.5.2. Domain exploration of experimental results

To facilitate the interpretation of the results, the experimental responses are normalized. The eco-efficiency means are normalized according to the highest eco-efficiency mean of a given provided service. The eco-efficiency variabilities are normalized with the maximum eco-efficiency variability of all services and water supply combined. Indeed, unlike the eco-efficiency means, the variabilities, which are evaluated using coefficients of variation, are dimensionless and can be compared regardless of the service provided by the agricultural territory. Finally, the experimental results are plotted for each of the three-component mixtures of the experimental domain, i.e. all possible crop combinations across the four faces of the tetrahedrons. This is a conservative approach to presenting the results in four-dimensions.

## 3. Results

### 3.1. Statistical validations of models resulting from the DoE methodology

Table 1 reports the coefficients of each model, as well as the statistics of the mathematical models for the eight experimental responses. For positive model coefficients, single factors or their interactions are considered to evolve in the same way as the DoE responses. The opposite applies for the negative model coefficients. Model coefficients (in bold with an asterisk) for which the results of the partial F-tests are greater than 0.05, i.e. more than 5 % chance for null coefficients, are kept in the models during the backward elimination step. The reason for this is that their removal would worsen the statistical characteristics of the models, i.e. with lower R² and adjusted R², higher p-values and residual variances, as well as a Mallows' Cₚ greater than the number of model coefficients. Nevertheless, the models still boast good predictive characteristics. The R² and adjusted R² of each model are greater than 95 %, highlighting the fact that the models fit the responses of the DOE adequately (Aleisa and Heijungs, 2020). This is further justified by the small p-values and residual variances of the models. In addition, Mallows' Cₚ are lower than the number of coefficients in each of the models, thus implying that simplified models can share similar statistical properties with the original models.

### 3.2. Domain exploration of experimental responses

#### 3.2.1. Environmental performance means

First for MAP and PE, as illustrated in Figs. 3 and 4, the presence of irrigation entails better performance means than without irrigation. The coefficients of interaction between irrigation and individual crops, i.e. γ₁,₁, γ₁,₂, γ₁,₃ and γ₁,₄, are all positive, thus indicating that the presence of irrigation leads to better eco-efficiency means than without irrigation. This is due to the absence of water stress when irrigation is supplied to the crops, resulting in both higher services of MAP and PE than for rainfed conditions. In addition, by combining the significant coefficients for the effects of individual crops (i.e. γ₀,₁), and for the interactions between crops and irrigation, (i.e. γ₁,₁), maize monoculture presents the highest eco-efficiency means, thanks to the high yields produced by maize monoculture under irrigation, as shown in Table S5 of the S.I. Irrigated areas containing dry beans and sunflowers do not have the highest performance mean because their coefficients are lower than the coefficients of the remaining crops. In fact, their yields calculated by AquaCrop are much lower than for the other crops, i.e. more than 2.5 fold lower than the yields of maize. Dry beans have a higher performance than sunflowers for MAP because nitrogen fixation favours a reduction in the use of nitrogen-based fertilisers. For PE, sunflowers perform better than dry beans because the seeds contain about 1.5 fold
<caption>**Table 1**</caption>

Coefficients and overall statistics of the models. The coefficients in bold with an asterisk are coefficients for which the results of the partial F-test are >0.05, i.e. more than 5 % chance that the coefficients are null. EE = eco-efficiency. MAP = mass of edible agricultural products. LO = land occupied. PE = food energy produced. AT = agricultural turnover. x₁ is the share of maize, x₂ is the share of wheat, x₃ is the share of sunflower, x₄ is the share of dry beans and z₁ is the presence or absence of irrigation.

<table><thead><tr><th></th><th></th><th>Mean EE<br>MAP</th><th>Mean EE<br>LO</th><th>Mean EE PE</th><th>Mean EE AT</th><th>Variability EE<br>MAP</th><th>Variability<br>EE LO</th><th>Variability<br>EE PE</th><th>Variability<br>EE AT</th></tr></thead><tbody><tr><td rowspan="10">Coefficients of the model for<br>the factors and their<br>interactions</td><td>γ<sub>0.1</sub> for x<sub>1</sub></td><td>1.93E+00</td><td>2.41E-04</td><td>3.61E+01</td><td>3.24E-01</td><td>7.81E-01</td><td>3.96E-02</td><td>7.80E-01</td><td>7.74E-01</td></tr><tr><td>γ<sub>0.2</sub> for x<sub>2</sub></td><td>2.54E+00</td><td>2.38E-04</td><td>4.62E+01</td><td>4.19E-01</td><td>1.22E-01</td><td>4.05E-02</td><td>1.19E-01</td><td>1.16E-01</td></tr><tr><td>γ<sub>0.3</sub> for x<sub>3</sub></td><td>1.06E+00</td><td>2.55E-04</td><td>3.04E+01</td><td>3.48E-01</td><td>2.81E-01</td><td>4.53E-02</td><td>2.85E-01</td><td>2.84E-01</td></tr><tr><td>γ<sub>0.4</sub> for x<sub>4</sub></td><td>1.85E+00</td><td>6.28E-04</td><td>3.44E+01</td><td>1.65E+00</td><td>1.74E-01</td><td>3.80E-02</td><td>1.79E-01</td><td>1.76E-01</td></tr><tr><td>γ<sub>0.12</sub> for x<sub>1</sub>x<sub>2</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>-9.44E-01</td><td>-4.98E-02</td><td>-9.00E-01</td><td>-8.12E-01</td></tr><tr><td>γ<sub>0.13</sub> for x<sub>1</sub>x<sub>3</sub></td><td>5.96E-02</td><td>—</td><td>—</td><td>—</td><td>-1.93E-01</td><td>-2.22E-02</td><td>-3.84E-01</td><td>-4.61E-01</td></tr><tr><td>γ<sub>0.14</sub> for x<sub>1</sub>x<sub>4</sub></td><td>—</td><td>-3.24E-04</td><td>—</td><td>-1.11E+00</td><td><b>-1.29E-01</b>*</td><td>-1.81E-02</td><td>-1.30E-01</td><td>-8.98E-01</td></tr><tr><td>γ<sub>0.23</sub> for x<sub>2</sub>x<sub>3</sub></td><td>1.15E-01</td><td>—</td><td><b>1.20E+00</b>*</td><td>—</td><td>-2.46E-01</td><td>-2.12E-02</td><td>-1.65E-01</td><td>-1.10E-01</td></tr><tr><td>γ<sub>0.24</sub> for x<sub>2</sub>x<sub>4</sub></td><td>6.10E-01</td><td>-3.32E-04</td><td>1.03E+01</td><td>-1.03E+00</td><td>-1.43E-01</td><td>-1.87E-02</td><td>-1.44E-01</td><td>—</td></tr><tr><td>γ<sub>0.34</sub> for x<sub>3</sub>x<sub>4</sub></td><td>-6.50E-01</td><td>-2.96E-04</td><td>-3.62E+00</td><td>-1.03E+00</td><td>—</td><td>—</td><td>—</td><td>-1.52E-01</td></tr><tr><td>γ<sub>1.1</sub> for x<sub>1</sub>z<sub>1</sub></td><td>1.19E+00</td><td>-2.06E-05</td><td>2.22E+01</td><td>1.97E-01</td><td>-7.37E-01</td><td>3.35E-03</td><td>-7.35E-01</td><td>-7.36E-01</td></tr><tr><td>γ<sub>1.2</sub> for x<sub>2</sub>z<sub>1</sub></td><td>1.18E-01</td><td>-1.39E-05</td><td>2.16E+00</td><td>2.30E-02</td><td>-8.42E-02</td><td>—</td><td>-8.22E-02</td><td>-8.05E-02</td></tr><tr><td>γ<sub>1.3</sub> for x<sub>3</sub>z<sub>1</sub></td><td>2.70E-01</td><td>-2.23E-05</td><td>7.68E+00</td><td>9.39E-02</td><td>-2.50E-01</td><td><b>-1.67E-03</b>*</td><td>-2.54E-01</td><td>-2.48E-01</td></tr><tr><td>γ<sub>1.4</sub> for x<sub>4</sub>z<sub>1</sub></td><td>2.02E-02</td><td>-8.97E-05</td><td>3.81E-01</td><td>3.24E-02</td><td>-9.92E-02</td><td>8.56E-03</td><td>-1.04E-01</td><td>-1.06E-01</td></tr><tr><td rowspan="10">Overall statistics of the<br>models</td><td>γ<sub>1.12</sub> for x<sub>1</sub>x<sub>2</sub>z<sub>1</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>9.12E-01</td><td><b>5.99E-03</b>*</td><td>8.69E-01</td><td>7.85E-01</td></tr><tr><td>γ<sub>1.13</sub> for x<sub>1</sub>x<sub>3</sub>z<sub>1</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>1.73E-01</td><td>7.61E-03</td><td>3.64E-01</td><td>4.44E-01</td></tr><tr><td>γ<sub>1.14</sub> for x<sub>1</sub>x<sub>4</sub>z<sub>1</sub></td><td>9.94E-01</td><td>7.35E-05</td><td>1.86E+01</td><td>2.01E-01</td><td>—</td><td>—</td><td>—</td><td>8.80E-01</td></tr><tr><td>γ<sub>1.23</sub> for x<sub>2</sub>x<sub>3</sub>z<sub>1</sub></td><td>-6.37E-02</td><td>—</td><td>-1.02E+00</td><td>—</td><td>2.42E-01</td><td>—</td><td>1.56E-01</td><td><b>9.69E-02</b>*</td></tr><tr><td>γ<sub>1.24</sub> for x<sub>2</sub>x<sub>4</sub>z<sub>1</sub></td><td>—</td><td>8.79E-05</td><td>—</td><td>—</td><td><b>8.85E-02</b>*</td><td>-9.91E-03</td><td><b>9.05E-02</b>*</td><td>—</td></tr><tr><td>γ<sub>1.34</sub> for x<sub>3</sub>x<sub>4</sub>z<sub>1</sub></td><td>2.12E-01</td><td>6.80E-05</td><td>5.67E+00</td><td>—</td><td>—</td><td>-9.88E-03</td><td>—</td><td>1.33E-01</td></tr><tr><td>Number of<br>coefficients</td><td>15</td><td>14</td><td>14</td><td>12</td><td>17</td><td>16</td><td>17</td><td>18</td></tr><tr><td>R<sup>2</sup></td><td>1.00E+00</td><td>9.99E-01</td><td>1.00E+00</td><td>9.98E-01</td><td>9.99E-01</td><td>9.84E-01</td><td>9.99E-01</td><td>9.99E-01</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>1.00E+00</td><td>9.97E-01</td><td>1.00E+00</td><td>9.97E-01</td><td>9.97E-01</td><td>9.66E-01</td><td>9.98E-01</td><td>9.98E-01</td></tr><tr><td>Fisher's p-value</td><td>1.00E-33</td><td>4.25E-28</td><td>3.15E-37</td><td>4.47E-27</td><td>4.59E-17</td><td>5.19E-21</td><td>2.36E-18</td><td>1.24E-16</td></tr><tr><td>Residual<br>variance</td><td>9.64E-05</td><td>2.73E-11</td><td>2.61E-02</td><td>3.56E-04</td><td>4.01E-04</td><td>1.19E-06</td><td>2.47E-04</td><td>2.29E-04</td></tr><tr><td>Mallows' C<sub>p</sub></td><td>13.6</td><td>9.1</td><td>12.3</td><td>9.2</td><td>16.3</td><td>14.7</td><td>16.5</td><td>16.9</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Means of the eco-efficiencies calculated for the food production (Mass of agricultural products (MAP)) service and the climate change long term (CClt) midpoint indicator. The means were normalized with the maximum eco-efficiency ratio calculated with the MAP service and the CClt indicator. The pure mixtures are on the vertices of the triangles. Binary mixtures are on the sides of the triangles. Tertiary mixtures are in the area inside the triangles. Each composition can be read along the coloured dotted lines at 60-degree angles of the sides of the triangles.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4. Means of eco-efficiencies calculated for the provision of calories (Food energy produced (PE)) service and the climate change long term (CClt) midpoint indicator. The means were normalized with the maximum eco-efficiency ratio calculated with the PE service and the CClt indicator.</figcaption></figure>

more energy per weight unit than the other crops (INRAE). When the agricultural area is not irrigated at all, wheat monoculture provides the highest eco-efficiency means, since $\gamma_{0,2}$ is much higher than $\gamma_{1,2}$, because its yield remains significant in rainfed conditions. The weakest decrease in eco-efficiency ratios is achievable by adding dry beans to wheat cropping, while the strongest decrease is achievable by adding maize. In fact, the lowest eco-efficiency means are found for maize monoculture in rainfed conditions. Indeed, the high values of $\gamma_{1,1}$ lead to a significant decrease in eco-efficiency in the absence of irrigation, i.e. when $z_1$ is equal to -1. Maize yields therefore rely heavily upon irrigation since, unlike wheat, maize grows during the dry (summer) season.

As illustrated in Fig. 5 for LO, the presence of irrigation causes a worst eco-efficiency mean than for an absent water supply because the coefficients of the interactions between the shares of crops and irrigation are negative. This is due to both irrigated and rainfed scenarios

producing the same amount of service (i.e. 700 ha). As such, the difference in the eco-efficiency mean results from the presence of IBWT infrastructure as well as the energy consumed for supplying water to irrigation. In both rainfed and irrigated conditions, the monoculture of dry beans has the best environmental performance mean because dry beans present the highest coefficient for the effects of individual crops. Indeed, dry beans require less irrigation and less intensive agricultural practices (e.g. less nitrogen fertiliser) than other crops.

As illustrated in Fig. 6 for AT, both for presence and absence of irrigation, the performance of dry bean monoculture is better than that of other crops, because of $\gamma_{0,4}$. This is results from the price of dry beans that vary about five-fold higher per unit of mass than for maize and wheat and about three-fold higher than for sunflowers. For this service, as indicated by the small value of $\gamma_{1,4}$, irrigation, when compared to rainfed conditions, does not entail a significant increase in eco-efficiency

<figure><img src="image_6.png" /><figcaption>Fig. 5. Means of the eco-efficiencies calculated for the land management (Land occupied (LO)) service and the climate change long term (CClt) midpoint indicator. The means were normalized with the maximum eco-efficiency ratio calculated with the LO service and the CClt indicator.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Means of eco-efficiencies calculated for the economic profit (Agricultural turnover (AT)) service and the climate change long term (CClt) midpoint indicator. The means were normalized with the maximum eco-efficiency ratio calculated with the AT service and the CClt indicator.</figcaption></figure>

for dry beans.

### 3.2.2. Variabilities of environmental performances

As illustrated in Fig. 7, and Figures S. 1 and S. 2 of the S.I., the majority of coefficients share close values, thus implying the variabilities of MAP, PE and AT eco-efficiencies are similar. Indeed, the provided services of MAP, PE and AT eco-efficiencies are calculated according to crop yields. In addition, irrigation contributes to stabilise the environmental performances of any combination of crops. This occurs because $\gamma_{1,1}$, $\gamma_{1,2}$, $\gamma_{1,3}$ and $\gamma_{1,4}$ are negative, while their absolute values are close to the values of $\gamma_{0,1}$, $\gamma_{0,2}$, $\gamma_{0,3}$ and $\gamma_{0,4}$, thus countering them. This result suggests that irrigation enables the crop yields to be safeguarded in a context of climate change (McCartney and Smakhtin). In the absence of irrigation, combinations of wheat and dry beans present lowest

variabilities due to the low values of $\gamma_{0,2}$ and $\gamma_{0,4}$, when compared to $\gamma_{0,1}$ and $\gamma_{0,3}$. A higher variability in environmental performances is achieved when maize is cultivated, due to the high values of $\gamma_{0,1}$ and $\gamma_{1,1}$, that are related to the sensitivity of maize to water stress. The significance of the absolute values of $\gamma_{0,12}$ and $\gamma_{1,12}$ implies that the variability of the performance of maize monoculture could be reduced by diversifying the agricultural area with other crops such as wheat, given that crop diversity tends to reduce yield variability (Dardonville et al., 2020).

As illustrated in Fig. 8, variabilities for LO are low for all crops in the agricultural area, whether irrigation was present or not. Indeed, the coefficients of the mathematical model are lower than the coefficients of the models for the other services provided by the land planning scenario of the territory. This is due to a constant provided service (i.e. 700 ha) and to the impacts on climate change that are mostly driven by

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Variabilities of the eco-efficiencies calculated for the food production (Mass of agricultural products (MAP)) service and the climate change long term (CClt) midpoint indicator. The variabilities were normalized with the maximum variability of the combined MAP, Land occupied (LO), Food energy produced (PE) and Agricultural turnover (AT) eco-efficiencies calculated relatively to the CClt indicator.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Variabilities of the eco-efficiencies calculated for the land management (LO) service and the climate change long term (CClt) midpoint indicator. The variabilities were normalized with the maximum variability of the combined Mass of agricultural products (MAP), LO, Food energy produced (PE) and Agricultural turnover (AT) eco-efficiencies calculated relatively to the CClt indicator.</figcaption></figure>

emissions related to the use of fertilisers. In fact, as shown in Table S8 of the S.I., fertilisers contribute with low variability to more than 50 % of the impacts for dry beans and 70 % of the impacts for the remaining crops. The latter present a low variability because fertilisers are supplied in order to reach the maximum achievable yields within a given year.

# 4. Discussion

## 4.1. Modelling choices and limitations

The purpose of the study is to prove that an innovative combination between the DoE methodology, T-LCA and crop modelling allows for the dynamics of the eco-efficiencies of agricultural land planning scenarios to be investigated in a context of climate change for various territorial services and adaptation solutions. Given that the various land planning scenarios are multifunctional and the quantities of territorial services they provide differ, it would not have been appropriate to combine a standard LCA approach with the DoE methodology to achieve the goal of this study, due to the recurring issue of managing multifunctionality. This study also highlights the relevance of the integrated approach for the identification of optimal agricultural land planning scenarios, i.e. scenarios that allow for a high environmental performance to be maintained over time. This was confirmed by the study of the environmental performance of land planning scenarios on a theoretical territory.

For the variabilities of environmental performances, some results remain coherent with field measurement. It is widely recognised that, in a context of climate change, irrigation and crop diversity favour the stability of yields (Dardonville et al., 2020). In fact, based on annual yields of 176 crop species in 91 nations between (1961–2010), it has been proven that crop diversity and irrigation exhibit significant stabilising effects, which are opposite in direction and roughly equal in magnitude to the destabilising impacts of observed precipitation and temperature variability (Renard and Tilman, 2019). In a context where the interannual variability of agricultural practices is low, e.g. fertilisation practices, it is advisable to implement irrigation and cultivate a diverse range of crops across an agricultural territory in order to stabilise its environmental performance related to climate change. This is truer under the hydrologic hypothesis of this study. In fact, the water resource is considered to be unconstrained in this study: the IBWT imports water

from an unconstrained area. This assumption is mainly theoretical, since climate change affects precipitation patterns, which could lead to a reduction in surface water flows (Chauveau et al., 2013) and groundwater recharge (McCartney and Smakhtin), and hence to a reduction in the amount of available water for irrigation. This could therefore decrease the environmental performance of the irrigation scenarios, both in terms of means and variability. If water shortages due to climate change were accounted for in this study, optimal performance would therefore require a diversity of crops in addition to supplying irrigation, as such scenarios present a relatively low variability of performance under rainfed conditions. Further research would be needed to assess the impact of additional climate change adaptation strategies, such as crop rotations (Smith et al., 2025), on the environmental performance of agricultural territories. For instance, integrating legumes (e.g., dry beans) into crop rotations could reduce fertiliser requirements and energy use for tillage (Nemecek et al., 2008), thereby enhancing overall environmental performance.

For the mean environmental performance, results should be interpreted with caution in light of the hypotheses and crop model limits of this study, as they may not be agronomically plausible, especially for the optimality of monoculture. For example, maize monoculture significantly affects soil quality as well as the carbon cycle and sequestration, with potentially stronger impacts on climate change (Franco-Luesma et al., 2020).

To enhance the reliability of this study's findings, it is essential to refine the assessment of greenhouse gas emissions attributable to fertiliser applications, as these often represent the primary contributor to climate change impacts. In the present study, nutrient cycles, i.e. nitrogen and phosphorus, were not accounted for in AquaCrop, and simplified emission models were employed, both of which introduce uncertainties into the results. Notably, nutrient supply was backcalculated based on crop content, an approach that lacks realism, given that yields cannot be predicted in advance. Furthermore, fertilisation practices are subject to variability due to meteorological conditions and fluctuations in fertiliser prices, factors that were not fully addressed in this analysis.
## 4.2. Towards couplings between DoE, T-LCA and complex computer models

The limitations of this study could be overcome by using integrated assessment models (IAM) encompassing crop growth and rotations, hydrology as well as carbon and nutrient cycles such as the Modelling of socio-Agro-Ecological system for Landscape Integrated Assessment (MAELIA) (Therond et al., 2014), instead of AquaCrop. The computation times necessary for exploring the environmental performance of a territory modelled by such combinations could be drastically reduced by the DoE methodology, due to a minimisation of the number of experiments (Gauchi, 2016). LCA methodology is increasingly being combined with complex computer models, such as IAM (Sacchi et al., 2022; Malik et al., 2025) and Agent-based models (Marvuglia et al., 2018). The innovative methodology of this study is therefore in line with current developments, by enabling the environmental performance of various scenarios to be explored with the aid of complex computer models. This will allow for optimal planning to be chosen in a context of climate change under real-world conditions and with a reduced timespan for experimentation.

## 4.3. Towards the assessment of other eco-efficiency ratios

The current study highlights the importance of considering a given service during the assessment of the environmental performance of an agricultural territory. Therefore, the improvement and broadening of the computation of eco-efficiency ratios would be beneficial. In order to account for economic profits, the annual turnover of the territory is calculated by assuming constant crop-selling prices since 2019. However these latter prices are notoriously volatile, and may be exacerbated by climate change (Diffenbaugh et al., 2012; Kotz et al., 2025). Further research is thus required for combining the methodology of this paper with other economic data or models, such as general or partial equilibrium models, in order to compute long-term crop prices (Valin et al., 2014). Other economic indicators could also be quantified, such as the gross margin, which would also cover operating costs, particularly for irrigation scenarios, which require higher production costs. More detailed nutrition indicators would also be noteworthy. In addition to the energy content, the nutrient content of the edible biomass produced by the territory could be evaluated. To this end, nutritional scores, such as the Nutrient Rich (NR) score, could be used to summarise the various nutrient contents of different types of food within a single score (Drewnowski et al., 2009), as well as the nutritional indices based on the total production of a territory. Environmental territorial services could also be computed. Further work could also consider carbon sequestration as a territorial service, thus allowing for the benefits of no/reduced tillage and of intercropping on the environmental performance of agricultural territories to be considered (Yang et al., 2023). This would raise the question of the optimality of the maize monoculture scenario. Finally, other environmental impact categories could likewise be used, since this work only considers impacts on climate change as potential environmental impacts. Consequently, trade-offs between other relevant impact categories affected by irrigation (e.g. water scarcity) could be accounted for, thus raising the question of the optimality of the irrigated scenarios.

## 4.4. Towards an assessment of agricultural land planning resilience

This paper provides quantitative information concerning the evolution of the environmental performance of land planning scenarios for agricultural territories in a context of climate change. The mean and variability are two dynamic criteria that contribute to characterise the resilience of a system (Dardonville et al., 2021), i.e. its ability to ensure the provision of its functions in the face of shock and stress (Meuwissen et al., 2019). Other criteria related to the environmental performance of land planning scenario dynamics could have been examined in this work

in order to further investigate resilience: these include trend and resistance (Dardonville et al., 2021). Trend can be characterised by the slope of the linear regression (i.e. a straight line) of the eco-efficiency (Dardonville et al., 2022). Resistance can be represented by the number of times eco-efficiency exceeds a low performance threshold (Li et al., 2019). Eco-efficiency being a ratio, it is necessary to set both a low threshold for the service provided, and a high threshold for the environmental impact. The threshold for the service provided could be evaluated according to the number of years during which crop yields are too low. The environmental impact threshold could be evaluated using science-based targets such as planetary boundaries (Rockström et al., 2009), which could be defined at a territorial scale (Bjørn and Hauschild, 2015).

## 5. Conclusion

This work has resulted in the development of an approach that combines the DoE method with T-LCA and crop modelling. This approach has enabled the exploration of the means and variabilities of the evolution of the environmental performance of different land planning scenarios in the context of climate change. This would not have been possible by combining a standard LCA approach with the DoE methodology, due to the recurring problem of managing multifunctionality. Consequently, this study paves the way for research on the environmental resilience of land planning scenarios by examining the dynamics of their environmental performance. Furthermore, the innovative methodology of this article has promising potential for applications in the combination of DoE, T-LCA and complex computer models (e.g. Integrated Assessment Models), in order to reduce simulation times and to explore the performance of various scenarios.

Notwithstanding the assumptions underlying this study, the stabilising effects of irrigation and crop diversity on yields under temperature and precipitation variability are supported by empirical field data. Consequently, under low interannual variability of fertilisation practices, the implementation of irrigation and the diversification of crops are recommended to enhance the stability of environmental performance related to climate change in agricultural territories.

Further research would still be required for combining T-LCA with Integrated Assessment Models in order to include, in the modelling step, crop rotation, nitrogen-phosphorus- and carbon-cycles as well as the effects of climate change on the amount of available water for irrigation. Moreover, extra services provided by the agricultural territory as well as additional impact categories other than climate change are still needed before the environmental performance of land planning scenarios for the agricultural territory can be investigated more extensively.

### CRediT authorship contribution statement

**Nicolas Rogy:** Writing – original draft, Methodology, Investigation, Conceptualization. **Agata Sferratore:** Writing – review & editing, Methodology, Investigation, Conceptualization. **Nicolas Géhéniau:** Writing – review & editing, Methodology, Investigation, Conceptualization. **Arnaud Hélias:** Writing – review & editing, Supervision, Methodology, Investigation, Conceptualization. **Eléonore Loiseau:** Writing – review & editing, Supervision, Methodology, Investigation, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicolas Rogy reports financial support was provided by Occitanie Region. Nicolas Rogy reports financial support was provided by ELSA-PACT industrial chair. Nicolas Geheniau reports a relationship with BRL Ingénierie that includes: employment and funding grants. Agata Sferratore reports a relationship with Société du Canal de Provence et
d'Aménagement de la Région Provençale que includes: employment and funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## 8 Acknowledgements

Climate scenarios used were from the NEX-GDDP-CMIP6 dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange and distributed by the NASA Center for Climate Simulation (NCCS). This work was financed by the Occitanie Region, and the industrial partners (BRL, SCP, SUEZ, GRDF, Bonduelle and Ecofilae) of the Industrial Chair for Environmental and Social Sustainability Assessment "ELSA-PACT". The authors acknowledge Joséphine Ras for the English proofreading, as well as artists from the thenounproject.com for the following icons: "Money Bag" by David Khai, "Zone" by Nithinan Tatah, "calories consumed" by dDara, "Wheat" by MOCROIS, "Canal" by M. Oki Orlando and "Rain" by Dicky Prayudawanto.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cesys.2026.100397.

## Data availability

Data will be made available on request.

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