# Climate change and irrigation management shape crop resilience in UAE arid agriculture: APSIM model-driven assessment

Achraf Mamassi, Nhamo Nhamo, Sridhar Gummadi, Khalil Ammar,
Mohamed Ahmed Hamdan Al Dhanhani, Hamed Abdullah Al Hashmi,
Hamza Bouras, Francesco Accatino, Rashyd Zaaboul

► To cite this version:

Achraf Mamassi, Nhamo Nhamo, Sridhar Gummadi, Khalil Ammar, Mohamed Ahmed Hamdan Al Dhanhani, et al.. Climate change and irrigation management shape crop resilience in UAE arid agriculture: APSIM model-driven assessment. Agricultural Water Management, 2025, 319, pp.109819. 10.1016/j.agwat.2025.109819. hal-05273505

HAL Id: hal-05273505

https://hal.inrae.fr/hal-05273505v1

Submitted on 23 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Climate change and irrigation management shape crop resilience in UAE arid agriculture: APSIM model-driven assessment

Achraf Mamassi<sup>a,b,*</sup>, Nhamo Nhamo<sup>a</sup>, Sridhar Gummadi<sup>a</sup>, Khalil Ammar<sup>a</sup>, Mohamed Ahmed Hamdan Al Dhanhani<sup>c</sup>, Hamed Abdullah Al Hashmi<sup>d</sup>, Hamza Bouras<sup>e ID</sup>, Francesco Accatino<sup>b ID</sup>, Rashyd Zaaboul<sup>a ID</sup>

## ARTICLE INFO

Handling Editor Xiying Zhang

Dataset link: Supplementary files - 22-sites simulations and figures

### Keywords:

APSIM-model

Climate change scenarios

Irrigation

Water-heat stress

Yield

UAE

## ABSTRACT

Climate change has intensified challenges to food security, compelling the United Arab Emirates (UAE) government to allocate significant resources and advanced technologies for assessing cropping systems performances across country. Crop modeling has emerged as cutting-edge tools for analyzing crop management and assessing water-soil resources usage and sustainability. This study applied a process-based crop model to evaluate performances of three annual crops—wheat, maize, and potato— under integrated climate change projections and irrigation management strategies within the UAE’s arid agroecosystems. APSIM-model was used to simulate crop eco-physiological responses, and assess their vulnerability—resilience profiles under combined climate-water stressors. Model calibration-validation processes were conducted using dataset encompassing crop phenological and productivity state variables. Time-series simulations were then performed under baseline-historical and future-projected period (1988–2100) defined by four Shared-Socioeconomic-Pathways (SSPs: 2.6–4.5–7.0–8.5). Results show that APSIM-model was successfully calibrated, and model validation further confirmed its robust accuracy in simulating crops development and yield prediction under the UAE’s agro-environmental conditions. Rising temperatures and water stress under medium-high emission scenarios (4.5–7.0–8.5) emerged as critical abiotic stressors, reducing wheat-yields up to half and maize-yields up to 75 %, and driving premature wheat and potato crop failure, particularly during the last two decades of the century. Leveraging APSIM-model for irrigation recommendations proved effective in ensuring maize efficient water-use, whereas it helps supporting appropriate potato scheduling across high-emissions scenarios. Findings highlighted the importance of investing in stress-tolerant crops and adapted varieties (e.g., C4 crops), alongside implementing UAE-specific soil-water management and climate-smart practices.

## 1. Introduction

United Arab Emirates (UAE) experiences arid to hyper-arid climatic conditions; characterized by extreme heat, temperature variability, and scarce-erratic precipitation, which pose substantial challenges to the agricultural sector (World Bank, 2021). The country has a historical reliance on imported agricultural commodities to ensure food supply for its population, with a food self-sufficiency ratio below 10 % throughout the 2010–2020 decade (FAO, 2021). This challenge is further

exacerbated by the impacts of climate change and unpredictable environmental events (Dasgupta and Robinson, 2022), which impose considerable constraints on the adaptability of cropping systems and the sustainable management of natural resources. Such pressures underscore the urgent need for climate-resilient agricultural strategies to counteract projected declines in food production (Benitez-Alfonso et al., 2023). Concurrently, the rapid population growth, economic development and shifts in dietary patterns and lifestyles have compelled the UAE’s government, policymakers and agricultural stakeholders to

<footnote><sup>a</sup> International Center for Biosaline Agriculture, ICBA, Dubai, United Arab Emirates</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, AgroParisTech, INRAE, UMR SADAPT, Paris, France</footnote>

<footnote><sup>c</sup> Ministry Of Climate Change and Environment, MOCCAE, Dubai, United Arab Emirates</footnote>

<footnote><sup>d</sup> United Arab Emirates Space Agency, UAESA, Abu Dhabi, United Arab Emirates</footnote>

<footnote><sup>e</sup> National Institute for Agricultural Research (INRA), Beni-Mellal, Morocco</footnote>

<footnote>* Corresponding author at: International Center for Biosaline Agriculture, ICBA, Dubai, United Arab Emirates.
E-mail address: achraf.mamassi@gmail.com (A. Mamassi).</footnote>
allocate significant investments toward strengthening national food security.

The targeted interventions focus on enhancing UAE's domestic agricultural productivity and bolstering the resilience of food systems against emerging environmental and socio-economic challenges (Al-Qodsi et al., 2024). Efforts have focused on cultivating drylands through the adoption of intensive irrigated cropping systems (Ajaj et al., 2018; Saif Al Qaydi, 2016). As a result, agriculture accounts for nearly half of the country's annual freshwater withdrawals, with groundwater comprising more than 90% of the conventional water supply (Ebraheem et al., 2023; Kizhisseri et al., 2021). The depletion of groundwater reserves, in conjunction with escalating water demand, has markedly intensified the scarcity of water resources. This challenge is further exacerbated by soils' physico-chemical properties of the country, which are predominantly sandy and infertile (Saif Al Qaydi, 2016). These soils exhibit high water permeability and evaporation rates, resulting in reduced moisture retention and poor nutrient availability. Together, these constraints critically limit the adoption of efficient agricultural management, particularly for irrigation practices (Ajaj et al., 2018; Ebraheem et al., 2023), and have galvanized national research and development initiatives toward climate-resilient agriculture tailored to both current climate and projected scenarios (Manikas et al., 2022). This commitment is emphasized by the UAE's hosting of "COP28" and the launch of the global "COP28-UAE Declaration on Sustainable Agriculture, Resilient Food Systems, and Climate Action".

Advanced technologies, particularly crop modeling, play a pivotal role as decision support tools for evaluating cropping systems and guiding optimization of management practices across diverse agro-pedos-climatic conditions (Keating and Thorburn, 2018; Mamassi et al., 2023a). Mechanistic models, also known as process-based models, are computerized representations of crop growth, development, and productivity that use biophysical equations to provide comprehensive understanding of the complex interactions between genotype, environment, and management (G×E × M) (Hoogenboom et al., 2004; Peng et al., 2020). These models simulate crop development and growth by integrating management practices with environmental drivers, using biophysical equations to represent resource fluxes; including carbon dioxide assimilation, water balance, solar radiation interception and nutrient uptake, to predict biomass accumulation and final yield (Holzworth et al., 2014). Therefore, models can be effective tools for the assessment of soil-water-crop interactions, identification of biophysical drivers of yield gap, and recommendation of optimal decisions to enhance system resilience under climate change impacts (Webber et al., 2014). In the UAE, much of the existing agricultural research remains rooted in conventional, descriptive, and experimental approaches, with limited integration of crop modelling and relevant biophysical simulations into decision-support frameworks for policy and on-farm management. This gap constrains the capacity to holistically analyze the complex soil-crop-climate continuum, synthesize the vast dataset-inputs underpinning these interactions, and embed future climate projections and environmental scenarios into actionable strategies. This study addresses a partial aspect of this gap, through applying APSIM as both an assessment and optimization platform of crop responses and irrigation practices under the UAE's arid, variable conditions, an application that, to our knowledge, has not been systematically undertaken in UAE.

The core objective is to evaluate eco-physiological responses; namely, how cropping systems adapt to and are affected by the UAE's extreme environmental stressors. This approach enables a comparative analysis of the sensitivity-resilience profiles of three key cropping systems—wheat, maize, and potato—under integrated scenarios of climate change and irrigation management, thereby providing insights into their relative performance under actual and future climate conditions. To address the main objective, the study is structured into three principal modelling exercises: i) assessing the APSIM-model's predictive accuracy in monitoring crop development and yield at sites-specific conditions of

the UAE, ii) running APSIM simulations to analyze the impacts of baseline and future challenging environment and climate change scenarios on the three cropping system development, productivity and plant-water status, and iii) employing the APSIM-model as decision support tool to formulate irrigation practices and assess their impacts under varying climate change scenarios, focusing on analyzing the three crops within-season resilience-sensitivity in terms of phenological development, crop productivity, and plant-water status.

## 2. Materials and methods

### 2.1. Study area and the selected cropping systems

United Arab Emirates (UAE) is a country in the Middle East, located on the eastern edge of the Arabian Peninsula. Its climate is predominantly arid to hyper-arid, characterized by a hot and humid atmosphere for much of the year, with a high annual mean temperature of 29°C, and scarce to irregular precipitation averaging only 140–200 mm per year (World Bank, 2021). This study utilizes a dataset collected from 22-sites distributed across the UAE, representing a broad geographical span of the country (Fig. 1). We applied crop modeling to simulate a range of scenarios for the cultivation of three annual crops—maize, wheat, and potato—under representative soil-climate conditions of local farms. The selection of wheat, maize, and potato was based on their role as staple foods in the UAE, where national self-sufficiency for cereals and tubers remains below 10 %, implying heavy reliance on imports (90 % of consumption). Domestic production of these crops and the land allocated for their cultivation remain very limited, covering approximately 1400 ha for wheat, 420 ha for potato, and 13,900 ha for maize, based on statistics from 2020 to 2025 (https://fcsc.gov.ae/en-us/Pages/Statistics-by-Subject.aspx). These crops are explicitly recognized within national food security priorities, and their inclusion in this study was undertaken at the demand of the UAE Ministry of Agriculture, thereby reflecting both their strategic importance and policy-level prioritization under UAE agricultural conditions.

### 2.2. Modelling framework and roadmap using APSIM-model

Agricultural Production Systems sIMulator (APSIM) is a process-based model that integrates biophysical processes and management modules; such as climate, soil, crops, and crop management practices, within a unified engine to simulate cropping systems at a daily resolution (Ahmed et al., 2016; Keating et al., 2003; Mamassi et al., 2023a). The model was developed in response to a growing need for research tools capable of providing accurate predictions of crop development-production by considering the soil-plant-atmosphere continuum. Detailed accounts of the APSIM development history and model's structure are provided in Gaydon, (2014) and Holzworth et al., (2014). APSIM version 7.10 was employed in present study.

Mechanistic models rely on a structured, three-step approach—parameterization, calibration, and validation—to ensure robust and context-specific model design prior to their application for scenario analysis (Mamassi et al., 2023a). Site- and crop-specific inputs encompassing climate, soil properties, management practices, and crop state variables are essential for tailoring the model to local conditions and for ensuring its reliability and predictive accuracy (Ahmed et al., 2016). Accordingly, "Sections 2.3 and 2.4" of the methodology detail the required datasets and the procedures employed to carry out each phase of the three-step. Afterwards, in "Section 2.5", we described the process of applying the calibrated and validated APSIM-model to simulate the impacts of baseline and future climate change scenarios on the three cropping system development, productivity and plant-water status. Finally, the "Section 2.6" outlined the modelling framework of employing APSIM-model as a decision support tool to formulate irrigation recommendations and evaluate their integrated impact under future climate conditions. Below, the Fig. (2) present a conceptual
<figure><img src="image_3.png" /><figcaption>Fig. 1. Study areas and the 22-sites location.</figcaption></figure>

<figure><img src="image_4.png" /></figure>

Study main objectives (or exercises):

**Section 2.4** – Assessing model performance in predicting crop growth, development, and yield at UAE arid agro-environment

**Section 2.5** – Applying the calibrated model to evaluate baseline and future climate scenarios on cropping system development, productivity, and plant-water status.

**Section 2.6** – Formulating irrigation recommendations and assessing their integrated impacts under projected climate conditions.

<caption>Fig. 2. Conceptual framework and workflow of the study methodology.</caption>
framework illustrating the overall methodology sections and relevant used/extracted dataset, applied processes and conducted exercises.

Given the extensive scope of the study; which encompasses three crops, 22-farms, seven climate models, and four climate change scenarios, and its central aim of analyzing eco-physiological responses to assess the three crops resilience profiles under combined climate change scenarios and irrigation strategies, the "Results and Discussion" section presents a detailed analysis centered on a single representative site, "Farm 2" (coordinates: 55.13°E, 24.07°N) (Fig. 1). This site has been carefully chosen to typify the prevailing conditions across the 22 sites. It is characterized by persistently high temperatures, low and erratic precipitation (Figure S1), predominantly sandy and saline soils with limited fertility (Table S1), and the implementation of advanced, high-intensity crop management practices. Concurrently, simulation results of the 22-sites may offer the granularity needed to inform site-specific adaptation strategies and targeted policy or farmer-level interventions across the diverse agricultural landscapes of the UAE. To support further exploration, the complete APSIM simulation outputs; along with accompanying visualizations, covering all 22 farm sites across multiple climate models and combinations of climate change and irrigation management scenarios, are available in the following repository: (https://nextcloud.inrae.fr/s/JHTiL4iSBbbycXi). Future research may expand on this foundation by systematically discussing how these combined scenarios interact within identified UAE's agro-pedо-environmental zones; for instance, contrasting inland desert regions with coastal agricultural areas.

## 2.3. APSIM-model parametrization process: dataset and data sources

### 2.3.1. Soil parameters

The soil parameters used to parameterize and run APSIM simulations, including both physical and chemical soil properties, were compiled from a combination of International Center for Biosaline Agriculture "ICBA" database, publicly available databases, and relevant literature sources. Table (1) presents the ranges of variation in soil properties across the 22 sites, with detailed soil datasets for each site provided in the Supplementary file named "Soil Dataset".

* Soil chemical parameters:

Soil parameters reflecting the chemical and mineral characteristics were obtained from existing databases curated by International Center for Biosaline Agriculture "ICBA" and the Environment Agency Abu Dhabi (EAD), based on in situ soil analyses. Soil samples were collected from multiple layers and analyzed for key properties, including organic matter content, pH, and available phosphorus, among others (Table 1). Also, the APSIM soil module incorporated electrical conductivity (EC), cation exchange capacity (CEC), and exchangeable sodium percentage (ESP) as key parameters, enabling the simulations to explicitly capture salinity-driven impacts. All

<table><caption>**Table 1**
Main soil proprieties and their value ranges across the 22 sites.</caption><thead><tr><th></th><th></th><th>Units</th><th>Characteristic</th></tr></thead><tbody><tr><td rowspan="4">Soil chemical proprieties</td><td>pH</td><td></td><td>7.9 – 8.5</td></tr><tr><td>Organic matter (OM)</td><td>%</td><td>0.13 – 0.84</td></tr><tr><td>Electric</td><td>(dS.m<sup>-1</sup>)</td><td>2.7 – 55</td></tr><tr><td>Conductivity (EC)</td><td></td><td></td></tr><tr><td rowspan="6">Soil physical proprieties</td><td>Texture (Sand/Silt/ Clay)</td><td>%</td><td>85–90 % / 0–10 % / 0–5 %</td></tr><tr><td>Bulk Density (BD)</td><td>(g.cm<sup>-3</sup>)</td><td>1.4 – 1.6</td></tr><tr><td>Wilting point (LL)</td><td>(cm<sup>-3</sup>. cm<sup>-3</sup>)</td><td>0.03 – 0.08</td></tr><tr><td>Field capacity (DUL)</td><td>(cm<sup>-3</sup>. cm<sup>-3</sup>)</td><td>0.085 – 0.13</td></tr><tr><td>Saturation capacity (SAT)</td><td>%</td><td>22 – 25</td></tr></tbody></table>

analyses were performed following standardized laboratory procedures.

* Soil physical parameters:

Parameters related to the soil physical proprieties were mainly derived from spatialized soil databases and literature. Soil texture, bulk density (BD), soil water thresholds at lower limit of extraction by crops (LL), drained upper limit (DUL), and saturation (SAT) were extracted following a prior process of classification of the 22 site-farm soil types, conducted using the UAE Soil Map available through the Emirates Soil Museum portal (https://www.emirate.ssoilmuseum.org/uae-soil-map). Then, based on this classification, representative physical properties of soils were obtained from generalized soil type tables reported by Shabbir and Abdelfattah, (2008), which define the values of physical characteristics of predominant soil types across the UAE (Table 1). To enhance accuracy and ensure model simulation reliability, the extracted physical soil datasets were cross-referenced and validated against the "Global High-Resolution Soil Profile Database for Crop Modeling Applications" (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1PEEY0) (Han et al., 2019; Liu et al., 2020).

* Additional parameters required by APSIM for configuring the Soil-Water module:

Parameters governing soil evaporation dynamics were derived following the guidelines outlined in the "CSIRO Protocol for the Development of APSoil Parameter Values for Use in APSIM" (Dalgliesh et al., 2016). Soil runoff was parameterized using the USDA Soil Curve Number method, while soil albedo and diffusivity values were estimated in accordance with the CSIRO protocol, based on the textural classification of soils at each farm site and the associated crop types.

### 2.3.2. Climate parameters

A diverse array of meteorological dataset; encompassing multiple parameter types, temporal resolutions, and data sources, was utilized across the various stages of the crop modeling and parametrization process in this study. Below, we summarize the weather datasets employed, categorized by their specific roles and applications within the modeling framework:

* Monthly mean temperature and cumulative rainfall data based on 10-year period (from 2011 to 2021) were extracted for each of the 22 sites locations from ERA5 database "The fifth generation ECMWF atmospheric reanalysis of the global climate". These datasets were employed to compute reference evapotranspiration (ET₀) values specific to each of the 22 sites and for each of the three crops. ET₀, a key determinant of crop water requirements, informs irrigation planning and underpins the parameterization of baseline irrigation practices in APSIM (Section 2.3.3). Detailed methods are provided in "Supplementary Material – Annex 1", with site-specific irrigation calculations for all 22 locations in the supplementary file "Baseline Reasoned Irrigation".

* Daily rainfall, minimum and maximum temperatures, and global radiation data for each of the 22 site locations were obtained from "ERA5". These datasets span a 10-year period, from 2011 to 2021, and were integral to the calibration-validation of APSIM-model for the three crops (Section 2.4). This climatic dataset was used for parameterizing meteorological inputs at model's calibration-validation processes, under the distinct environmental conditions of the 22 sites.

* Daily rainfall, minimum and maximum temperatures, and global radiation were extracted at each of the 22 site locations, for: i) historical baseline period (from 1988 to 2014), and ii) future projected period (from 2015 to 2100) using downscaled data from seven global climatic models: INM-CM5-0 (MC1), GFDL-ESM4 (MC2), MPI-ESM1-2-HR (MC3), MRI-ESM2-0 (MC4), ACCESS-ESM1-5 (MC5), CNRM-ESM2-1 (MC6), and CanESM5 (MC7). This daily data; from
the NEX-GDDP-CMIP6, has been downscaled by NASA Center for Climate Simulation (NCCS) to 25 km resolution using models' outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The selected seven climate models encompass the full spectrum of "Equilibrium Climate Sensitivity" (ECS) that represent the range of temperatures' increase sensitivity to GHG emissions. Also, data of future climatic projections are derived across the four "Shared Socioeconomic Pathways" (SSP1-2.6 "sustainability", SSP2-4.5 "business as usual", SSP3-7.0 "Regional rivalry", and SSP5-8.5 "fossil-fueled development"), and tailored to the 22 sites-specific environmental conditions. This meteorological dataset was subsequently integrated into the APSIM framework to simulate crop state variables under a range of baseline-future projected climate change scenarios (see Section 2.5).

### 2.3.3. Crop management information

Regarding crop management inputs for the APSIM simulations, two approaches were considered to define the management strategies for each of the three crops across the 22 sites-farms: i) conventional management practices commonly employed by UAE farmers, and ii) optimized management strategies, derived through systematic reasoning and calculations to identify the most suitable sowing, irrigation, and fertilization regimes. A detailed summary of the crop management information for both conventional and optimized approaches are provided in "Supplementary Material – Annex 1". The comparison between the optimal recommended crop management practices and those currently employed by farmers in the UAE revealed a high degree of alignment. Accordingly, the simulations conducted in this study were based on crop management strategies derived from reasoned approaches; either through informed calculations or literature sources, specifically tailored to UAE farming conditions. This ensures a more accurate representation of crop performance under the actual prevailing conditions in the UAE. Finally, it is important to note that the APSIM-model currently does not include potassium (K) fertilization module within crop management. As a result, K-fertilization is assumed to be at optimal level when simulating the three crops growth-yield. Additionally, the phosphorus (P) fertilization module is not activated for potato crop, meaning that P-fertilization is automatically assumed to be optimal for potato during simulations. In Table (2), we summarized the main information on the three crop management practices, which has been used for parametrizing APSIM-model during this study.

To account for irrigation water quality, groundwater salinity was incorporated by selecting the 22 study sites within areas classified as highly, moderately, or marginally suitable for irrigated agriculture based on the UAE groundwater salinity suitability mapping (Abdelfattah, 2013). Using these maps, and across these sites, salinity levels ranged between 1 and 4 dS.m⁻¹—thresholds previously reported as tolerable for wheat (Bouras et al., 2023), potato (Levy et al., 1988), and maize (Vennam et al., 2024) growth and productivity—particularly as the cultivars used in this study were registered under ICBA breeding

<caption>**Table 2**</caption>

Reasoned crop management information for APSIM simulations of the three crops (Maize, Wheat and Potato).

<table><thead><tr><th>Crop</th><th></th><th>Maize</th><th>Wheat</th><th>Potato</th></tr></thead><tbody><tr><td rowspan="3">Sowing</td><td>Date</td><td>Oct-Nov</td><td>Nov</td><td>Sep-Oct</td></tr><tr><td>Density (grain/m²)</td><td>10</td><td>200-300</td><td>8-10</td></tr><tr><td>Depth (cm)</td><td>5-6</td><td>2-3</td><td>10-12</td></tr><tr><td rowspan="3">Irrigation</td><td>Type</td><td>Drip irrigation</td><td></td><td></td></tr><tr><td>Dates</td><td>All growth cycle</td><td></td><td></td></tr><tr><td>Doses (mm)</td><td>600-700</td><td>600-700</td><td>1000-1200</td></tr><tr><td rowspan="3">Fertilization</td><td>Application frequency</td><td>Daily</td><td>Daily</td><td>Daily</td></tr><tr><td>Total N-fertilization (kg N.ha⁻¹)</td><td>250-300</td><td>180-220</td><td>250-350</td></tr><tr><td>P-fertilization (P) (kg N. ha⁻¹) at sowing</td><td>150</td><td>80</td><td>-</td></tr></tbody></table>

programs as salinity-tolerant varieties. This approach enabled consistent integration of groundwater quality into model parameterization while compensating for the lack of site-specific water quality datasets.

## 2.4. APSIM-model calibration and validation processes

### 2.4.1. Crop growth, development and productivity state variables

After setting the values of the various required input parameters (i.e., model parametrization), the subsequent step involved calibrating (or adjusting) a set of APSIM-model's crop-cultivar coefficients. These coefficients were specifically related to crop phenology, biomass accumulation, and yield development, and had to be fine-tuned for each of the three crops, taking into account the unique conditions found within the UAE farming context. Accordingly, dataset encompassing information on crops development and production state variables was indispensable for calibrating APSIM-model coefficients. We employed crop yield statistics from 2011 to 2021, specific to the UAE, aligned with the geographic locations of the studied sites (https://fcsc.gov.ae/en-us/Pages/Statistics/Statistics-by-Subject.aspx). These data were complemented by expert-curated crop development observations (i.e., phenological stages) under local conditions of sites, provided by agronomists o ICBA. This integrative approach enhanced accuracy of the target three crops development and growth simulations, in the context of the UAE's distinct agro-environment. The following section details the methodology adopted to calibrate the APSIM-model for simulating crop development and yield performance under UAE agro-pedo-climatic conditions.

### 2.4.2. Crop-cultivar coefficients calibration

Based on real conditions and simulations conducted in 17-sites (i.e., selected randomly from the total 22-sites and represent 77 % of dataset), we have conducted the calibration process that involved three distinct and consecutive steps:

i) **Identification of influential crop-cultivar coefficients requiring calibration:** To determine which coefficients necessitated calibration, we referred to prior global sensitivity analyses of key APSIM outputs—such as phenology, biomass, and yield (e.g., Casadebaig et al. 2014 for wheat; Yang et al. 2025 for maize and Ojeda et al. 2021 for potato)—to distinguish between influential and non-influential cultivar parameters. This step ensured a targeted-efficient calibration process through focusing only on parameters with significant impact on outputs sensitivity and model performance,

ii) **Adopting the Boote's systematic approach (Boote, 1999; Li et al., 2018):** In a pre-adjustment step, all crop coefficients were initialized using default values derived from studies conducted under conditions similar to those in the UAE, serving as a starting point to guide the calibration and narrow the range of coefficient adjustments in APSIM (e.g., Mamassi et al. 2022 for wheat and Chisanga et al. 2020 and Gummadi et al. 2020 for maize). The Boote approach was then applied to define the sequence of coefficient adjustments: first calibrating development and growth coefficients, followed by leaf area coefficients, and finally grain yield coefficients. Within this sequence, each trait-specific coefficient (see details in "Supplementary Material – Annex 2") was adjusted based on field observations and next step approach,

iii) **Using the trial-and-errors strategy to adjust the crop-cultivar coefficients values:** a manual trial-and-error approach was employed to adjust the coefficients for the three crops, with the objective of minimizing discrepancies between simulated and observed crop development stages and yield variables (Section 2.4.3),

Finally, the calibrated coefficients were reviewed for consistency with real characteristics of the three crops under UAE agro-pedo-climatic conditions. Agronomy experts from ICBA validated these
coefficients and made necessary adjustments to avoid overfitting, thereby ensuring the robustness and reliability of the calibration process (see details of APSIM calibrated coefficients for the three crops in "Supplementary Material - Annex 2"). For other crop-related coefficients (such as non-influential cultivar parameters, crop-water, crop-nitrogen, and crop-radiation coefficients), the default values from the standard wheat-XML file were retained and used in this study.

### 2.4.3. APSIM-model evaluation and performance metrics

The validation process aimed to rigorously assess both the accuracy and robustness of the APSIM model across diverse agro-environmental conditions. Model validation was conducted using simulations from five independent sites, representing 23 % of the total 22 sites, which were excluded from the calibration phase to ensure an unbiased assessment of predictive performance. Simulated outputs spanning crop seasons from 2011 to 2021 were systematically compared with observed field data collected under actual UAE farming conditions (Section 2.4.1), encompassing crops development, growth and productivity traits. Two complementary performance metrics were employed: root mean square error (RMSE) and normalized RMSE (NRMSE), quantifying the deviation between simulated and observed data for crop yields and phenological development. Validation was performed at two hierarchical levels: (i) site-specific, evaluating model fidelity at individual locations by analyzing residual deviations between fields simulated and observed values, and (ii) aggregated, assessing the alignment between mean simulated outputs across all sites and national-level observations to capture broader-scale predictive reliability. Additionally, the validation incorporated temporal consistency checks across multiple seasons, ensuring that the model accurately captured inter-annual variability in crop development and productivity. Collectively, this multi-level, multi-season validation provides a rigorous framework for assessing APSIM's predictive capacity, demonstrating its suitability for monitoring and informing agricultural status and management under UAE-specific environmental and agronomic conditions.

RMSE (Eq. 1) and NRMSE (Eq. 2) were calculated to assess the robustness of APSIM-model calibration process and the evaluation of APSIM capacity:

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} [x_{si} - x_{mi}]^2} \quad (1)$$

$$NRMSE = \frac{\sqrt{\frac{1}{n} \sum_{i=1}^{n} [x_{si} - x_{mi}]^2}}{\bar{x}_m} \times 100 \quad (2)$$

where $x_{mi}$ are the measured values, $x_{si}$ are the simulated values, $x_m$ is the mean of the observed values, and $n$ is the number of observations.

### 2.5. Assessing climate change effects on crops development, productivity and water status

After calibrating and validating the model for the 22 sites, simulations were conducted to assess crop development (development stages and cycle length), productivity (yield), and water status (crop water uptake) for maize, wheat, and potato. These simulations incorporated both historical baseline climate (1988–2014) and future projected climate scenarios (2015–2100). Projections were generated using seven climatic models (MCs) and four climate change scenarios (SSPs) (Section 2.3.2). For each site-farm condition, individual APSIM simulations were executed for every climate model (MCs) and climate change scenario (SSPs). Simulation outputs quantified the impacts of climate change on key variables including yield, growth cycle duration, crop water uptake, and water stress factors. For each farm and climate change scenario (SSPs), findings were systematically presented through generated time series simulation datasets and graphs to illustrate the evolution of grain

yield, growth cycle length, water stress factor, and crop water uptake under projected climate scenarios, using the seven climatic models.

### 2.6. Utilizing the APSIM-model as a decision support tool to recommend irrigation practices

Soil water deficit represents the gap between the actual water content in the soil and the amount needed to support optimal crop growth, development and productivity. Monitoring this deficit is essential for efficient irrigation planning, as it enables farmers and irrigation managers to determine both the timing and quantity of water required to restore soil moisture to optimal levels. Many crop models, including APSIM, incorporate internal algorithms that dynamically calculate irrigation needs based on real-time soil water deficit, allowing for more precise and resource-efficient water management (Kheir et al., 2021).

By placing greater emphasis on the intra-seasonal climate variability within the generated scenarios; dynamics that influence crop water status and physiological processes from sowing to harvest, we conducted a meticulous simulation study. In this study, we leveraged the APSIM-model as a decision-support framework to identify and recommend irrigation strategies. Specifically, we employed the model's "irrigate on soil water deficit" function, which enabled the automation of irrigation scheduling based on soil moisture conditions. This approach allowed for the precise maintenance of soil water availability throughout the crop growth cycle, effectively minimizing-deleting water stress and supporting optimal crop performance under varying climate conditions.

We configured the irrigation module within APSIM to calculate daily irrigation doses, ensuring crops were maintained under conditions of "no soil water deficit": The specific APSIM parameter, "Irrigate when soil water deficit reaches this amount (mm)", was set to 0 mm, thereby triggering irrigation whenever any soil moisture deficit occurred. As a result, an extensive analysis was conducted encompassing the three crops, the 22 sites-farms and diverse climate scenarios spanning from 1988 to 2100. Comprehensive files were extracted (see repository files), containing detailed daily and annual information regarding the APSIM-model's recommended irrigation doses, crop phenological development and cycle length, yield, and the evolution of crop water uptake.

## 3. Results

### 3.1. APSIM-model calibration and validation

The close agreement between observed and simulated crop state variables for maize, wheat, and potato demonstrates APSIM's reliability in capturing crop-specific coefficients and growth dynamics. Calibration of APSIM-model provided a highly accurate prediction of the three crops productivity with NRMSEs ranging between 10 and 15 % (Table 3).

<table><caption>**Table 3**

Calibration performance and validation of APSIM yield prediction accuracy across sites and the mean of UAE.</caption><thead><tr><th rowspan="2">Crops (crop output)</th><th colspan="2">Calibration</th><th colspan="2">Validation</th></tr><tr><th>RMSE (t. ha<sup>-1</sup>)</th><th>NRMSE (%)</th><th>RMSE (t. ha<sup>-1</sup>)</th><th>NRMSE (%)</th></tr></thead><tbody><tr><td>Maize (Biomass yield)</td><td>2.95</td><td>15.33</td><td>3.02</td><td>15.72</td></tr><tr><td>Wheat (Grain yield)</td><td>0.49</td><td>9.69</td><td>2.82</td><td>14.70</td></tr><tr><td>Potato (Tuber yield)</td><td>1.62</td><td>11.54</td><td>0.62</td><td>12.25</td></tr><tr><td></td><td></td><td></td><td>0.33</td><td>6.46</td></tr><tr><td></td><td></td><td></td><td>1.75</td><td>12.49</td></tr><tr><td></td><td></td><td></td><td>1.52</td><td>10.89</td></tr></tbody></table>
Similarly, the simulated crop phenological stages and cycle duration showed strong alignment with expert-defined benchmarks, based on agronomic recommendations and field experience. Overall, calibration results indicated the reasonable adjustment of coefficients related to the phenological development, growth and yield traits of the three studied crops. Afterward, the observed crop yield and phenological development from the independent selected 5 sites (i.e., dataset for model validation and not included in calibration process) were compared to corresponding variables simulated using the previous adjusted crop coefficients. APSIM-model has accurately predicted crop yield for each of the three crops with NRMSEs vary between 6.5 and 16 %. Also, the model has logically reproduced the three crops growth cycle lengths and reasonably defined the evolution of phenological stages during the growth season. The comprehensive results of calibration and validation detailing these phenological simulations are provided in the Supplementary file at repository named "APSIM Calibration and Validation Results".

## 3.2. Comparing the climate change scenarios impacts on crops phenological development, productivity and water status

In the following Section (3.2) of this study, we undertake a comprehensive analysis by comparing APSIM simulation outputs for the three crops across the seven climate models and the four climate change scenarios (SSPs of 2.6, 4.5, 7.0, and 8.5). The analysis focuses on three key dimensions of crop response: i) phenological development, assessed through the duration of the growth cycle; ii) productivity, measured via final yield; and iii) plant water status, evaluated through crop water uptake. A summary of the Section (3.2) key findings is provided in Table (S2) of Supplementary Material.

The aim of this investigation is to assess the eco-physiological responses of cropping systems to the UAE's extreme environmental stressors and to compare the vulnerability-adaptability profiles of the three crops under integrated climate change and irrigation management scenarios. For this purpose, the analysis concentrates on Farm 2; selected as a representative site due to its typical agro-pedo-climatic characteristics (more details in Section 2.2). Also, the complete simulation results for all 22 farms—capturing local variability necessary for site-specific adaptation planning and policy development—are available through the following repository link: (https://nextcloud.inrae.fr/s/JHTiL4iSBbbycXi).

### 3.2.1. Impact on crops phenological development and cycle length

Description and interpretation of APSIM simulation results for crop development and cycle duration is grounded on the model's structural design of phenological development process for each crop. Specifically, the three crop phenology modules within APSIM are structured to mechanistically capture the influence of temperature and various stresses on development processes. For the three-crops, phenological development is governed by the accumulation of "Daily Thermal Time" (TT) variable, which serves as a quantitative measure of physiological progress through successive growth stages. TT is typically derived from a graphical function that describes its relationship with another variable known as "Daily Crown Mean Temperature" (Tc), calculated as $(T_c = (T_{max} + T_{min}) / 2 - T_b)$, where $T_{max}$ and $T_{min}$ are the daily maximum and minimum air temperatures, and $T_b$ is the base temperature specific to each crop (0°C for wheat and maize; 2°C for potato). The *TT-Tc relationship* is modulated by several crop-specific and environmental factors, including photoperiod sensitivity, vernalization requirements, and abiotic stressors such as soil water deficit, nitrogen limitation, and phosphorus availability (Zheng et al., 2015). Considering *wheat* as an example, when Tc ranges from 0°C to 26°C, the TT tendency is positive. As Tc increases beyond 26°C and approaches 34°C, TT exhibits a negative trend. If Tc exceeds 34°C (the "senescence temperature threshold" for wheat crop) in a specific day, TT is defined as null value, and the model inhibit the progress of crop development (Figure S2).

When crop is subjected to consecutive days of heat and water stresses—exceeding defined thresholds specific to each crop and soil condition—the APSIM-model simulates potential leaf and biomass senescence, thus, crop failure before maturity. For a more detailed analysis of the impact of temperatures, heat and water stresses on the evolution of three crop development cycles, refer the specific literature related to APSIM modules core structure (Robertson et al., 2002; Zheng et al., 2015; Brown et al., 2011).

Results presented in Fig. (3) for wheat indicate a consistent and gradual decline in growth cycle length under all scenarios until approximately the 2040–2050 timeframe. Beyond this period, trend in wheat growth cycle duration exhibits a distinct shift, largely influenced by the SSPs scenarios. Under SSPs 2.6 and 4.5, a clear stabilization emerges post-2050, driven by the projected moderation or slight decline in temperatures. The stabilization or decline of temperatures (which Tc still in 0–26°C range) reduced the rate of daily TT accumulation, and the phenological development of the wheat crop begins to plateau, suggesting a steady-state adaptation to the moderated climatic conditions. Under the SSPs 7 and 8.5, a marked extension in the wheat growth cycle is observed, primarily driven by rising temperatures. As Tc increasingly exceed the 26°C threshold of wheat — where TT evolution becomes negative, this suppresses the rate of TT accumulation, ultimately prolonging the crop's development cycle under high-emission scenarios. The prolonged cycle includes a greater number of stressed days (i.e., heat-water stresses), which can significantly impact crop productivity and increase water requirements.

Under the SSP 2.6, the maize phenological cycle length, similar to that of wheat, declines until approximately 2050–2060, after which it begins to stabilize (Fig. 3). Under SSP 4.5, a potential stabilization of the cycle length is observed late toward the end of the century (2090–2100). Maize exhibits a positive TT accumulation trend when Tc ranges from 0°C to 34°C — extending its critical threshold comparing to wheat. A gradual decline and stabilization in temperatures below the maize upper threshold of 34°C, reduces the rate of thermal time (TT) accumulation, leading to a transition from a decreasing to a stabilized crop developmental duration under SSP 2.6 and SSP 4.5 scenarios. At the same time, it reduces the number of days the crop is exposed to heat stress, potentially creating relative favorable conditions for maize growth and productivity depending on the water-nitrogen stresses status (i.e., limiting factors in this case). The higher SSPs of 7 and 8.5 exhibit a persistent negative trend of the length of the maize cycle. On other hand, the continued shortening of the maize growth cycle under SSPs 7.0 and 8.5 is driven by rising Tc values that, while increasing, remain below the 34°C threshold—maintaining positive TT accumulation.

Phenological development of potato is notably more temperature-sensitive than of wheat or maize. As illustrated in Fig. (3), even under low-medium emissions scenarios, particularly when high-ECS climate models are considered, the potato growth cycle is projected to exceed a critical agronomic threshold after 2050; surpassing the number of days in a calendar year. This extension is primarily driven by a decline in TT accumulation, as Tc increasingly exceeds the crop's critical threshold of 20°C (i.e., very low in comparison to maize), activating the negative TT function. As a result, the potato crop experiences more frequent heat-stress during its development, exacerbating delays in progression through growth stages. Under more severe climate projections (SSP 7.0 and 8.5), this phenomenon becomes increasingly pronounced across all climate models, especially under MC6 and MC7, where the crop cycle remains extended beyond one calendar year consistently from 2034 until the end of projections (i.e., 2100). This extension would effectively transform potato from an annual to a biennial crop. In the final decade of the century (2090–2100), simulations under the high-emission SSP8.5 scenario projected instances of crop failure. These failures were triggered when, over successive periods, temperatures exceeded the potato's "senescence threshold" of 28°C, beyond which TT accumulation ceases entirely. Potato crop's prolonged exposure to such temperatures, compounded by sustained water stress, halted crop development within
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Evolution of wheat, maize and potato development cycle length under the climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) when using projections of seven climatic models between 1988 and 2100. The black line distinguishes the historical baseline climate period (1988–2014) from the future climate projections (2015–2100).</figcaption></figure>

the APSIM-potato simulations, ultimately leading to simulation termination and classification as crop failure.

### 3.2.2. Impact on crops production (yield)

Fig. (4) presents the projected evolution of wheat yield under various SSPs, derived using multiple climate models. Under SSP 2.6; the most conservative emission scenario, wheat yield remains relatively stable during and beyond historical baseline period of 1988–2014. However, yield variability widens in future projections, particularly under MC5–MC7, where moderate to high ECS drives stronger inter-annual fluctuations in temperature and precipitation. In contrast, other moderate- to higher-emission SSPs (4.5, 7.0, and 8.5) display a consistent downward trend in wheat yield, with the extent of decline escalating alongside the SSP severity and climate model ECS level. Under SSP 4.5, the average yield decreases about 0.5 t.ha⁻¹ initially, deepening to nearly 1 t.ha⁻¹ toward the end of the century. More pronounced declines; ranging from 0.5 to 3 t.ha⁻¹ (up to 50 % of maximum yield of 6 t. ha⁻¹), are observed under SSPs 7.0 and 8.5, especially with models predicting higher ECS level.

Across all scenarios (Fig. 4), maize yield initially declines by approximately 2 to 3 t.ha⁻¹ (up to 10–15 % of maximum yield 25 t. ha⁻¹) during the baseline period (1988–2014), followed by a more pronounced reduction of 4 to 5 t.ha⁻¹ (20 to 30 %) through 2040–2050. Beyond this point, the trajectory of maize yield diverges depending on

the emissions pathway. Under SSP 2.6, yields tend to stabilize post-2050, fluctuating between 19 and 24 t.ha⁻¹ depending on the climate model. A modest upward trend is even observed in the final decades of the century, reaching 25 t.ha⁻¹. This potential recovery is likely due to maize's higher tolerance, combined with the modeled reversal in temperature increase associated with SSP 2.6 and, which collectively favor phenological stability (Fig. 3) and yield recovery (Fig. 4). In contrast, while SSP of 4.5 also shows initial post-2050 stabilization, but a renewed decline in yield emerges toward the end of century. Likewise, under the high-emissions SSPs 7.0 and 8.5, maize yields exhibit both significant declines and heightened inter-annual variability, with yields ranging from 4 to 18 t.ha⁻¹ in the later decades.

Under the low-emission pathway SSP 2.6, potato yields remain relatively stable throughout the century, ranging between 12 and 15 t. ha⁻¹ across models (Fig. 4). This reflects the moderate warming and fewer extreme events associated with SSP 2.6, which help preserve favorable conditions even for potato sensitive development. By contrast, higher-emission scenarios (SSPs 4.5, 7.0, and 8.5) exhibit a pronounced decline in yield, with severity escalating with increasing SSP levels and climate models' ECSs, resulting in yield reductions of up to one- to twofold; attaining 6–7 t.ha⁻¹. Yield losses arise as rising temperatures exceed the 20°C potato-critical threshold for optimal development, inducing prolonged crop cycle across calendar year or more. Under SSP 8.5, crop failures are repeatedly simulated in the final decades,
<figure><img src="image_6.png" /><figcaption>Fig. 4. Evolution of wheat, maize and potato yield under the climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) when using projections of seven climatic models between 1988 and 2100. The black line distinguishes the historical baseline climate period (1988–2014) from the future climate projections (2015–2100).</figcaption></figure>

particularly with high-ECS models like MC5, MC6, and MC7.

### 3.2.3. Impact on crops water uptake

For the wheat crop, the projected patterns of water uptake under various SSP scenarios broadly mirror the trends observed in wheat yield, albeit with a slightly less pronounced decline in water uptake under the more extreme warming scenarios (SSP 7.0 and 8.5), particularly when compared to the higher yield decline tendency. As illustrated in Fig. (5), historical baseline water uptake for wheat averaged approximately 250 mm. Across future scenarios, this value generally remains within the 200–250 mm range, even under high-emission pathways. Notably, under SSP 7.0 and 8.5, the reduction in water uptake does not exceed 30–40 % (equivalent to 70–80 mm), highlighting the crop's relative resilience in terms of water acquisition. However, yield reductions under these scenarios ranged from 0.5 to 1.0 times the baseline average of approximately 5.5 t.ha⁻¹.

For maize, projected water uptake under SSP 2.6 and 4.5 closely follows trends in biomass yield, reflecting a tight physiological link between crop growth and water use (Fig. 5). Under SSP 2.6, stable or slightly increased biomass corresponds with consistent water uptake, indicating favorable conditions and efficient water use. In contrast, under SSP 4.5, the observed decline in biomass is not due to water limitations but rather to shortened growth cycles, which reduce the time available for biomass accumulation. As a C4 crop, maize remains physiologically resilient to moderate heat-stress (SSP 4.5) (Ludwig, 2013), whilst at this scenario, reduced cycle duration limits growth

potential, underscoring the importance of developmental timing over water availability in determining yield outcomes under these scenarios. Fig. (5) shows that during the historical baseline, maize required about 350 mm of water to produce 26–27 t.ha⁻¹ of biomass. Under SSP 7.0 and 8.5, this relationship deteriorates: biomass yield declines by up to 4-fold, while water uptake drops by 2-fold.

Fig. (5) shows that potato water uptake remains relatively stable—between 300 and 400 mm—across both the baseline and future projections, with climate change impacts generally below 25 %, largely reflecting inter-annual variability. Under SSP 2.6, water uptake and yield remain proportionally aligned, indicating resilience. However, under SSP 4.5, 7.0, and 8.5, this relationship weakens: yield declines outpace reductions in water uptake. Notably, crop failures emerge under SSP 8.5 in the century's final decades.

### 3.3. Assessing the use of APSIM-model as a decision support tool to recommend optimal irrigation doses

In this section, we evaluate the use of APSIM-model to inform irrigation practices aimed at mitigating water-stress under projected climate change scenarios. Specifically, we assess the model's utility in recommending irrigation practices "Apsim.irr"; particularly water doses and dates, and their effects on the variability of the three crop state variables: development cycle length, crop yield, and water uptake. These simulations are compared against outcomes from the baseline reasoned irrigation "Bsl.irr" used to parameterize APSIM's irrigation
<figure><img src="image_7.png" /><figcaption>Fig. 5. Evolution of wheat, maize and potato cumulative water uptake (mm) under the climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) when using projections of seven climatic models between 1988 and 2100.</figcaption></figure>

module in previous sections. Also, this approach allows for quantifying
the sensitivity of crop performance to irrigation adjustments under
differing climate projections. A summary of the Section (3.3) key find-
ings is provided in Table (S2) of Supplementary Material.

3.3.1. Impact on crops phenological development

Comparison of crops' cycle lengths under APSIM-recommended
irrigation versus baseline irrigation reveals minimal differences for
wheat, maize, and potato across all SSP scenarios and climate models
(Figure 6) (Figure S3). For both wheat and maize, cycle length remained
stable regardless of the irrigation strategy applied, with slight extended
ranges of variation of maize cycle length under APSIM-based irrigation
(Figure 6). For potato, compared to the baseline irrigation where cycle
duration began to exceed a single calendar year as early as 2050 for SSPs
2.6-4.5 and 2030-2040 for SSPs 7.0-8.5 (Figure 3), APSIM-guided irri-
gation successfully delayed this phenomenon until approximately
2060-2070 for SSPs 2.6-4.5 and 2050 for SSPs 7.0-8.5, and only under
the more extreme climate models, notably MC5 and MC7 (Figure S3).
Another key distinction emerged for potato under SSP 8.5: baseline
irrigation simulations showed repeated crop failures for MC5, MC6, and
MC7 in the late century (Figure S3), while APSIM-based irrigation pre-
vented such failures entirely (Figure 6).

**3.3.2. Impact on crops production (yield)**

The comparative analysis in Fig. (7) reveals a consistent decline in

wheat yield under all SSP scenarios when adopting the APSIM-model’s
irrigation recommendations versus baseline reasoned irrigation prac-
tices. Specifically, maximum yield during the historical baseline period
(1988–2014) decreases by 0.2 to 0.5 t·ha⁻¹, while minimum yields in
the last two decades of the century fall by 0.5 to 1 t·ha⁻¹ (Figure S4 vs.
Fig. 4). This nuanced reduction reflects the complex interplay of factors
influencing wheat growth, including water availability, heat stress, ni-
trogen dynamics, and intrinsic physiological responses. The comparison
of maize yield variation over the SSPs and climate models presented in
Fig. (7) and Figure (S4) highlights several noteworthy differences that
affirm the superiority of adopting the irrigation strategy based on
APSIM. Firstly, under APSIM-based irrigation, yields surpassed 27 t
ha⁻¹, exceeding the 25 t ha⁻¹ maximum achieved with the baseline
irrigation strategy (Fig. 7). Secondly, a significant disparity is observed
regarding the minimum limit of yield decrease over the years under SSP
4.5, 7.0, and 8.5. For instance, under SSP 4.5, the minimum maize yield
was constrained to 17 t ha⁻¹ when employing the APSIM irrigation
strategy, whereas yields declined to 12 t ha⁻¹ under the baseline strategy
(Fig. 7) (Figure S4 vs. Fig. 4). For potato, yield remained largely stable
across all SSP scenarios under both irrigation strategies (Fig. 7). How-
ever, a modest decline observed under the APSIM-based approach—
paralleling trends seen in wheat—suggests that the APSIM’s irrigation
recommendations may not fully capture temperature-related stress
factors that intensify under climate change. Nevertheless, the APSIM
strategy demonstrated a significant advantage in preventing simulated
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Comparison of cycle length variations of three crops (wheat, maize and potato) between 1988 and 2100, under changing in three factors: irrigation strategy (i.e., APSIM-based irrigation "Apism.irr" and Baseline reasoned irrigation "Bsl.irr"); climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) and the seven climatic models (MC1 to MC7).</figcaption></figure>

crop failure under extreme conditions, particularly under SSP 8.5, where
repeated failures were observed with the baseline irrigation.

3.3.3. *Impacts on crops water uptake*

APSIM-based irrigation strategies demonstrated broadly similar
outcomes for wheat and potato. Cumulative wheat-crop water uptake
remained comparable or slightly lower than baseline levels
(200–270 mm) under SSP2.6 and SSP4.5 but declined markedly under
SSP7.0 and SSP8.5, particularly in the latter half of the century (Fig. (8);
Figure (S5)), closely mirroring reductions in yield (Fig. 7). This occurred
despite an overall increasing trend in cumulative irrigation doses
(Supplementary files – “Farm 2” simulations), suggesting that although
APSIM dynamically adjusts water inputs in response to climate-induced
extensions in crop cycles, elevated heat stress and impaired transpira-
tion progressively constrain photosynthesis and biomass accumulation,

thereby reducing water productivity. Similarly, for potato, APSIM
consistently recommended higher irrigation volumes (500–700 mm)
across all SSPs to accommodate prolonged growing periods (Fig. 6), yet
water uptake remained modest (300–350 mm) slightly lower than
baseline irrigation levels, and yields declined, particularly under high-
emission scenarios. During the historical baseline period (1988–2014)
(Figure S5), maize water uptake showed only a slight increase—around
10 mm—under APSIM-based irrigation compared to baseline reasoned
irrigation (Figure S5). However, as SSP scenario values intensify over
time, this difference becomes increasingly pronounced (Fig. 8). Under
SSP 7.0 and 8.5, particularly with the MC7 climate model, the diver-
gence in maize water uptake reaches up to 80–100 mm, paralleling a
substantial yield increase of 6–8 t·ha⁻¹ with the APSIM-based irrigation.
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Comparison of yield variations of three crops (wheat, maize and potato) between 1988 and 2100, under changing in three factors: irrigation strategy (i.e., APSIM-based irrigation "Apism.irr" and Baseline reasoned irrigation "Bsl.irr"); climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) and the seven climatic models (MC1 to MC7).</figcaption></figure>

# 4. Discussion

## 4.1. APSIM's performance in arid cropping systems: evidence from the UAE's conditions

APSIM model demonstrated strong accuracy in reproducing both crop growth-yields and phenological development across independent farms, as well as the UAE's mean values of crop state variables, indicating that the calibration captured key eco-physiological processes under UAE's arid conditions. APSIM's strong performance in simulating cereals (wheat and maize) under arid systems is consistent with earlier findings (Kheir et al., 2021; Mamassi et al., 2022), while this study provides, to our knowledge, the first evaluation of APSIM-potato under such harsh conditions, extending its application beyond the more favorable environments where it has previously been tested (Borus et al., 2018; Tang et al., 2022). Additionally, prior studies evaluating APSIM

under reasoned irrigation and fertilization regimes (i.e., close to the optimal); comparable to the employed practices in the UAE, have reported similarly high accuracy levels (Ahmed et al., 2016; Kumar et al., 2023). This is further substantiated by the fact that the model was originally developed within the Australian context (Gaydon, 2014; Holzworth et al., 2014), known for its challenging arid climate similar to that of the UAE. These results valorize the suitability of the APSIM-model for crop simulation under UAE conditions. By leveraging APSIM, researchers and policymakers can gain valuable insights into crop responses to evolving climatic conditions in the UAE, enabling the development of targeted strategies to mitigate risks and capitalize on emerging opportunities.
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Comparison of water uptake variations of three crops (wheat, maize and potato) between 1988 and 2100, under changing in three factors: irrigation strategy (i.e., APSIM-based irrigation "Apism.irr" and Baseline reasoned irrigation "Bsl.irr"); climate change scenarios (i.e., SSPs of 2.6, 4.5, 7.0 and 8.5) and the seven climatic models (MC1 to MC7).</figcaption></figure>

## 4.2. Impacts of climate change on crop phenology, productivity, and water use

**Wheat phenology** in the UAE was sensitive to future climate scenarios, with low to moderate-emission pathways (SSPs 2.6 and 4.5) leading to a stabilization of the growth cycle, whereas high-emission scenarios (SSPs 7 and 8.5) are projected to prolong the cycle, increasing exposure to heat and water stress and potentially reducing productivity. In an already arid to hyper-arid conditions (like of UAE), disruptions associated with high-emission scenarios are likely to intensify stress on wheat crops, particularly during critical vegetative (Nawaz et al., 2014) and reproductive (Hochman, 1982) growth stages. This emphasizes heightened sensitivity of soft wheat phenology to high-emission climate scenarios and highlights the urgent need for adaptive agricultural shifts in sowing-harvesting schedules and agro-nomic breeding traits (Rezaei et al., 2018). **Maize phenology** in the UAE shows a declining and eventually stabilizing growth cycle under

moderate-emission scenarios (SSPs 2.6 and 4.5), benefiting from temperatures remaining below its upper threshold of 34°C, which reduces heat stress exposure and may favor productivity depending on water and nutrient availability. In contrast, under high-emission scenarios, the maize growth cycle continues to shorten due to rising temperatures, indicating a different sensitivity pattern compared with wheat. This trend ultimately accelerates crop development, shortening growth cycle and reducing number of days with relative favorable conditions for maize growth. **Potato phenology** proved most temperature-sensitive, with growth cycles projected to extend beyond a calendar year under moderate- to high-emission scenarios as temperatures surpass its 20 °C threshold. This induces frequent heat stress, delayed development, and a potential shift toward biennial growth, disrupting traditional crop rotations and land-use planning (Kirina et al., 2025; Mondo et al., 2024). Under extreme warming, when senescence thresholds are exceeded, complete crop failure becomes likely. Critically, simulation outcomes suggest that under current potato varieties and typical open-field
systems, cultivation in the UAE may become increasingly unfeasible by the second half of the century. Without targeted adaptation, the crop's development cycle may no longer align with on-farm conditions, emphasizing the urgent need for faster-maturing, heat-tolerant varieties (Kim and Webber, 2024). Adaptation could involve adjusting planting dates to exploit optimal seasonal windows (Kirina et al., 2025) and employing innovative soil and water management to improve root-zone microclimates (Tang et al., 2024).

**Wheat yields** remain relatively stable under SSP 2.6, though high-ECS models (MC5-MC7) introduce greater inter-annual variability. In contrast, moderate- to high-emission scenarios (SSPs 4.5, 7.0, 8.5) show progressively stronger declines, with losses most pronounced under SSP 8.5 by century's end. The yields erosion is primarily attributed to the compounded effects of rising temperatures, prolonged exposure to heat- and water-stress under the extended crop cycle durations, which disrupt phenological development and reduce productivity. Collectively, these findings show that while wheat remains well adapted to current UAE conditions and projected low-emission climates, achieving favorable yields of about 6 t.ha⁻¹, its vulnerability rises sharply under high-emission scenarios. Comparable patterns of yields stability under mild scenarios and decline under harsher ones have been reported in other semi-arid to arid regions, including Iran (Pakrooh and Kamal, 2023). This underscores the need for targeted breeding to develop resilient soft wheat cultivars with phenological and yield components' traits adapted to climate change challenges (Xiong et al., 2024), while also evaluating the potential of alternative cereal species—such as durum wheat or quinoa—that are better suited to dryland environments (Heinz et al., 2024; Wei et al., 2025), provided they are as well compatible with the dietary preferences of UAE population (Mamassi et al., 2025). **Maize yields** decline across all scenarios early in the century, reflecting sensitivity to rising heat and water stress. Under SSP 2.6, yields later stabilize as moderated warming aligns with maize's higher thermal tolerance. In contrast, moderate- to high-emission scenarios drive renewed declines and heightened variability, underscoring maize's vulnerability under sustained warming. Although maize's physiology allows greater tolerance to heat (due to its C4 photosynthesis pathway (Ludwig, 2013)), the increasingly shortened growth cycle under moderate-high warming limits the crop's capacity to fully utilize available water and nutrients. Consequently, even in the absence of acute heat stress—as noted under certain SSP 4.5 scenarios—the compression of the growth window under medium-high SSPs restricts biomass accumulation and yield potential. This illustrates the nuanced interplay between maize physiological-phenology and climate change in shaping yield outcomes under future scenarios (Asseng et al., 2009). **Potato yields** remain relatively stable throughout the century under the low-emission pathway, reflecting the moderate warming and limited extreme events that allow favorable conditions for this temperature-sensitive crop. In contrast, under high-emission scenarios, potato yields decline sharply as temperatures surpass critical developmental thresholds, extending growth cycles and intensifying abiotic stresses; including heatwaves, evapotranspiration, and nutrient depletion (Dewedar et al., 2021; Kim and Webber, 2024). Repeated crop failures are projected in severe late-century projections, underscoring this crop vulnerability and the urgent need for climate-adaptive management strategies

**Wheat water uptake** under future SSP scenarios largely follows the trends observed for yield, with modest declines even under high-emission pathways. Despite substantial yield reductions under the more severe scenarios, water uptake remains relatively resilient, reflecting wheat's capacity to maintain water acquisition under increased thermal and hydric stress. These trends underscore a decline in WUE as emissions forcing increases. One plausible explanation lies in the intensification of heat-stress under higher SSPs, which can elevate transpiration rates without proportionally enhancing photosynthetic efficiency, leading to greater water loss per unit of biomass produced (Abdelhakim et al., 2021). Additionally, high temperatures may disrupt wheat crop at sensitive phenological stages—such as flowering and

grain filling (Hyles et al., 2020)—thereby impact yield components despite adequate or near-adequate water availability. Overall, these results highlight a buffered yet finite physiological response of wheat to warming, particularly under moderate SSP scenarios. While water uptake remains relatively stable, declining efficiency under extreme conditions highlights the urgency of adaptive soil-water management to optimize use and sustain yields in harsh climates (Shoukat et al., 2024). For example, conservation agriculture has been shown to enhance soil health and maintain productivity under long-term warming (Teng et al., 2024). Projected **maize water uptake** under moderate emission scenarios closely parallels biomass trends, reflecting the strong coupling between growth and water use (Ludwig, 2013). Under low- to mid-emission scenarios, stable water uptake matches yield trends, but high-emission pathways show biomass declines outpacing water use, reflecting heat stress and shortened growth cycles, which reduce WUE and productivity despite maize's C4 heat tolerance. Elevated temperatures of high-emissions scenarios accelerate transpiration, reduce photosynthetic efficiency, and, coupled with shortened growth durations, severely impair biomass accumulation and water-use effectiveness (Jia et al., 2022). **Potato water uptake** remains relatively stable across historical and projected periods, with variations largely reflecting inter-annual climate fluctuations rather than systemic constraints. Under low-emission scenarios, water uptake and yield remain closely aligned, indicating physiological resilience and effective adaptation to moderate warming. In higher-emission scenarios, however, yield declines outpace changes in water uptake. This decoupling results from prolonged crop cycles, increased heat stress, and elevated transpiration, which reduce photosynthetic efficiency and amplify vulnerability to nutrient and water limitations.

### 4.3. Implication of APSIM-based irrigation on crop phenology, productivity and water-use responses

Relative stability of **wheat and maize phenology (or cycle length)** across both irrigation strategies underscores their resilience to short-term fluctuations in soil moisture, reflecting a lower sensitivity of phenological development to water management in these cereals. By contrast, **potato phenology**, a temperature-sensitive crop, demonstrated a marked dependence on optimized irrigation. APSIM-guided precision irrigation proved critical in buffering phenological disruptions and preventing potato crop failure. As such, under medium-high climate emissions' scenarios; stimulating delayed phenological development and extending crop cycle length, an optimized irrigation may act directly through partially adjusting a crop development schedule, as in potato, and emerging as a crucial short- to mid-term adaptation strategy for sustaining open-field potato cultivation within the UAE's harsh environmental context. This highlights the role of dynamic irrigation in fine-tuning water supply to potato crop developmental stages, mitigating thermal-hydric stress accumulation, and maintaining more favorable plant root-zone microclimates (Medina et al., 2025). Moreover, precision irrigation may improve the cereals growing conditions through sustaining favorable microclimate and minimizing heat-water-stress during the extended cycle length, as in maize. Precision irrigation has been shown to play a decisive role in buffering crops against heat-water stress interactions, with particularly strong benefits for temperature-sensitive species (Mulders et al., 2024; Tang et al., 2024).

The observed decline in **wheat yields** under APSIM-recommended irrigation compared to baseline practices highlights the trade-offs between precision water management and the complex physiological and environmental constraints governing cereal productivity in arid systems. Notably, simulation outputs indicate that irrigation volumes applied during critical temperature wheat-sensitive phases (from February to April) under baseline reasoned strategy generally exceed those prescribed by APSIM. This suggests that the additional irrigation amount may temporarily alleviate heat-nutrient induced stresses by
supporting transpiration-driven cooling and nutrient uptake (Yadav and Bhatt, 2024), processes which are less explicitly captured in the APSIM irrigation algorithm focused primarily on plant water demand for photosynthesis. That is to say, given that wheat (C3 crop) exhibits lower WUE-transpiration efficiency, the additional water input under baseline irrigation strategies may partially offset these vulnerabilities by preserving soil moisture and supporting physiological resilience. From both agronomic and economic perspectives, supplemental irrigation beyond APSIM recommendations requires careful evaluation. Water-use efficiency, crop-specific responsiveness, and the costs of additional water, energy, and infrastructure must be weighed against potential yield gains (Mamassi et al., 2025). Regional water availability and sustainability, particularly under arid and climate-stressed conditions, should guide irrigation intensity (Borsato et al., 2020). Ultimately, a holistic approach integrating productivity, environmental stewardship, and equitable water use is essential to ensure irrigation strategies are both effective and sustainable (Fiser et al., 2025). For maize yield, findings confirm that adopting APSIM as a decision-support tool for optimizing irrigation can reduce yield variability across climate change scenarios, thereby mitigating adverse impacts on maize productivity. Although this contribution does not reverse the overall declining trend in maize production under high-emission scenarios such as SSP7.0 and SSP8.5, targeted irrigation strategies can still improve yields by enhancing water availability for this C4 crop, which exhibits inherent tolerance to heat-water stresses (Dahri et al., 2024). For potato yields, the relative stability under both irrigation strategies suggests that APSIM's precision irrigation may underrepresent key temperature-stress interactions intensified by climate change, including transpiration-driven canopy cooling and water-facilitated nutrient uptake; critical processes for heat-sensitive crops like potato (Adekanmbi et al., 2023). Although APSIM schedules irrigation to meet biomass-driven water demand, it may underestimate yield losses when high temperatures reduce photosynthetic efficiency or accelerate developmental rates (Dewedar et al., 2021 Fig. 6). Nevertheless, the APSIM strategy demonstrated a significant advantage in preventing simulated potato crop failure under extreme conditions, particularly under SSP8.5, where repeated failures were observed with the baseline irrigation. These yield-related findings combined with cycle length-related results highlight a key trade-off: while baseline reasoned irrigation may yield higher productivity under moderate stress with an overestimation of amount of applied water, APSIM-based irrigation support more appropriate agricultural scheduling under high-emissions scenarios, and enhance system resilience through precise doses under high stressed periods (avoiding crop failure), emphasizing the need for irrigation strategies that balance yield optimization with climate-adaptive risk management.

APSIM-based irrigation strategies simulated broadly comparable **wheat and potato water uptake** outcomes. Cumulative water uptake remained near baseline under moderate emission scenarios (SSP2.6 and SSP4.5) but declined substantially under high-emission pathways (SSP7.0 and SSP8.5), paralleling reductions in both crop yields. This indicates that while APSIM dynamically adjusts water supply to extended crop cycles, elevated heat stress and impaired transpiration progressively constrain photosynthesis and biomass accumulation, reducing water productivity and reflecting both crops sensitivity to extreme temperatures as well as the limits of irrigation in offsetting heat-stress impacts. The divergence; greater irrigation inputs alongside diminishing water uptake and productivity in both crops, reveals the limitation of current wheat and potato physiological-phenological traits under UAE conditions and high-emission scenarios, while highlighting their acute sensitivity to soil-water management and the mounting challenge of sustaining productivity under intensifying environmental pressures. Moreover, it highlights the diminishing efficacy of APSIM-recommended irrigation under intensifying climate stress, where heat-transpiration imbalance and potential nutrient limitations—factors not fully captured by the model's irrigation module; constrain the crops' ability to convert water into yield (Zhang et al.,

2022). Under APSIM-based irrigation, **maize water uptake** during the historical baseline period was only slightly higher than with conventional irrigation (Figure S5). However, under intensifying SSP scenarios, the difference becomes increasingly pronounced. In high-emission pathways (SSP7.0 and SSP8.5), particularly with high-ECS models like MC7, APSIM-driven irrigation substantially enhances water uptake, supporting marked improvements in yield. This reflects APSIM's capacity to better match water application to the maize's physiological needs under rising climate stress, resulting in more favorable outcomes for maize productivity. Notably, APSIM-based irrigation doses (Supplementary files – "Farm 2" simulations) show a declining trend under more severe climate scenarios. This is primarily linked to shorter maize growth cycles (Fig. 6), which reduce the crop's duration of water demand. Additionally, climate-induced stressors such as elevated temperatures diminish maize's capacity for biomass production and water-use efficiency (Li and Sun, 2016; Meng et al., 2016). As a result, APSIM adjusts its irrigation recommendations downward to align with the crop's reduced growth potential and resource needs.

## 4.4. Study limitations and perspectives

### 4.4.1. Uncertainty sources and future directions

Uncertainty in crop modeling applications is typically attributed to three primary sources (Challinor et al., 2013; Seidel et al., 2018; Tao et al., 2018; White et al., 2011): (i) model structure, which reflects the adequacy of the underlying biophysical equations to capture interaction within plant-soil-atmosphere continuum and relevant management practices; (ii) calibration of influential crop coefficients; and (iii) input data quality and relevant sources. In this study, structural limitations of APSIM included the absence of a potassium (K) fertilization module for all crops and of a phosphorus (P) component for potato. Because simulations were conducted under optimal management assumptions, these omissions are unlikely to have biased outcomes, yet they point to areas where future model developments could better represent nutrient-water-climate interactions in arid systems. With respect to calibration, uncertainties arise from the adjustment of difficult-to-measure coefficients, which is often influenced by methodological choices and potential user bias. To minimize this, we employed a standardized calibration protocol previously validated for APSIM (Mamassi et al., 2022) and other crop models (as DSSAT-Nwheat (Shawon et al., 2025)), used a large and heterogeneous set of field data (17 farms dataset for calibration process), and further reinforced the calibration through multi-site, multi-year validation against independent datasets; yielding strong statistical performance metrics. Finally, input data uncertainty, particularly in soil and meteorological parameters, is well-documented in large-scale modeling studies (Elli et al., 2020; Liu et al., 2020; Ojeda et al., 2018; Rigolot et al., 2017). Here, we employed site-specific databases tailored for modeling applications, which have been previously validated through rigorous uncertainty-sensitivity analyses to minimize input-related biases. Notably, the "Global High-Resolution Soil Profile Database for Crop Modeling Applications" has been evaluated via uncertainty analysis (Han et al., 2019), while the "CSIRO Protocol for the Development of APSoil Parameter Values for Use in APSIM" was specifically designed for APSIM applications and similarly assessed for parameter uncertainty (Dalgiesh et al., 2016). Despite these precautions, we acknowledge that uncertainties in climate projections, soil attributes, and crop responses could propagate through the model and influence results. Given the extensive scope of the present work—spanning three crops, 22 farms, seven climate models, and four climate scenarios—the outputs already provide a valuable basis for future research. A logical next step will be to conduct a dedicated uncertainty-sensitivity analysis (e.g., using Bayesian or Monte Carlo approaches) as an independent study, building directly on the simulation framework established here. This sequencing follows a widely adopted pathway in crop modeling studies, where initial assessments focus on establishing system responses, then
standalone investigations that quantify uncertainties and explore their implications for interpretation and policy relevance.

### 4.4.2. Pathways for integrated modeling framework in UAE's arid agro-environments.

This study evaluated three crops across 22 farms, seven climate models, and four climate scenarios, with the detailed analysis centered on one representative site (Farm 2). While this site captures typical UAE conditions—high heat, erratic rainfall, sandy-saline soils, and intensive management—the focus inevitably limits discussion of the full spatial heterogeneity of UAE agriculture. To address this, full APSIM outputs for all 22 sites are made available in a public repository (https://nextcloud.inrae.fr/s/JHTiL4iSBbbycXi). Future research should extend these analyses by systematically comparing agro-pedo-environmental zones (e. g., inland desert versus coastal systems), integrating additional dimensions such as groundwater dynamics, energy requirements, and socio-economic drivers. Such efforts would enable more robust food-water-energy nexus assessments, generate site-specific adaptation pathways, and directly inform both farm-level management and national policy on climate-resilient agriculture in the UAE.

Finally, although the APSIM framework (or overall crop simulation models) is well-suited to simulate crop eco-physiological responses and irrigation management, it does not account for broader Water-Energy-Food interactions (WEF nexus); such as the energy intensity of groundwater extraction or the long-term sustainability of aquifer use under cumulative pressures. Bridging these gaps will require integrating APSIM outputs with hydrological and energy-system models (McNider et al., 2015; Talbot and Monfet, 2024), or complementing them with energy-water resource datasets and embedding these within empirical approaches (Yupanqui et al., 2025), to more accurately quantify trade-offs and synergies across the WEF nexus in the region. Extending such analyses beyond the field scale to larger spatial domains; such as watersheds or agro-ecological zones, would allow for a more comprehensive evaluation of resource dynamics, infrastructure constraints, and policy interventions (Mamassi et al., 2025). Such integrated modeling would enable stakeholders to anticipate thresholds of groundwater availability, evaluate the energy footprint of irrigation under future scenarios. Thus, future research in this direction could provide more holistic evidence to guide investments in water-saving technologies, renewable energy use in irrigation, and infrastructure planning for climate-resilient agriculture in the UAE.

## 5. Conclusion

Once heavily dependent on imported food, the UAE has expanded agriculture through intensive dryland cropping, drawing mainly on groundwater. Rapid depletion of this resource, coupled with rising food-water demand, has heightened water scarcity. Our study provides a model-based assessment and practical guidelines for managing cropping systems and adapting irrigation under climate change in the UAE's arid agro-environment. Robust validation of the APSIM model strengthens confidence in its use for assessing crop state variables under arid conditions. Simulating climate change scenarios for cropping systems, we reveal the urgent need to understand and mitigate UAE's arid environmental risks that disrupt crop scheduling, reduce productivity, and compromise water-use efficiency. Our findings emphasize investing in resilient crops and stress-tolerant cultivars, alongside soil-water management, efficient irrigation, and climate-smart practices, to enhance agricultural resilience under current and projected UAE arid agro-ecosystem. Moreover, this study demonstrates the effectiveness of APSIM in guiding crop-specific irrigation under diverse climate scenarios. Findings indicate that aligning crop production and cycle length with the available water supply is crucial for the sustainability of agricultural activities and the economic viability of irrigation practices. We have provided a compelling discussion on the need for such alignment and the benefits it can bring to the agricultural sector. Finally, the

APSIM simulations across various UAE's locations provide key insights for site-specific adaptation and evidence-based policy across the diverse agricultural landscapes. Future research should examine how integrated climate change and irrigation scenarios affect different agro-pedo-environmental zones, from arid inland areas to coastal regions, to enable context-specific assessment of crop resilience and guide tailored adaptation strategies for farmers.

## CRediT authorship contribution statement

**Hamed Abdullah Al Hashmi:** Validation, Investigation, Data curation. **Hamza Bouras:** Writing – original draft, Visualization, Validation, Software, Formal analysis. **Francesco Accatino:** Writing – original draft, Visualization, Validation, Formal analysis. **Rashyd Zaatboul:** Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **MAMASSI Achraf:** Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization. **Nhamo Nhamo:** Writing – original draft, Validation, Resources, Investigation, Data curation, Conceptualization. **Sridhar Gummadi:** Validation, Investigation, Data curation. **Khalil Ammar:** Validation, Resources, Methodology, Data curation, Conceptualization. **Mohamed Ahmed Hamdan Al Dhanhani:** Validation, Resources, Data curation.

## 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

This work was made possible with the valuable support of the International Center for Biosaline Agriculture (ICBA), the UAE Space Agency (UAESA), and the UAE's Ministry of Climate Change and Environment (MOCCAE). Their contributions and collaboration are gratefully acknowledged.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agwat.2025.109819.

## Data availability

Data will be made available on request.

Supplementary files - 22-sites simulations and figures (Nextcould)

## References

- Abdelfattah, M.A., 2013. Integrated suitability assessment: a way forward for land use planning and sustainable development in Abu Dhabi, United Arab Emirates. Arid L. Res. Manag. 27, 41–64. https://doi.org/10.1080/15324982.2012.722579.

- Abdelhakim, L.O.A., Palma, C.F.F., Zhou, R., Wollenweber, B., Ottosen, C.O., Rosenqvist, E., 2021. The effect of individual and combined drought and heat stress under elevated CO2 on physiological responses in spring wheat genotypes. Plant Physiol. Biochem. 162, 301–314. https://doi.org/10.1016/j.PLAPHY.2021.02.015.

- Adekanmbi, T., Wang, X., Basheer, S., Liu, S., Yang, A., Cheng, H., 2023. Climate change impacts on global potato yields: a review. Environ. Res. Clim. 3, 12001. https://doi.org/10.1088/2752-5295/AD0E13.

- Ahmed, M., Akram, M.N., Asim, M., Aslam, M., Hassan, F. ul, Higgins, S., Stöckle, C.O., Hoogenboom, G., 2016. Calibration and validation of APSIM-Wheat and CERES-Wheat for spring wheat under rainfed conditions: models evaluation and application. Comput. Electron. Agric. 123, 384–401. https://doi.org/10.1016/j.compag.2016.03.015.

- Ajaj, R.M., Shahin, S.M., Salem, M.A., 2018. The challenges of climate change and food security in the United Arab Emirates (UAE): from deep understanding to quick
actions. Curr. Nutr. Food Sci. 15, 422–429. https://doi.org/10.2174/1573401314666180326163009.

Al-Qodsi, E.M., Jadahlaq, I.M., El-Maknouzi, M.E.H., 2024. The place of UAE's food security in The National legislation and its role in supporting global food security. Cogent. Soc. Sci. 10, 2319379. https://doi.org/10.1080/23311886.2024.2319379; TYTYPE:STRING;JOURNAL.

Asseng, S., Cao, W., Zhang, W., Ludwig, F., 2009. Crop Physiology, Modelling and Climate Change: Impact and Adaptation Strategies, In: Crop Physiology: Applications for Genetic Improvement and Agronom. Elsevier, Amsterdam.

Benitez-Alfonso, Y., Soanes, B.K., Zimba, S., Sinanaj, B., German, L., Sharma, V., Bohra, A., Kolesnikova, A., Dunn, J.A., Martin, A.C., Khashi u Rahman, M., Saati-Santamaría, Z., García-Fraile, P., Ferreira, E.A., Frazaó, L.A., Cowling, W.A., Siddique, K.H.M., Pandey, M.K., Farooq, M., Varshney, R.K., Chapman, M.A., Boesch, C., Daszkowska-Golec, A., Foyer, C.H., 2023. Enhancing climate change resilience in agricultural crops. Curr. Biol. 33, R1246–R1261. https://doi.org/10.1016/J.CUB.2023.10.028.

Boote, K.J., 1999. Concepts for calibrating crop growth models. DSSAT Version, pp. 179–199 vol. 3.

Borsato, E., Rosa, L., Marinello, F., Tarolli, P., D'Odorico, P., 2020. Weak and strong sustainability of irrigation: a framework for irrigation practices under limited water availability. Front. Sustain. Food Syst. 4. https://doi.org/10.3389/FSUFS.2020.00017.

Borus, D., Parsons, D., Boersma, M., Brown, H., Mohammed, C., 2018. Improving the prediction of potato productivity: APSIM-Potato model parameterization and evaluation in Tasmania, Australia. Aust. J. Crop Sci. 12, 32–43. https://doi.org/10.21475/AJCS.18.12.01.PNE570.

Bouras, H., Mamassi, A., Devkota, K.P., Chouk-Allah, R., Bouazzama, B., 2023. Integrated effect of saline water irrigation and phosphorus fertilization practices on wheat (Triticum aestivum) growth, productivity, nutrient content and soil properties under dryland farming. Plant Stress 10, 100295. https://doi.org/10.1016/J.STRESS.2023.100295.

Brown, H., Huth, N., Holzworth, D., 2011. A potato model build using the APSIM Plant. NET framework. 19th International Congress on Modelling and Simulation, Perth, Australia. 12–16 December 2011. 961–967.

Casadebaig, P., Chenu, K., Faivre, R., 2014. Sensitivity analysis of dynamic crop models to assist crop science: assessing the impact of multiple traits on yield in Australian wheat. 3èmes Reconences R Montpellier. Queensland Alliance for Agriculture and Food Innovation, Montpellier, France, pp. 1–2.

Challinor, A.J., Smith, M.S., Thornton, P., 2013. Use of agro-climate ensembles for quantifying uncertainty and informing adaptation. Agric. For. Meteor. 170, 2–7. https://doi.org/10.1016/j.agrformet.2012.09.007.

Chisanga, C.B., Phiri, E., Chineye, V.R.N., Chabala, L.M., 2020. Projecting maize yield under local-scale climate change scenarios using crop models: sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates. Food Energy Secur. 9, e231. https://doi.org/10.1002/FES3.231.

Dahri, S.H., Shaikh, I.A., Talpur, M.A., Mangrio, M.A., Dahri, Z.H., Hoogenboom, G., Knox, J.W., 2004. Modelling the impacts of climate change on the sustainability of rainfed and irrigated maize in Pakistan. Agric. Water Manag. 296, 108794. https://doi.org/10.1016/J.AGWAT.2024.108794.

Dalgliesh, N., Hochman, Z., Huth, N., Holzworth, D., 2016. CSIRO agriculture and food: A Protocol for the Development of APSOIL Paramtervalues for Use in APSIM. Australia.

Dasgupta, S., Robinson, E.J.Z., 2022. Attributing changes in food insecurity to a changing climate. Sci. Rep. 12, 1–11. https://doi.org/10.1038/s41598-022-08696-x; SUBJMETA=2739,704,843,844;KWRD=CLIMATE-CHANGE+IMPACTS, ENVIRONMENTAL+ECONOMICS.

Dewedar, O., Plauborg, F., El-Shafie, A., Marwa, A., 2021. Response of potato biomass and tuber yield under future climate change scenarios in Egypt. J. Water L. Dev. 139–150. https://doi.org/10.24425/jwld.2021.137106.

Ebraheem, A.A., Alshamsi, D., Sherif, M., Neuman, E., Alghafli, K., Al Mulla, M., Sefelnasr, A., 2023. Mapping Groundwater Potential Zones in UAE Using GIS-Based Hydrogeological Modeling 255–268. https://doi.org/10.1007/978-3-031-24506-0-17.

Elli, E.F., Huth, N., Sentelhas, P.C., Carneiro, R.L., Alvares, C.A., 2020. Ability of the APSIM next generation eucalyptus model to simulate complex traits across contrasting environments. Ecol. Model. 419, 108959. https://doi.org/10.1016/j.ecolmodel.2020.108959.

FAO, 2021. The State of Food Security and Nutrition in the World 2021 [WWW Document]. State Food Secur. Nutr. World 2021. https://doi.org/10.4060/CB4474EN.

Fiser, C., Zagmajster, M., Jemec Kokalj, A., Mali, N., Šumrada, T., Glavan, M., Hose, G.C., Schwartz, B., Lorenzo, T., Di, Griebler, C., Cvejić, R., 2025. Toward sustainable irrigation practices safeguarding groundwater biodiversity and ecosystem services. Bioscience 0 115. https://doi.org/10.1093/BIOSCI/BIAF016.

Gaydon, D., 2014. The APSIM Model – An Overview, in: SAARC Agriculture Centre (Ed.), The SAARC-Australia Project-Developing Capacity in Cropping Systems Modelling for South Asia. Dhaka, Bangladesh, pp. 15–31.

Gummadi, S., Kadiyala, M.D.M., Rao, K.P.C., Athanasiadis, I., Mulwa, R., Kilavi, M., Legesse, G., Amede, T., 2020. Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in embu county, Kenya. PLoS One 15, e0241147. https://doi.org/10.1371/JOURNAL.PONE.0241147.

Han, E., Ines, A.V.M., Koo, J., 2019. Development of a 10-km resolution global soil profile dataset for crop modeling applications. Environ. Model. Softw. 119, 70–83. https://doi.org/10.1016/j.envsoft.2019.05.012.

Heinz, M., Galetti, V., Holzkämper, A., 2024. How to find alternative crops for climate- resilient regional food production. Agric. Syst. 213, 103793. https://doi.org/10.1016/J.AGSY.2023.103793.

Hochman, Z., 1982. Effect of water stress with phasic development on yield of wheat grown in a semi-arid environment. F. Crop Res. 5, 55–67. https://doi.org/10.1016/0378-4290(82)90006-5.

Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S., Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E., Hammer, G.L., Robertson, M.J., Dimes, J.P., Whitbread, A.M., Hunt, J., van Rees, H., McClelland, T., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., Keating, B.A., 2014. APSIM - evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009.

Hoogenboom, G., White, J., Messina, C., 2004. From genome to crop: integration through simulation modeling. F. Crop. Res. 90, 145–163.

Hyles, J., Bloomfield, M.T., Hunt, J.R., Trethowan, R.M., Trevaskis, B., 2020. Phenology and related traits for wheat adaptation, 2020 125 Heredity 125, 417–430. https://doi.org/10.1038/s41437-020-0320-1.

Jia, H., Chen, F., Zhang, C., Dong, J., Du, E., Wang, L., 2022. High emissions could increase the future risk of maize drought in China by 60–70. Sci. Total Environ. 852, 158474. https://doi.org/10.1016/J.SCITOTENV.2022.158474.

Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N., Meinke, H., Hochman, Z., 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288.

Keating, B.A., Thorburn, P.J., 2018. Modelling crops and cropping systems—evolving purpose, practice and prospects. Eur. J. Agron. 100, 163–176. https://doi.org/10.1016/J.EJA.2018.04.007.

Kheir, A.M.S., Alkharabsheh, H.M., Seleiman, M.F., Al-Saif, A.M., Ammar, K.A., Attia, A., Zoghdan, M.G., Shabana, M.M.A., Aboelsoud, H., Schillaci, C., 2021. Calibration and Validation of AQUACROP and APSIM Models to Optimize Wheat Yield and Water Saving in Arid Regions. https://doi.org/10.3390/land10121375.

Kim, Y.U., Webber, H., 2024. Contrasting responses of spring and summer potato to climate change in South Korea. Potato Res 67, 1265–1286. https://doi.org/10.1007/S11540-024-09691-7/FIGURES/11.

Kirina, T., Sutip, I., Groot, A., Ludwig, F., Demissie, T., 2025. Projected climate change impacts on potato yield in east Africa. Eur. J. Agron. 166, 127560. https://doi.org/10.1016/J.EJA.2025.127560.

Kizhisseri, M.I., Mohamed, M.M., El-Shorbagy, W., Chowdhury, R., McDonald, A., 2021. Development of a dynamic water budget model for Abu Dhabi emirate. UAE. PLoS One 16, e0245140. https://doi.org/10.1371/JOURNAL.PONE.0245140.

Kumar, U., Hansen, E.M., Thomsen, I.K., Vogeler, I., 2023. Performance of APSIM to simulate the dynamics of winter wheat growth, phenology, and nitrogen uptake from early growth stages to maturity in Northern Europe. Plants 12, 986. https://doi.org/10.3390/PLANTS12050986/S1.

Levy, D., Fogelmann, E., Itzhak, Y., 1988. The effect of water salinity on potatoes (Solanum tuberosum L.): physiological indices and yielding capacity. Potato Res 31, 601–610. https://doi.org/10.1007/BF02361852/METRICS.

Li, Z., He, J., Xu, X., Jin, X., Huang, W., Clark, B., Yang, G., Li, Z., 2018. Estimating genetic parameters of DSSAT-CERES model with the GLUE method for winter wheat (Triticum aestivum L.) production. Comput. Electron. Agric. 154, 213–221. https://doi.org/10.1016/j.compag.2018.09.009.

Li, Z., Sun, Z., 2016. Optimized single irrigation can achieve high corn yield and water use efficiency in the corn belt of northeast China. Eur. J. Agron. 75, 12–24. https://doi.org/10.1016/J.EJA.2015.12.015.

Liu, H., Pequeno, D.N.L., Hernández-Ochoa, I.M., Krupnik, T.J., Sonder, K., Xiong, W., Xu, Y., 2020. A consistent calibration across three wheat models to simulate wheat yield and phenology in China. Ecol. Model. 430, 109132. https://doi.org/10.1016/j.ecolmodel.2020.109132.

Ludwig, M., 2013. Evolution of the C4 photosynthetic pathway: events at the cellular and molecular levels. Photosynth. Res. 117, 147–161. https://doi.org/10.1007/S11120-013-9853-Y.

Mamassi, A., Balaghi, R., Devkota, K.P., Bouras, H., El-Gharous, M., Tychon, B., 2023a. Modeling genotype × environment × management interactions for a sustainable intensification after rainfed wheat cropping system in Morocco, 2023 121 Agric. Food Secur 12, 1–23. https://doi.org/10.1186/s40066-023-00428-2.

Mamassi, A., Guilpart, N., Muneret, L., Accatino, F., 2025. Water-cropland resources and agricultural management shape the main interactions with food self-sufficiency goals. Glob. Food. Sec. 44, 100841. https://doi.org/10.1016/J.GFS.2025.100841.

Mamassi, A., Marrou, H., El Gharous, M., Wellens, J., Jabbour, F.-E., Zeroual, Y., Hamma, A., Tychon, B., 2022. Relevance of soil fertility spatial databases for parameterizing APSIM-wheat crop model in Moroccan rainfed areas, 2022 425 Agron. Sustain. Dev. 42, 1–16. https://doi.org/10.1007/s13593-022-00813-4.

Manikas, I., Sundarakani, B., Anastasiadis, F., Ali, B., 2022. A framework for food security via resilient Agri-Food supply chains: case of UAE, 2022, Vol. 14, 6375 Sustain 14, 6375. https://doi.org/10.3390/SU1406375.

McNider, R.T., Handside, C., Doty, K., Ellenburg, W.L., Cruise, J.F., Christy, J.R., Moss, D., Sharda, V., Hoogenboom, G., Caldwell, P., 2015. An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands. Environ. Model. Softw. 72, 341–355. https://doi.org/10.1016/J.ENVSOFT.2014.10.009.

Medina, R., Canaza, B., Sullon, A., Ivanovich, M.E., Maquera, G., 2025. Intelligent automated control of abiotic water and thermal stress in greenhouses for the
optimization of pre-basic potato production. Smart Agric. Technol., **101272** https://doi.org/10.1016/J.ATECH.2025.101272

Meng, Q., Chen, X., Lobell, D.B., Cui, Z., Zhang, Y., Yang, H., Zhang, F., 2016. Growing sensitivity of maize to water scarcity under climate change. Sci. Rep. 6, 1-7. https://doi.org/10.1038/SREP19605; TECHMETA=139; SUBJMETA=106,443,631,694,704; KWRD=CLIMATE+CHANGE,PHYSIOLOGY.

Mondo, J.M., Chuma, G.B., Matiti, H.M., Kihye, J.B., Bagula, E.M., Karume, K., Kahindo, C., Egeru, A., Majaliwa, J.G.M., Agre, P.A., Adebola, P.A., Asfaw, A., 2024. Crop calendar optimization for climate change adaptation in yam farming in South-Kivu, eastern D.R. Congo. PLoS One 19, e0309775. https://doi.org/10.1371/JOURNAL.PONE.0309775.

Mulders, P.J.A.M., van den Heuvel, E.R., van de Molengraft, M.J.G., Heemels, W.P.M.H., Reidsma, P., 2024. Extreme drought and rainfall had a large impact on potato production in the Netherlands between 2015 and 2020. Commun. Earth Environ. 5, 1-9. https://doi.org/10.1038/S43247-024-01658-3; SUBJMETA=106,2739,4111,694,704; KWRD=CLIMATE-CHANGE+IMPACTS, NATURAL+HAZARDS.

Nawaz, H., Hussain Labar, N., Yasmeen, A., Rehmani, M.I.A., Hussain, N., Ishaq, M., Rehmani, A., Nasrullah, H.M., 2014. Pictorial review of critical stages at vegetative and reproductive growth in wheat for irrigation water regimes. Appl. Sci. Bus. Econ. 2, 1-7.

Ojeda, J.J., Caviglia, O.P., Volenec, J.J., Brouder, S.M., Agnusdei, M.G., 2018. Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM. Agric. Water Manag. 195, 154-171. https://doi.org/10.1016/j.agwat.2017.10.010.

Ojeda, J.J., Huth, N., Holzworth, D., Raymundo, R., Zyskowski, R.F., Sinton, S.M., Michel, A.J., Brown, H.E., 2021. Assessing errors during simulation configuration in crop models – a global case study using APSIM-Potato. Ecol. Model. 458, 109703. https://doi.org/10.1016/J.ECOLMODEL.2021.109703.

Pakrooh, P., Kamal, M.A., 2023. Modeling the potential impacts of climate change on wheat yield in Iran: evidence from national and provincial data analysis. Ecol. Model. 486, 110513. https://doi.org/10.1016/J.ECOLMODEL.2023.110513.

Peng, B., Guan, K., Tang, J., Ainsworth, E.A., Asseng, S., Bernacchi, C.J., Cooper, M., Delucia, E.H., Elliott, J.W., Ewert, F., Grant, R.F., Gustafson, D.I., Hammer, G.L., Jin, Z., Jones, J.W., Kimm, H., Lawrence, D.M., Li, Y., Lombardozzi, D.L., Marshall-Colon, A., Messina, C.D., Ort, D.R., Schnable, J.C., Vallejos, C.E., Wu, A., Yin, X., Zhou, W., 2020. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nat. Plants 2020 64 6, 338-348. https://doi.org/10.1038/s41477-020-0625-3.

Rezaei, E.E., Siebert, S., Hüging, H., Ewert, F., 2018. Climate change effect on wheat phenology depends on cultivar change. Sci. Rep. 8, 1-10. https://doi.org/10.1038/S41598-018-23101-2; SUBJMETA=2653,449,631,711; KWRD=PLANT+BREEDING, PLANT+DEVELOPMENT.

Rigolot, C., de Voil, P., Douxchamps, S., Prestwidge, D., Van Wijk, M., Thornton, P.K., Rodriguez, D., Henderson, B., Medina, D., Herrero, M., 2017. Interactions between intervention packages, climatic risk, climate change and food security in mixed crop-livestock systems in Burkina Faso. Agric. Syst. 151, 217-224. https://doi.org/10.1016/j.agsy.2015.12.017.

Robertson, M.J., Carberry, P.S., Huth, N.I., Turpin, J.E., Probert, M.E., Poulton, P.L., Bell, M., Wright, G.C., Yeates, S.J., Brinsmead, R.B., 2002. Simulation of growth and development of diverse legume species in APSIM. Aust. J. Agric. Res. 53, 429-446. https://doi.org/10.1071/AR01106.

Saïf Al Qaydi, 2016. The status and prospects for agriculture in the United Arab Emirates (UAE) and their potential to contribute to food security. J. Basic Appl. Sci. 12, 155-163. https://doi.org/10.6000/1927-5129.2016.12.23.

Seidel, S.J., Palosuo, T., Thorburn, P., Wallach, D., 2018. Towards improved calibration of crop models – where are we now and where should we go? Eur. J. Agron. 94, 25-35. https://doi.org/10.1016/j.eja.2018.01.006.

Shabbir, S.A., Abdelfattah, M.A., 2008. Soils of Abu Dhabi emirate. In: Perry, Richard J. (Ed.), Terrestrial Environment of Abu Dhabi Emirate. Environment Agency, Abu Dhabi, pp. 1-463.

Shawon, A.R., Attia, A., Ko, J., Memic, E., Uptmoor, R., Hackauf, B., Feike, T., 2025. Impact of calibration strategy and data on wheat simulation with the DSSAT-Nwheat model. Agron. J. 117, e70111. https://doi.org/10.1002/AGJ2.70111; JOURNAL: JOURNAL:14350645; PAGE:STRING;ARTICLE/CHAPTER.

Shoukat, M.R., Wang, J., Habib-ur-Rahman, M., Hui, X., Hoogenboom, G., Yan, H., 2024. Adaptation strategies for winter wheat production at farmer fields under a changing climate: employing crop and multiple global climate models. Agric. Syst. 220, 104066. https://doi.org/10.1016/J.AGSY.2024.104066.

Talbot, M.H., Monfet, D., 2024. Development of a crop growth model for the energy analysis of controlled agriculture environment spaces. Biosyst. Eng. 238, 38-50. https://doi.org/10.1016/J.BIOSYSTEMSENG.2023.12.012.

Tang, J., Bai, H., Zhang, X., Wang, R., Guo, F., Xiao, D., Zhou, H., 2022. Reducing potato water footprint by adjusting planting date in the agro-pastoral ecotone in north China. Ecol. Model. 474, 110155. https://doi.org/10.1016/J.ECOLMODEL.2022.110155.

Tang, J., Bai, H., Zhang, S., Xiao, D., Tianzhu, Z., Li Liu, D., Wang, B., Feng, P., 2024. Adaptations of potato production to future climate change by optimizing planting date, irrigation and fertilizer in the Agro-Pastoral ecotone of China. Clim. Risk Manag 44, 100604. https://doi.org/10.1016/J.CRM.2024.100604.

Tao, F., Rötter, R.P., Palosuo, T., Gregorio Hernández Díaz-Ambrona, C., Mínguez, M.I., Semenov, M.A., Kersebaum, K.C., Nendel, C., Specka, X., Hoffmann, H., Ewert, F., Dambreville, A., Martre, P., Rodríguez, L., Ruiz-Ramos, M., Gaiser, T., Höhn, J.G., Salo, T., Ferrise, R., Bindi, M., Cammarano, D., Schulman, A.H., 2018. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol. 24, 1291-1307. https://doi.org/10.1111/GCB.14019.

Teng, J., Hou, R., Dungait, J.A.J., Zhou, G., Kuzyakov, Y., Zhang, J., Tian, J., Cui, Z., Zhang, F., Delgado-Baquerizo, M., 2024. Conservation agriculture improves soil health and sustains crop yields after long-term warming. Nat. Commun. 15, 1-12. https://doi.org/10.1038/s41467-024-53169-6.

Vennam, R.R., Bheemanahalli, R., Reddy, K.R., Dhillon, J., Zhang, X., Adeli, A., 2024. Early-season maize responses to salt stress: Morpho-physiological, leaf reflectance, and mineral composition. J. Agric. Food Res. 15, 100994. https://doi.org/10.1016/J.JAFR.2014.100994.

Webber, H., Gaiser, T., Ewert, F., 2014. What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa? Agric. Syst. 127, 161-177. https://doi.org/10.1016/J.AGSY.2013.12.006.

Wei, D., Castro, L.G., Chhatre, A., Tuninetti, M., Davis, K.F., 2025. Swapping rice for alternative cereals can reduce climate-induced production losses and increase farmer incomes in India. Nat. Commun. 16, 1-8. https://doi.org/10.1038/S41467-025-57420-6; SUBJMETA=1143,134,685,704,706,841,844; KWRD=AGRICULTURE, CLIMATE-CHANGE+ADAPTATION,DEVELOPING+WORLD,SUSTAINABILITY.

White, J.W., Hoogenboom, G., Kimball, B.A., Wall, G.W., 2011. Methodologies for simulating impacts of climate change on crop production. F. Crop. Res. 124, 357-368. https://doi.org/10.1016/j.fcr.2011.07.001.

World Bank, 2021. The World Bank [WWW Document]. United Arab Emirates Database. URL https://data.worldbank.org/country/united-arab-emirates (Accessed 11.17.22).

Xiong, W., Reynolds, M.P., Montes, C., Crossa, J., Snapp, S., Akin, B., Mesut, K., Ozdemir, F., Li, H., He, Z., Wang, D., Chen, F., 2024. New wheat breeding paradigms for a warming climate. Nat. Clim. Chang 14, 869-875. https://doi.org/10.1038/S41558-024-02609-0; SUBJMETA=106,158,2456,2739,631,694,704; KWRD=AGROECOLOGY,CLIMATE-CHANGE+IMPACTS.

Yadav, S.B.K., Bhatt, S., 2024. Heat stress and its tolerance in wheat. Cogent Food Agric. 10, 2413398. https://doi.org/10.1080/23311932.2024.2413398; PAGE:STRING; ARTICLE/CHAPTER.

Yang, X., Zhang, X., Zhao, Z., Ma, N., Tian, J., Xu, Z., Zhang, J., Zhang, Y., 2025. Rainfall and maximum temperature are dominant climatic factors influencing APSIM-Maize cultivar parameters sensitivity in semiarid regions. Eur. J. Agron. 164, 127494. https://doi.org/10.1016/J.EJA.2024.127494.

Yupanqui, C., Dias, N., Goodarzi, M.R., Sharma, S., Vaghee, H., Mohtar, R., 2025. A review of water-energy-food nexus frameworks, models, challenges and future opportunities to create an integrated, national security-based development index. Energy Nexus 18, 100409. https://doi.org/10.1016/J.NEXUS.2025.100409.

Zhang, T., He, Y., DePauw, R., Jin, Z., Garvin, D., Yue, X., Anderson, W., Li, T., Dong, X., Zhang, T., Yang, X., 2022. Climate change May outpace current wheat breeding yield improvements in North America. Nat. Commun. 13, 1-9. https://doi.org/10.1038/s41467-022-33265-1.

Zheng, B., Chenu, K., Doherty, A., Chapman, S., 2015. The APSIM-Wheat Module (7.5 R3008). Queensland, Australia.