Cropping patterns for phenology stability and resource conservation under extreme climates

Context: Cultivation pattern is the foundation of agricultural policy, enabling farmers to perform various farming operations efficiently. In the meantime, with an increasing frequency of extreme climate events, it is imperative to devise low-cost strategies to anticipate disastrous climate events and achieve regional food security even in regions facing typhoons, floods, and extreme temperatures.

Objective: The study was primarily conducted to evaluate crop cultivation patterns, focusing on their intrinsic climate mitigation potential and phenological stability to secure sufficient food.

Methods: Overall, the study was performed in three phases: i) crop type evaluation, ii) cultivation pattern analysis, and iii) cropping area analysis. In the first phase, the study used climate data of 1979–2020 with reference to seven crop types (i.e., cereals, oilseed, vegetables, beans, peas, nuts, and tubers) and evaluated yield reductions and phenological shifts.

Results and conclusions: : The results showed that tubers and nuts were the most resilient crops to extreme environmental events, whereas cereals (conventional staple crops) and oilseed (essential food crops) were most vulnerable to extreme climate events. Time-course phenological stability was higher in nuts and tubers, which possessed elevated resistance against phenological shifts. In the second phase, intercropping was highlighted as a better cultivation pattern; however, the recommended cultivation pattern was agroforestry due to its significant advantages over alley cropping, forest farming, and windbreaks. The third phase recommended incorporating windbreak plants in the perpendicular single-row formation with an interrow distance of 20 r (r—horizontal radius of the tree canopy).

Significance: Overall, the study aligns the fundamental aspects of agroecology with redesigning crop cultivation policies and has global implications in agricultural systems to mitigate extreme environments and ensure food security.

Introduction

Climate is a combination of multiple physical factors jointly supporting life on this planet. A range of specific balances among those physical factors ensures species’ survival and life activities (Adil et al., 2024; Ahmad and Liu 2022). This balance is bilaterally interconnected with anthropogenic activities (Raza et al., 2023; Hussain et al., 2024). Recent decades have seen some serious perturbations and lethal impacts of human activities on the climate and vice versa, ultimately damaging agriculture and the goal of food security. Sudden environmental changes and extreme climate events have become more frequent, permanently damaging crops and croplands (FAO, 2015). The diversity in agroclimates makes the situation more complicated and vulnerable to change. Almost 85% of the cultivated agricultural landholdings are sensitive to severe climates globally (Shukla et al., 2021). Linear climate changes have been estimated to reduce 15%–18% of the income of irrigated farms and 20%–25% of the income of unirrigated farmlands. However, sudden climate changes can cause up to 87% of economic loss even when other crop factors remain optimal for plant growth. These sudden climatic changes are termed as extreme climatic events, and there is clear evidence of the increasing intensity and frequency of ECEs since the last five decades (Ummenhofer and Meehl, 2017). Extreme climatic events considered in this study include i) heatwaves (>35°C, ≥3 days), ii) cold spells (<5°C, ≥3 days), iii) extreme precipitation (≥95th percentile rainfall), iv) drought (PDSI < –3), v) high-wind events (>25 m s⁻¹ sustained for ≥2 h), and vi) floods (water depth >0.5 m over ≥48 h). These events vary geographically: heatwaves dominate subtropical Asia and Africa, while high-wind and flood events are frequent in Southeast Asia, the Caribbean, and coastal Americas (Lewsey et al., 2004; UNCTAD, 2019).

Cultivating a wide range of food crops is essential due to their edible, nutritional, financial, sociological, ecological, and culinary significance (FAO, 2017). Food crops, such as oilseeds, vegetables, beans, nuts, peas, and tubers, are essential for ensuring food security (Ali et al., 2017). They help meet the dietary needs of populations worldwide by providing the necessary calories, vitamins, minerals, and dietary fiber for human health. Diversification of food crops ensures a variety of options and reduces dependence on a single crop, thereby enhancing food security. Diversification of foods contributes to a balanced and nutritious diet, enhancing food security and human health. They form the basis of agricultural economies, providing income and livelihood opportunities for farmers, farm laborers, and those in the food supply chain (Mihailescu and Soares, 2020). The cultivation, processing, distribution, and trade of food crops contribute to economic growth, employment generation, and poverty reduction. Their integration into crop rotation systems maintains soil fertility, breaks pest and disease cycles (Shafique et al., 2014; Deng et al., 2019), and optimizes agricultural productivity by reducing soil nutrient depletion and improving the soil structure (Ahmad et al., 2022a).

Cultivating and managing crops is crucial for achieving sustainable development goals, ensuring food availability, and fostering resilient and inclusive agricultural systems (Meyer et al., 2012). Therefore, most regression analyses and crop models have been developed by focusing on food crops and can predict the crop response under given natural resources and linear climate changes (Ahmed et al., 2018). However, various food crops are sensitive to climate change (Abbas et al., 2017). Therefore, researchers have recommended growing food crops in combination with other crops. Basche and coworkers (Basche et al., 2016) recommended cultivating cover crops in maize fields to reduce water evaporation and to deal with soil erosion. Similarly, researchers devised methods to maintain natural resources by alternating the cultivation of vegetables with other crops. However, an efficient mitigation of disastrous environmental events still remains an unsolved challenge. There is also an information gap in controlling trenchant winds to avoid erosion, crop lodging, and soil water dryness. There is a need to investigate crop cultivation policies on a deeper scale to deal with these threats.

Researchers have long focused on developing climate-resilient crops to deal with the changing climate and associated threats (Ahmad et al., 2020). Although climate resilience could partially tackle climate issues, plant tolerance has a certain limit. Moreover, it cannot minimize cropland damage caused by erosion, stagnant waters, or winds. Therefore, a more comprehensive way is imperative to be devised, which could ensure crop/food security and save the croplands for sustainable agriculture (Mohammady et al., 2022). Cropping pattern is one of the most important factors of agricultural policies that simultaneously impact resource utilization, survival, growth, and production of plants. Furthermore, it is one of the main tools to maintain the edaphic states of the farms (Mortensen and Smith, 2020). Soil erosion and natural resources can be managed by optimizing cropping patterns. Therefore, a more comprehensive approach is needed to ensure favorable impacts of cropping patterns on crop phenology and resistance to extreme climates.

This study aimed to i) quantify crop-type vulnerability and phenological stability under extreme climates; ii) evaluate the performance of different cropping patterns, including tree-incorporated systems, under these extremes; and iii) identify spatial planting strategies that improve resilience through airflow simulations. The research addressed three questions: 1) Which crop groups exhibit the highest phenological stability under extreme climates? 2) Which cropping patterns best conserve resources and reduce vulnerability? 3) How can optimized spatial tree arrangements mitigate wind-induced losses? Conventional cultivation patterns are no longer effective in times of sudden extreme climate events. Redesigning the entire crop cultivation plan and the food production chain needs to be addressed. Climate resilience is less diversified among different cultivars of a single species as compared to its diversity among different plant species. Given this background, the current study was designed to find an improved cropping system to enhance system resilience to climatic and extreme weather events. It developed a precise relationship between available natural resources and crop growth. Furthermore, it was also aimed to devise an optimized cropping pattern to support sustainable agriculture for food security. It also introduced economically efficient and highly effective agricultural adaptations for sustainable crop production. Modern tools utilizing artificial intelligence and environmental simulations have been used to investigate the extent to which extreme climatic events can damage agricultural farms cultivated under different cropping patterns. The current study devises an optimized cropping pattern for a resilient agricultural system as an improved cultivation policy.

Methodology

Study area

For this study, data on regional cropping patterns, including temporal crop rotation, annual meteorological details, and crop yields, were collected from different arable lands across geographic regions. Initially, data were collected worldwide on agricultural parameters of food crops (crop type, cultivation period, adopted cropping pattern, and crop yield) and extreme climate event patterns (time, intensity, and duration) of high temperature, low temperature, high precipitation, drought, and wind. At later stages, the data were screened according to crop cultivation patterns. The collected global data were subdivided into two major groups of cultivation patterns: i) without tree incorporation and ii) with incorporated trees. The common interests of key stakeholders were taken into account, and data were collected on food crops, and the impact of climate change on crop survival and yield was assessed, specifically high-end climate change or extreme climate events. The assessments were conducted to determine the degree to which the adaptation was made in the cropping patterns and the extent to which they were effective in mitigating extreme climate events. Hydroclimatic conditions and post-cultivational management practices (e.g., irrigation methods, fertilization patterns) were not considered in the study to exclude unwanted interference of irrelevant factors. Based on these census data, the study focused on the dynamics of cropping patterns and their impact on food crops.

Data collection

Initially, the crops were classified into seven groups, i.e., cereals, oilseed, vegetables, beans, peas, nuts, and tubers (Supplementary Table S1). Besides the main crops, spices and crops with specific flavors were also tagged as vegetables for the smooth application of the models. Moreover, the woody plants (i.e., trees and shrubs) were highlighted in red (which could be used to develop combinations in the case of agroforestry). Two different types of data were collected for each class, i.e., extreme climate data and agronomic data. Extreme climate was operationally defined as years with CEI ≥1.0, corresponding to the upper 10th percentile of combined temperature, precipitation, and wind anomalies. Crop vulnerability trajectories across phenophases were quantified using polynomial regressions of phase-specific normalized difference vegetation index (NDVI) deviations (R² > 0.83). The World Meteorological Organization (WMO) guidelines for plant phenological observations were strictly fulfilled to set the principal growth stages of monocot and dicot plants, which impacted the extraction, classification, and processing of phenological data (Koch et al., 2009). Furthermore, the cereal code for system uniform coding of phenologically similar phases was implemented for all the crop species. The temporal period selected for the datasets was 1979–2020 based on the availability of datasets with good resolution. The National Oceanic and Atmospheric Administration (NOAA) database was used to procure the climate datasets with complete spatial coverage and 5-km resolution for the continents in the Americas. Findings from phase 1 (crop sensitivity and phenological stability) informed the selection of cropping systems in phase 2, prioritizing those with minimal phenophase deviation (e.g., tubers in mixed cropping). Phase 3 used airflow simulations to align spatial tree placement (windbreaks) with crops showing the highest wind vulnerability, thereby integrating results into an adaptive land-use design.

The climate dataset used was from the NOAA Global Historical Climatology Network (NOAA-GHCN), with a spatial resolution of 5 km. The data covered the period from 1979 to 2020 and included temperature, precipitation, wind speed, and drought indices. The datasets were homogenized following NOAA protocols and validated using spatial consistency and absolute limit tests. For some regions (e.g., Asia and Africa), partial coverage (65%) was achieved and interpolated using regional means. The 85% area of Australia and Europe could be covered. Data on extreme climate events were downloaded from the database with a spatial resolution of 5 km and tagged with the crop-cultivated areas. The data were used to calculate the climate extreme index (CEI), and data points with the highest 10th percentile were extracted to be accounted for as extreme climate according to NOAA guidelines. Extreme climates were defined using NOAA’s CEI, the Palmer drought severity index (PDSI) for drought classification, and a derived threshold of >35°C for heatwaves. Flood and wind events were characterized by duration and wind speed anomalies, respectively, based on regional thresholds and literature guidelines. To cover the phenological aspects, a data mining operation was performed at the Web of Science (WoS) and Scopus databases using the logical strings of cropping pattern, cultivation pattern, extreme environment, and crop cultivation policy. Furthermore, logical strings of common English names of the crops were also added during the data search, accompanied by Boolean operators to narrow down the search for precise data collection, while additional temporal filters were added in each session to screen the literature published on and after 1979. Data were assessed for explicitly mentioned phenology and cropping patterns to check their relevance. A total of 645 events were found, which underwent three screening steps: i) identical repetitions, ii) confused origin, and iii) mixed impact. Finally, a data pool of 109 studies could be procured to be processed for downstream steps. The data were arranged according to temporal hierarchy, and after performing the modeling and simulation steps, the data underwent an aggregate statistical analysis. Therefore, the entire data processing could be divided into four steps: i) data mining, ii) data normalization and calibration, iii) modeling and simulation, and iv) statistical analysis. A screening test was performed for the phenological data of food crops using the spatial consistency test and the absolute limit test (Kaspar et al., 2015). Data encoding was performed using the mono-digit system devised by the Biologische Bundesanstalt, Bundessortenamt, and Chemical Industry (BBCH), limiting the illustrated output to two stages (i.e., emergence and maturity). The winter and spring varieties of every crop were separately classified and treated as individual crop batches during phenological studies (Wu et al., 2019). However, their yield anomalies were averaged under a similar type of extreme climatic conditions. A complete list of the symbols used in the current study is provided in Table 1.

Table 1

Table 1. A list of symbols and Boolean operators that were used in the manuscript.

Model development

The NDVI was considered a standard to assess plant survival against extreme climates. Although a formal multicriteria analysis (MCA) was not conducted, the study considered multiple indicators, NDVI yield stability, phenological shift resistance, and microclimate modulation, when evaluating agroforestry and intercropping systems. Moreover, the regional cases were individually studied using an empirically grounded model under the Aporia framework of agent-based modeling (ABM) parameters (Murray-Rust et al., 2014). The phenological parameters were analyzed using the phenology forcing model (PFM) for monocropping and crop rotation (Ahmad and Liu, 2022). The study focused on four major phenophases: emergence, vegetative stage, reproductive stage (including anthesis), and maturity. A shift was classified as significant if the phenophase deviation exceeded ±2 days compared to the long-term mean under control (non-extreme) conditions. This threshold was chosen based on BBCH guidelines and expert phenological literature. However, the mean values were calibrated for modeling in the case of intercropping, while an additional normalization step regarding the cropping share was performed in the case of mixed cropping (Richard et al., 2017). To estimate the impact of trees in agroforestry, PFM input values were calibrated with the MaxEnt values due to their high area under the curve (AUC) rating (0.77). Phenological stability was defined as the degree of shift in key phenophases (emergence, vegetative, reproductive, maturity) under extreme climate stress, compared to baseline conditions. The stability index was calculated as the mean absolute difference in days between stressed and non-stressed years, normalized over a 41-year time series (1979–2020). Lower phenophase deviation indicated greater stability. The temperature changes were incorporated as tree-based reductions in daily temperature (−4°C), shade-based (−2°C), maximum air temperature (−3°C), and minimum air temperature (+1°C) (Gomes et al., 2020). However, the lowest values of the temperature change mimicry were incorporated to synchronize the model with the data due to the datasets of extreme climates in the data pool. The shade-based temperature mimicry remained restricted to half of the cultivated area due to the spatially distributed trees in the cropping pattern. The newly developed parameters were used as input in the PFM to estimate the phenology shift for the tree-incorporated crop system.

Productivity and resilience were primarily measured via NDVI-based growth metrics and phenological stability. Conventional indices such as land equivalent ratio (LER) or soil health indicators were not used. Instead, resilience was inferred from stress-specific yield retention and phenophase constancy. The annual cultivation period for both winter and spring varieties was adjusted from the regional cultivation pattern as defined by the crop calendars in the International Production Assessment Division (IPAD), Foreign Agricultural Services by the United States Department of Agriculture (USDA) (IPAD-CCC, ). Yield reductions were estimated using NDVI-based regression models calibrated with observed yield datasets across climatic extremes. Climate anomalies (e.g., temperature spikes, drought severity, extreme wind) were used as independent variables to quantify their effect on NDVI as a proxy for yield. No mechanistic crop growth models (e.g., DSSAT, APSIM) were applied due to heterogeneity in crop species and spatial domains. Yield anomalies were calculated relative to a baseline period of 1980–1990, representing years with minimal reported climate extremes. Control datasets from unaffected zones (i.e., years with CEI < 0.5) were used for calibration in each crop group to differentiate climate-driven effects. The resilience of each crop group was assessed using a composite approach that included NDVI-based yield reduction estimates under extreme events, as well as phenophase shift resistance. Tolerance thresholds were based on the NOAA-guided CEI data and included parameters such as high temperature events (>35°C), drought severity (PDSI scale), and flood duration. These stressor thresholds were linked with the phenological and productivity responses of each crop type.

Uncertainty analysis

The uncertainty analysis was performed based on the NDVI and crop production data collected from the WoS and other databases (IPAD-CPM Crop Production Maps, ; Becker-Reshef et al., 2022). NDVI values were directly used in the model; however, the yield data were transformed into per-hectare yields to calibrate the cropping system performance. The heat energy converted to biomass was enhanced to 10% to balance the temperature reductions by the shade impact of trees while calculating the heat units (HU). The harvest index (HI) was calibrated to the belowground yield of nuts and tubers and to the aboveground parts for the rest of the crop types (Asefa, 2019). Initially, the datasets for AUC values not less than 0.77 were screened to ensure the inclusion of only reliable data components. A couple of weeks after an extreme climate event (based on the NOAA guidelines), the HI was categorized as the stress harvest index. All these parameters jointly calculated the generalized likelihood uncertainty estimation. To ensure dataset reliability, two independent filters were applied: a) model components with the area under the curve (AUC) ≥0.77 in cross-validation were retained, and b) each crop’s performance was evaluated using both NDVI-derived and HI-derived productivity indices. Only parameters passing both thresholds were accepted for uncertainty estimation. The following algorithm was employed to indicate the goodness of fit of the model and illustrated in a forest plot with a confidence interval (CI) of 95% and a funnel plot depicting the odds ratio in each crop type. The algorithm used to determine generalized likelihood uncertainty was as follows:

$$L({\theta }_i❘O_v)={\text{N}}_{DS}(-\frac{\overline{\text{X}}{e}^2}{\min \overline{\text{X}}{e}^2}), (i=1, 2, 3,\dots ,\text{N})$$

Where ${\text{N}}_{DS}$ is the number of data subsets, $O_v$ is the observation vector fractionated by ($O_1$, $O_2$,…, $O_T$), $\overline{\text{X}}{e}^2$ is the mean squared error, and $\min \overline{\text{X}}{e}^2$ is the minimum mean squared error calculated by

$$\min \overline{\text{X}}{e}_i^2=\frac{1}{T}\sum _{t=1}^T{(P_t(i)-O_t)}^2$$

Where $T$ is the frequency of a data point, and $P_t$ and $O_t$ are the lowest and the highest values for $T$, respectively. The following equation was used to calculate the likelihood:

$$L_w({\theta }_i)=\frac{L({\theta }_i❘O)}{{\sum }_{i-1}^{N}L({\theta }_i❘O)}$$

$$\sum _{i=1}^N{L}_w({\theta }_i)=1$$

Where $L_w$ is the likelihood weight, with a collective sum of 1 at the likelihood scale for a data point to be acceptable for downstream analysis. Therefore, the certainty of the conclusions and the reliability of the datasets were ensured by double screening the parameters.

Simulation and cropping area analysis

An open-scale 3D airflow simulation was performed using the Ansys Fluent v2023 R2 (Academic License, Guangdong Academy of Agricultural Sciences) on a horizontal plane with a single plant, a single row of plants, and a double row of plants. The model was assisted with a surface-to-surface (S2S) add-in to precisely estimate air surface mechanics. The incident air speed was adjusted to a constant speed of 14 m/s with an ambient temperature of 25°C for 8 s due to previous scientific reports about the crop-damaging potential of air at this speed (Cleugh, 1998; Campi et al., 2009). The influence area of perturbed air was calibrated with the diameter of the interfering plant, and the wind direction after the plant perturbation was marked in the resultant cropping area plot. The 20-r spacing recommendation for windbreaks was derived from 3D computational fluid dynamics (CFD) simulations performed in Ansys Fluent. The model incorporated a surface-to-surface radiation add-in and applied boundary layer equations under a constant 14 m/s wind to observe airflow dynamics. The 20-r interrow distance was the optimal spacing at which the wind velocity consistently dropped below the 4 m/s damage threshold across the protected area. Considering the mass conservation equation

$$\frac{\partial {\overline{\text{u}}}_i}{\partial {\text{x}}_i}=0$$

where ${\text{x}}_i$ denotes the Cartesian coordinates and ${\overline{\text{u}}}_i$ represents the components of mean velocity. Therefore, the overall momentum equation for incident air was

$${\overline{\text{u}}}_j\frac{\partial {\overline{\text{u}}}_i}{\partial {\text{x}}_j}=-\frac{1}{p}\frac{\partial \overline{\text{p}}}{\partial {\text{x}}_i}+\frac{{\partial }^2{\overline{\text{u}}}_i}{\partial {\text{x}}_j^2}-\frac{\partial }{\partial {\text{x}}_j}(\overline{{\text{u}}_i^{\text{'}}{\text{u}}_j^{\text{'}}})+S_i$$

A specific momentum sink (volumetric) was additionally incorporated in the momentum equation to remove the error from the resistance values

$$S_i=-C_i\frac{1}{2}V{\overline{\text{u}}}_i$$

The stress created by the incident air was modeled by the equation:

$$-\overline{{\text{u}}_i^{\text{'}}{\text{u}}_j^{\text{'}}}=v_t(\frac{\partial {\overline{\text{u}}}_i}{\partial {\text{x}}_j}+\frac{\partial {\overline{\text{u}}}_j}{\partial {\text{x}}_i})-\frac{2}{3}k{\text{δ}}_{ij}$$

A logarithmic wind profile equation was applied to estimate the windbreak effectiveness, based on the incident air momentum, to calculate the safe distance for cultivation.

$$T=T_0+(T_1-V_0)\times \frac{In(\frac{d}{d_0})}{In(\frac{d_1}{d_0})}$$

Where $T$ is the wind momentum at an open area or a specific distance from the windbreak, $T_0$ is the wind momentum at the striking point on windbreak, $T_1$ is the wind momentum at a reference distance, $d$ is the distance from the windbreak where you want to calculate the wind speed, $d_0$ is the distance at which the wind could cause a momentum of $T_0$, and $d_1$ is the reference distance at which the wind momentum retards to $T_1$This equation assumed a logarithmic wind profile under the approximation of all the other interacting factors being constant, e.g., windbreak porosity and height. However, the threshold level below which the wind speed was considered safe was 4 m/s for maize (Wen et al., 2019), wheat (Niu et al., 2016), and most of the agricultural crops (Martinez-Vazquez, 2016). The distance from the windbreak position was considered safe, at which the wind speed drops below the threshold for at least half the time observed. The area was considered a windbreak-effective area suitable for crop cultivation, while the entire analysis was termed the safe area analysis.

Statistical analysis

Statistical analyses provide more details of the analysis procedures, focusing on results presented in tables and graphs. Phenological shifts were quantified based on changes in phenophase durations (emergence to maturity) using the PFM, calibrated for each crop group. The study did not use generalized additive models (GAMs) or time-series decomposition methods, as the aim was to evaluate interspecies phenological stability under extreme environments rather than modeling non-linear trends over time. Growing degree days (GDD) were not explicitly calculated; instead, shifts in phase timing and duration under climate stress were the primary focus. The yield-related results were analyzed based on the significant yield differences determined by Tukey’s test exercised in DSAASTAT (Onofri, Italy). The simulated airflow observations were validated using the discrete model validity analysis described by Rosenfeld and coworkers (Rosenfeld et al., 2010). The comparison of the phenology shift was performed according to Ahmad and Liu (2022).

Results

Time-scale vulnerability analysis

Considering the vulnerability of various crop types against different types of climate extremes revealed interesting facts about crop vulnerability throughout a crop’s life. Most of the crops (i.e., cereals, oilseed, peas, and nuts) showed maximum vulnerability variation toward high precipitation. Their vulnerability increased from emergence to the vegetative stage and then again reduced up to maturity. A declining trend was observed in the vulnerability index of most of the crops toward extreme winds, except oilseed and tubers. Drought showed a growing vulnerability index toward all crops (except tubers) throughout the growing period. Tubers could tolerate most of the climate extremes with the least variation in their vulnerability index, showing an almost stable trend in the analysis. However, the vulnerability of peas and nuts was the most dynamic in all climates throughout their cultivation period (Figure 1).

Figure 1

Figure 1. Vulnerability analysis of crop groups toward extreme environmental events represented in bar graphs. Time-course vulnerability variance of crop plants to the environment from emergence to maturity is represented in line graphs. Environmental subclasses were defined as extreme high temperature (HT), extreme low temperature (LT), extreme high precipitation (HP), extreme low precipitation (LP), wind (W), and drought (D).

Crops’ response to extreme climate events

The results proved that cereal crops showed the highest sensitivity toward extreme climates. Its unison NDVI was the minimum among all the seven crop groups. Less sensitivity toward extreme climates was recorded even for oilseed. Nuts and tubers recorded maximum tolerance toward extreme climates. Furthermore, extreme temperatures showed the maximum impact variation among agricultural crops. It could reduce the NDVI of cereals by approximately 48.7% at the CEI value of 1.2. It also influenced oilseed, vegetables, and beans and reduced their NDVI to approximately 22.3%, 19.8%, and 3.8%, respectively. Extreme precipitation has impacted all seven crop types, significantly reducing their NDVI. However, a reduction in its impact was recorded from cereals to tubers, except in peas. Peas were less influenced by extreme precipitation than tubers. The wind appeared as the most influential factor in extreme climates. It cut down ≥50% of the NDVI of grains and oilseed. However, more than 25% of NDVI reduction was persistently noticed in all the other crop types facing extreme winds (Figure 2).

Figure 2

Figure 2. NDVI-based growth assessment of different crop groups under extreme climate extreme indices, i.e., temperature (T), precipitation (P), and wind (W).

Evaluation of the cropping patterns

Observations revealed that crops cultivated in monocropping patterns were more vulnerable to extreme climates and readily lost their NDVI with increasing climate extreme index. Even food security could be seriously affected at the collective CEI value of 5.0. Only 20% of the cultivated plants could survive at a CEI value of 10.0. Intercropping performed a little better than monocropping. However, a significant difference could be observed in the crop rotation, in which an NDVI value of −0.6 was recorded at the highest CEI. Mixed cropping produced better results than the other three cropping patterns without trees, and the cultivated plants showed an NDVI value of 0.13 at 12.5 of CEI. In the case of cropping patterns incorporated with trees, agroforestry and alley cropping were the best cropping patterns for which NDVI values of 0.4 and 0.22, respectively, were recorded at the maximum climate extreme of 15.0 (Figure 3). While phenological changes were linked to cropping system resilience, the study did not model land allocation changes using GIS or remote sensing tools. However, spatial vulnerability was inferred through aggregated CEI maps and phenology deviation zones.

Figure 3

Figure 3. Assessment of the different cropping patterns against the hierarchical increase of extreme climatic index (CEI). Analysis of mixed cropping with respect to agroforestry (A). Crop cultivation combined with tree cropping under extreme climate conditions is represented in CEI (B).

Agricultural production analysis

South America was the most prominent continent in terms of cereal production, and the most important finding was that cereal crops were the most vulnerable to extreme climates. Europe and Africa followed in terms of their share of cereals in agricultural output. North America depended the least on cereals, and vegetables took the largest share in the agricultural production of Asian countries (22.8%). A significant part of Asia and Australia’s agricultural production was represented by tubers, the most resilient crop type to extreme climate events. The three major crop types in Australia were tubers, nuts, and oilseed (Supplementary Figure S2). North American and African countries were the least tuber-producing regions. However, African countries had been abundantly producing nuts, even more than any other continent in the world, which was the second most tolerant crop type against extreme climate events (Figure 4).

Figure 4

Figure 4. Percentage yield anomaly of different crop types in different continents of the world tagged with the risk levels of regional food security to extreme environmental events.

Phenological stability analysis

Phenological stability analysis proved that the crop with the highest sensitivity toward extreme climates also showed the highest phenological deviations, e.g., cereals. However, the stability index showed a gradual improvement in each successive phenophase, starting from emergence to the vegetative stage, reproductive stage, and maturity. Nuts and tubers recorded the least phenophase shift change among all crop types. However, their phenological stability was reduced with the passing phenophases. The maturity stage of nuts and tubers recorded more phenology shifts (2.1 and 1.6 days, respectively) than their emergence stage (1.5 and 1.3, respectively). Peas were the third most stable crop type in terms of phenology shift due to extreme climates (Figure 5). High temperature expanded the total period of seed emergence with a non-significant difference in the phenophase shift. However, earlier phenology of the three phenophases (i.e., vegetative, reproductive, and maturity) was evident. Low temperature caused induced delayed phenology in all the tested phenophases. Drought and wind caused a delayed emergence, but they caused the early occurrence of the other three phenophases (Supplementary Figure S3).

Figure 5

Figure 5. Phenological stability comparison of different crop groups against the climate extreme index (CEI) gradient. The Y-axis represents phenology shift (days), whereas CEI is plotted along the X-axis in each category plot.

Cropping area analysis

It was evident in the airflow simulations that the trees cultivated as windbreaks can effectively save crops from extreme winds. However, the cultivation areas under the tree cover, opposite to the wind direction, were most affected by the extreme winds. The air coming from the opposite direction was compressed toward the ground, and that compression elevated the air mechanics up to 3-r distance from the tree trunk. However, due to cyclonic movement, its impact was reduced at the 4-r distance, from where it again started an uprising. Similarly, the air passing above the tree canopy bounced upward with rotational kinetics, and then its impact area progressed again toward the ground. However, a single tree could block the air with a severe impact approximately 10-r distance. The vertical view of the air simulation proved that the rotational and mechanics of the air passing from the sides of the tree were highly dynamic. Although airflow was blocked, the compressions of the blocked air brought perturbed cyclonic flows of air currents. Therefore, a single tree can only produce a significant benefit against extreme wind only up to the 2-r distance. Therefore, the trees planted in a row were analyzed, and the vertical view of simulated airflow showed that the cyclonic perturbations of air flows were effective around the 2-d (4-r) distance. Then, two sides of the airflow started separating, making an area safe for plant cultivation. This wind-protected zone was extended up to a 10-d distance because the airflow was again merged from both sides at the 11-d zone (Figure 6). An analytical framework (Supplementary Figure S1B) has been included to systematically connect the three phases of the study: crop sensitivity analysis (phase 1), cropping pattern evaluation (phase 2), and spatial planting recommendations (phase 3). This logic model helps trace how vulnerability findings inform the selection of resilient cropping systems and tree-based windbreak strategies. The airflow simulation of double-row windbreak trees proved to have no significant benefit with respect to increasing the cultivation area (Supplementary Figure S4).

Figure 6

Figure 6. Plotting area analysis for a single tree in the horizontal dimension (A) and in the vertical dimension (B) by following the patterns in airflow simulations. Plotting area analysis of a single row of plants generated from the airflow simulation (C).

Discussion

Several initiatives have commenced globally to increase crop production, including the 2030 Agenda for Sustainable Development, the Green Deal, and Good Agricultural Practices (GAPs) (FAO, 2006). Following the agenda, a new ecological cropping (EC) strategy will be promoted to grow crops under natural eco-cycles to reduce synthetic inputs. Agroforestry is a bit closer to EC; however, it may require an extraordinary spatial extension of cultivated land in the initial stages of the EC program. EC with low fertilizer and pesticide inputs would require decades before getting established. Therefore, an extensively exercised crop extension program is recommended along with agroforestry adaptations.

Choosing the right crop as an agricultural investment helps anticipate extreme climate impacts and attain food security. It is considered as one of the three major crop cultivation regime factors. Previously, some researchers focused on the types of nutritional contents, e.g., proteinaceous foods and starchy foods, to estimate the suitability of the crops in projected environments (Vågsholm et al., 2020). However, the current study has mainly focused on crop types and enlisted them based on their vulnerability to climate extremes. The results of the analyses prove that the cultivation of tubers and root crops strengthened crop resilience against climate extremes. Tuber and root crops, such as cassava (Manihot esculenta), sweet potato (Ipomoea batatas), yam (Dioscorea spp.), taro (Colocasia esculenta), and potatoes (Solanum tuberosum), play a key role in enhancing the resilience of farming systems against extreme climate events (Nanbol and Namo, 2019; Ahmad et al., 2022b). These crops are particularly valued in tropical and subtropical agroecosystems for their capacity to withstand prolonged droughts, poor soil fertility, and variable rainfall patterns (Ahmad et al., 2019; Khan et al., 2019; More et al., 2019). Many tuber crops are deep-rooted, allowing them to access subsoil moisture during dry periods, thereby maintaining productivity even under water-stressed conditions. Additionally, their growth beneath the soil surface shields them from direct exposure to climatic extremes such as heatwaves, floods, and strong winds. Tuber crops also offer flexibility in harvest time, allowing farmers to leave them in the ground as a living food reserve until needed (More et al., 2019; Tariq et al., 2021). Moreover, many are compatible with cover cropping systems, which can reduce soil erosion, suppress weeds, and improve organic matter, contributing to long-term soil conservation and moisture retention. Their ability to thrive in marginal or degraded lands makes them a critical component of climate-resilient, low-input agriculture.

In contrast, the resilience of other crop groups varies. Legumes such as beans and peas are moderately resilient due to their nitrogen-fixing ability, which improves soil health and reduces fertilizer dependency (Bashir et al., 2013; Akram et al., 2020). Some legume varieties also exhibit drought tolerance and contribute to crop diversification (Ahmad et al., 2013; Yousaf et al., 2015; Alam et al., 2024). However, they are often sensitive to waterlogging and extreme heat during flowering, which can severely reduce yields. Oilseed crops like sunflower and canola offer some drought tolerance (particularly sunflower) but are generally vulnerable to temperature extremes and pests under climate stress (Khan et al., 2015). Vegetables are typically high-value crops with shallow root systems and high water requirements (Akram et al., 2014a; Yasin et al., 2018), making them highly vulnerable to drought, heat stress, and erratic rainfall (Ahmed et al., 2017; Shahzadi et al., 2022b). However, under protected agriculture or integrated with efficient irrigation and shade structures, their resilience can be enhanced (Ahmad et al., 2014; Bashir et al., 2016; Akram et al., 2021). Cereals such as millets, sorghum, and maize show variable resilience: millets and sorghum are notable for their drought resistance and low-input needs, making them suitable for arid regions, while rice and wheat are more sensitive to temperature and water stress (Mubeen et al., 2022; Shahzadi et al., 2022a; Alam et al., 2024). Lastly, tree nuts (e.g., almonds, walnuts) have long lifespans and deep roots, which may buffer them against short-term drought, but they are vulnerable to long-term water scarcity and increased pest pressure under changing climates.

The findings directly affect the areas plagued by disastrous climates, e.g., tropical storms and typhoons, for example South Asia (India, Pakistan, Bangladesh), Sub-Saharan Africa (Sahel and Horn of Africa), Southeast Asia (Philippines, Indonesia, Vietnam), Caribbean and Central America (Haiti, Puerto Rico, Honduras), and Western United States (California, Arizona), etc. Most often, farmer communities are not aware of these tools to tolerate extreme climate shocks (Sohail et al., 2022), and even if they are aware, they do not prefer to adopt them because of social requirements and palatability preferences (Arbuckle et al., 2015; Ahmad et al., 2021; Yasin et al., 2022). However, it is one of the most important tools to avoid food security threats, and it must be recognized and exploited in areas vulnerable to extreme climate events. Currently, there are large geographic areas under the continuous threat of typhoons and storms every year. The Philippines faces more than 20 typhoons each year (Santos, 2021). Therefore, the technique has real implications for the Philippines and other identical areas, where tubers are grown under kitchen gardening, and domestic cultivations may provide people with nutritional and food security. Furthermore, a variety of tuber crops (e.g., potatoes and sweet potatoes) can be selected based on the regional environments and edaphic conditions.

Cropping patterns belong to the regional agriculture policy and profoundly impact crop cultivation operations (Li et al., 2018). Changing the cropping pattern means amending the agriculture policy to better adapt to modern resource utilization patterns (Chouchane et al., 2020; Fatima et al., 2020; Raza et al., 2019). Climate change has exerted more pressure on redesigning agriculture policies, thus synchronizing the cropping patterns (More et al., 2019; Nanbol and Namo, 2019; Ahmad et al., 2020). Improving the cropping pattern is also a feasible and cost-effective technique to cope with disastrous climates and related obstacles. Therefore, the current study is critically important as it highlights a comparison of different cropping patterns under extreme climate events. Furthermore, the production behavior of different crops cultivated under different cropping patterns and tolerating climate extremes adds novelty to the study.

The study is a step forward in the mitigation of climatic shocks. However, there could be several other ways to achieve the food security goal under severe climates. There must be interconnected research investigations with an immediate communication system to disseminate information on real useful techniques of climate mitigation (Dwivedi et al., 2022). Timely research in the agriculture sector and efficient awareness/communication activities at the national and global scales can update ground practitioners with the recent advancements in the field.

Previously, researchers have recorded that the major food security issues were related to winter wheat, maize, and soybean in the United States (Xia et al., 2022). However, rice and maize are the most vulnerable crops in Africa (Derbile et al., 2022). According to the findings of the current study, wheat crop in the entire of Europe is highly vulnerable to climate extremes, and Asian countries may face food security issues due to soybean. The current study is a more comprehensive representation of the vulnerability patterns of crops in the entire world. It was found that crops with belowground edible parts were the most climate-resilient, and cereals and oilseeds were the most vulnerable to climate extremes. Furthermore, drought and temperature were powerful factors retarding crop yields. Here, it is suggested that the governments and other authorities make some efforts to reduce the vulnerability index of these crops (cereals and oilseed) in the respective areas before working on agri-extension programs.

Monocropping is one of the initial cropping patterns with certain benefits (Mahlayeye et al., 2022). However, it is not encouraged to be practiced in the same area for longer periodic cycles due to the increased risks of pathogens. Later on, crop rotation and intercropping were also supported by the concluding findings of several scientific investigations (Akram et al., 2014b; Jiang et al., 2021; Kumar et al., 2021). However, a few people prefer mixed cropping due to its resistance to climate change. In a recent study, Manners et al. recommended integrating banana crops into the traditional cultivation systems of Africa (Manners et al., 2021). According to the study, the climate in Africa is becoming hotter and more humid, and more frequent climate extreme events are expected in the near future. Therefore, they recommended an RT&B (roots, tuber, and banana) cultivation pattern for African countries. The current study stretches the findings at the global scale and recommends cultivating climate-resilient tubers under mixed cropping patterns. Most importantly, the cultivation of resilient climate crops and the selection of climate-mitigating cropping patterns are the preliminary steps to tackling extreme climates of the future. However, precise future climates are unknown, so we cannot predict the response of future crop cultivars. Therefore, continuous research and fact-based recommendations are essential for successful agriculture operations. Otherwise, without adopting the proactive approach, a devastating impact of climate change on the livelihoods of growers can be expected.

Although implanting windbreaks is the most common method of crop cultivation, the current study did not record satisfactory results about the efficiency of windbreaks (Smith et al., 2021). It performed better than the other methods, but it was the least effective method in tree-incorporated cropping patterns. Forest farming proved better than windbreaks in terms of climate resilience. However, optimized cropping patterns cannot be adopted at a large scale due to difficult management practices. Therefore, growers rely on alley cultivation of crops, which also supports livestock production, but the specific size of the agriculture tools and the strict timetable to access the land for management activities are the limiting factors for alley cropping. Agroforestry or wildcrafting is the understory cultivation of plants in the forest ecosystem (Trozzo et al., 2021). However, these plants are mainly food-producing native plants or fruit-bearing trees. Besides its limitations concerning the cultivation of staple food crops, the method performs the best with respect to the cultivation of nuts, tubers, medicinal plants, and ornamental herbs. The current investigation has proven that this cropping pattern is the most resistant pattern against climate shocks. It can be recommended as the method of survival for the human population because it can tolerate the most extreme climates, except wildfires, floods, volcanic eruptions, etc. However, its resistance to climatic disasters will be amplified if coupled with the cultivation of belowground edible crops (i.e., tubers and nuts). Moreover, it is also very beneficial for farmers who can continuously earn money both from the trees and the understory crop by scheduling harvest time.

Generally, the selection and the share of crops in foods depend on taste and palatability preferences (Anguah et al., 2017). However, different crop types have differential vulnerability toward climate extremes. The current study proved that the taste preferences could also put humans at food security risks. The agriculture system in South America has a greater share of cereals in their annual crop productions in comparison to the other continents. Similarly, Oceania is the most prominent continent in growing oilseed crops, which are the second most vulnerable crop type. According to the general rule, the continents significantly depending on the crops with less climate tolerance should be at greater risk to food security. However, Oceania also grew a comparable amount of tubers besides oilseed, which reduced the food security risk in the area. The agriculture sector of African countries mainly depends on the production of nuts, a crop type with high climate resilience.

Comparison of the phenological stability of different crop types is the prime novel part of the study. Phenology is the key to designing interconnected relationships in an ecosystem and food chain (Thakur, 2020). Growing a penologically stable crop endows stability to the agri-ecosystem. Nuts, tubers, peas, and beans were the crops with significant resistance toward phenophase shift. The results also illustrated that low temperature and drought shifted all the tested phenophases either earlier or delayed. Although the phenology shift is strongly backed by physiological reasoning, the current study only focused on the phenological responses of crop types. Separate investigations are required for deeper insights into the physiological grounds.

The study used artificial intelligence technology to produce airflow simulations and their aftermaths. The technique compared different ground conditions and the air impact area for a precisely designed agriculture policy. Previously, several researchers have recommended the integration of windbreaks in conventional cropping patterns. A recent study also stressed the integration of windbreak in arable fields (Thevs et al., 2021). They claimed that windbreaks could significantly save water due to reduced evapotranspiration in the fields by integrating wind barriers at 200 m apart. However, the type and size of the windbreaks were overemphasized in the study. The current study correlated the efficiency of the windbreak with the radius of the plants used to construct it, and it is the more realistic approach to integrate trees in the existing cropping patterns. The study also showed a clear passage of air airflow, based on which it can be recommended that the windbreak plants should be selected with a lower canopy height so that the volume of the air passing beneath the canopy should be limited. Furthermore, the area under the tree canopy, opposite the wind direction, is under the maximum wind impact. Therefore, this must be avoided for cropping purposes or should be tailored with some other measures to mitigate wind impact. Furthermore, the study also provided a scientific basis to avoid integrating double or multiple tree rows as a windbreak because there is no significant benefit of planting multiple tree rows, and it will only increase the costs of agri-operations.

Conclusion

Optimization of the cultivation pattern is a low-cost strategy to anticipate disastrous climates, including typhoons, floods, etc. The processed climate data of 1979–2020 proved that tubers and nuts were the most resilient crops to extreme climate events. In contrast, cereals and oilseed were the most vulnerable crop types to extreme climates. Phenological stability was higher in nuts and tubers, which also exhibited the highest phenology shift resistance. Agroforestry was limned as the best cultivation pattern with multiple advantages over alley cropping, forest farming, and windbreaks. Incorporating windbreak plants in the perpendicular single-row formation with an interrow distance of 20 r could be the best strategy to avoid extreme winds and their disastrous effects. Overall, the study has global implications for agricultural policies and can help in achieving food security.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

Ethics and consent do not apply to the study because there are no human or animal tissues involved.

Author contributions

AA: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. IS: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. QA: Resources, Software, Validation, Writing – original draft, Writing – review & editing. MU: Formal Analysis, Software, Validation, Writing – original draft, Writing – review & editing. NY: Formal Analysis, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing. WA: Data curation, Investigation, Software, Visualization, Writing – original draft, Writing – review & editing. TW: Data curation, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. WK: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing. AAS: Formal Analysis, Investigation, Validation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. We thank the United Arab Emirates University for providing a postdoctoral grant on climate action (#12S140).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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The author(s) declare that no Generative AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fagro.2025.1672935/full#supplementary-material

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