Effect of climate change adaptation options on maize yield across different agro-climatic zones in South Asia: A meta-analysis

doi:10.21203/rs.3.rs-6055958/v1

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Effect of climate change adaptation options on maize yield across different agro-climatic zones in South Asia: A meta-analysis

https://doi.org/10.21203/rs.3.rs-6055958/v1

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Maize (Zea mays L.), despite being a crucial and versatile crop, faces up to 30% yield loss due to climate-induced hazards such as heat and drought. To mitigate climate risks, various adaptation strategies has been suggested. However, the effectiveness of these strategies may vary significantly across different agro-climatic zones (ACZ), depending on the local conditions, making adoption decisions challenging. This study synthesises and evaluates the linkage of regional climatic hazards to potential adaptation options, and assess their suitability across different ACZs, soils and seasons in South Asia (SA). Additionally, we strengthen our work by using local literature from SA countries to introduce granularity and enhance the contextual relevance of our findings. Meta-analysis involving subgroup analysis and meta-regression was conducted to capture the influence of ACZ, soil textures, and seasonal conditions (rainfall and temperature) on yield benefits. Among 1114 observations reviewed for meta-analysis, 62% reported a positive yield response. In-situ moisture conservation, nutrient management and zero tillage showed mean yield benefits of 6.8%, 6.2% and 4.3%, respectively, over conventional practices across SA. ACZ significantly influenced the performance of the adaptation option, with central-western zone and north-eastern plain zone showing greatest yield benefits. Fine-textured soils had a significant positive impact of adaptation options in both wet and dry seasons while coarse-textured soils had a notable positive effect only in dry season. Performance of adaptation options were strongly influenced by rainfall and temperature, underscoring the need for region-specific technologies. Our findings improve the understanding of suitability and effectiveness of adaptation options across different regions, soils and climate, thereby supporting selection of appropriate adaptation options for greater benefits. We conclude by emphasizing the need for localized adaptation options that addresses the regional climatic risks and are productive in local soil and season to enhance maize resilience in SA.

Maize

South Asia

Adaptation options

Agro climatic zones

Yield effect size

Agriculture is the backbone of South Asia's economy, providing livelihoods for over 60% of the population (Andersson and Kugbega 2024). However, this critical sector is increasingly threatened by climate change, dense populations, and scarce resources for adaptation (Singh et al. 2021), which poses serious risks to both food security and economic stability across the region. According to the Intergovernmental Panel on Climate Change (IPCC 2021), South Asia is projected to experience rising temperatures and erratic rainfall patterns, leading to significant reductions in agricultural productivity. By 2050, overall, a mean yield reduction of 8% for cereal crops was identified in both Africa and S. Asia (Knox et al. 2012). The vast agricultural regions of India, Pakistan, and Bangladesh are particularly vulnerable to these climate vagaries, making it even more challenging to feed a population expected to reach 1.8 billion by mid-21st century (FAO 2021).

Among the key crops in the region, maize (Zea mays L.) plays a vital role in food security and the economy (Fig. 1). Maize is cultivated in approximately 11.84 million ha (m ha) in South Asia and 9.0 m ha is in India, supporting the livelihoods of over 20 million farmers (FAO 2023). As a staple food, a crucial feedstock for livestock, and an industrial raw material, maize is fundamental to rural economies and food systems (Dou et al. 2024). Its adaptability to diverse agro ecological zones highlights its importance, particularly in the context of growing climate uncertainties (Singh et al. 2023).

Despite its versatility, maize production faces substantial risks due to climate change. Rising temperatures can induce heat stress during critical growth stages such as flowering and grain filling, reducing kernel development and overall yield (Yuan et al. 2022). Projections suggest maize yields may decline by 10–30% due to increasing temperatures and altered precipitation patterns (FAO 2019, Tesfaye et al. 2017; Onyekwelu and Sharda 2024 ; Xu et al. 2024a). Moreover, erratic rainfall intensifies the risks of both droughts and floods, further complicating crop management and reducing yields. Du and Xiong (2024) highlighted that drought events alone could lead to yield losses of up to 50% in some regions, emphasizing the urgent need for effective adaptation measures. To mitigate the risks posed by climate change, farmers are increasingly adopting various adaptation options. These include climate-resilient crop varieties, adjustments in planting dates, improved irrigation techniques, and integrated pest and nutrient management (Reddy and Reddy 2015). However, the effectiveness of these options may vary significantly across different agro-climatic zones, depending on the specific climate risks and local conditions. These variations necessitate localized research to determine which adaptation options are being used in each region and to what extent they are effective in mitigating climate hazard. An analysis of farmers' adaptation practices in Asia identified key themes and recommended a need for more qualitative studies and standardized review methods for climate change adaptation (Shaffril et al. 2018). Climate change is expected to reduce crop yields and greater yield decreases in tropical regions in the second half of the century, however, adaptation options can improve yields, especially for wheat and rice (Challinor et al. 2014). Abbas et al. (2023) have simulated the impact of climate change on maize yield in Pakistan predicting the yield loss up to 33% in mid-century and use of different adaptation options could reduce the yield loss. Al Mamun et al. (2024) found that climate change in coastal Bangladesh exacerbated drought conditions and irrigation challenges, prompting farmers to adopt technologies such as using organic fertilizers and drought-resistant crops. Kumara et al. (2020) conducted a meta-analysis on the benefits of Conservation Agriculture (CA) in South Asia, revealing that CA significantly enhanced carbon sequestration (+ 16.30%) compared to conventional tillage and reduced water usage. The study advocates for scaling up CA as a strategy for climate change adaptation and mitigation. In India, Kumara et al. (2023) conducted a meta-analysis on sustainable agricultural practices suitable for carbon sequestration and found that biochar significantly enhanced sequestration. Steward et al. (2018) found that maize-based conservation agriculture practice improved yield performance under drought and heat stress, especially during drier seasons, thus enhancing the adaptive capacity of maize production to climate stress, making it a promising strategy for mitigating climate impacts. Despite the growing body of research on climate adaptation, there is a notable gap in understanding the location specific adaptation strategies that can be employed in maize production in the South Asian Region. While other crops have been extensively studied, there is limited knowledge about how well current adaptation options perform in relation to the unique climate challenges in different agroclimatic zones (ACZ). Although there are a few global studies on addressing the impact of climate change on cereal crops, however, these studies did not consider subnational and local information related to South Asia. Moreover, commodity specific studies are also lacking in the current literature (Aryal et al. 2020; Grigorieva et al. 2023).

This research aims to address existing gaps by analysing climate adaptation literature to provide a comprehensive overview of climatic hazard and yield benefits in maize of various adaptation options across South Asia. This study uniquely incorporates local literature, enhancing the contextual relevance of findings and addressing gaps in localized agricultural practices. Through a systematic literature review that integrates bibliometric analysis and meta-analysis, this study synthesizes South Asian literature and disaggregates the findings to provide insights into the effectiveness of adaptation technologies across different ACZs in different maize growing seasons.

2.1 Literature search and PRISMA framework

Systematic Literature Review (SLR) is increasingly being used in climate-change research, especially in vulnerability and risk analysis (Djalante et al. 2018). SLR is a methodical process used to collect, evaluate, and synthesize all relevant studies on a specific topic (Marzi et al. 2024). It follows a structured approach to ensure transparency, reduce bias, and provide a comprehensive overview of existing evidence (Okoli et al. 2015; Ofem et al, 2022). A systematic search was conducted to assess the effectiveness of various adaptation options to reduce the impact of climatic hazards on maize production in South Asia. This search utilized global databases such as Scopus, Web of Science, Google Scholar, as well as national and local databases and reports from India, Nepal, Bangladesh, and Sri Lanka. Relevant studies were identified using a comprehensive search string that incorporated multiple keywords related to maize, climatic hazards, yield changes, adaptation technologies, and geographical regions. In our research, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, alongside methodologies outlined by (Saja et al. 2019), to ensure a robust and thorough review of the existing literature to minimizes bias. The publications were screened based on specific inclusion and exclusion criteria, as shown in Fig. 2. Publications (44) that provided quantified data on maize yield by use of various adaptation option to climate change were included for meta-analysis. Included studies involved field experiments with both control and treatment groups, provided quantitative yield data, and clearly described the experimental conditions. Excluded studies were those without measurable outcomes, such as reviews, modeling, pot studies, or research based on farmers' perceptions (Fig. 2).

2.2 Bibliometric analysis

We used bibliometric analysis of published literature which involved use of quantitative metrics like citation counts, publication trends, and authorship data to evaluate research patterns (Ellegaard and Wallin 2015; Alsharif et al. 2020; Donthu et al. 2021) The bibliometric analysis included 1125 publications sourced from WoS, scopus, google scholar, and local South Asian literature, after removing duplicates and irrelevant studies. 1125 publications were screened, and 157 were selected for further review, focusing on adaptation options. This was performed to identify key trends and research hotspots in climate adaptation research for maize. Our study focused on trends in publication counts, climatic hazards, methodology used, and regional concentrations in different zones of South Asia (Supplementary file S1).

2.3 Meta-analysis

Meta-analysis combines results from multiple studies to provide a quantitative summary, enhancing understanding and statistical power while evaluating consistency and overall trends (Mosteller and Colditz 1996; Cleophas and Zwinderman 2017). Figure 3. depicts the locations of the studies selected for meta-analysis. The meta-analysis of the collected datasets was done using R software (R4.4.1) utilizing a suite of specialized packages (Balduzzi et al. 2019; Viechtbauer 2010; Wickham et al. 2019)) to evaluate the effectiveness of different adaptation options, heterogeneity, considering variations in experimental design, duration, and agro-ecological conditions. Sensitivity analysis was done to ensure result robustness, with outcomes reported as mean effect sizes, confidence intervals, and p-values.

2.3.1 Database

The dataset for the meta-analysis included 44 records, yielding 1,114 observations (Supplementary file S2). The data encompasses details on location, year, soil type, rainfall, temperature, adaptation options, and quantified yields for control and treatment groups, and the corresponding standard deviations. Environmental conditions were categorized by temperature (T1: 25–30°C, T2: <25°C, T3: >30°C) and rainfall (R1: 400–700 mm, R2: <400 mm, R3: >700 mm). The control for adaptation options, along with the number of observations, are summarized in the Table 1. The dataset was further grouped by soil types (S1 medium, S2 coarse and S3 fine) and agro-climatic zones (Central Western Zone (CWZ), North Eastern Plains Zone (NEPZ), Northern Hills Zone (NHZ), North Western Plains Zone (NWPZ), and Peninsular Zone (PZ) (AICRP, 2020, Köppen and Geiger 1930) to assess their effectiveness across various environmental conditions in South Asia.

Table 1

Details of adaptation technologies included in this study
Category	Adaptation option	Control	Number of observations (n-=1114 )
Ecological Intensification (EI)	Best management for nutrient, water, weed, tillage, hybrid/ stress tolerant variety based on local conditions	Local farmers practice	111
Irrigation management (I)	Drip/supplemental irrigation	Rain fed	15
Irrigation management (I)	Ridges and furrow	Check basin	15
Inter cropping (IC)	Maize + pulses/legume	Sole maize	8
Nutrient management (NM)	Green seeker/SSNM/STFR based N application/INM/ supplemental N	RDF /farmers practice	237
Nutrient management (NM)	Hormonal priming/nutrient priming	Hydro priming	237
In-situ moisture conservation (MC)	CT/ZT + Residue mulch/ hydrogel/in-situ moisture conservation	No moisture conservation practice	390
Zero tillage (ZT)	Zero/ reduced tillage	Conventional tillage	53
Multiple adaptation option (MA)	Combination of raised bed/ permanent bed, drip/ subsurface irrigation/residue mulch/INM/ zero/reduced tillage	Flatbed conventional tillage	265
Variety (V)	Improved/ drought tolerant variety	Local/traditional variety	35

2.3.2 Data extraction

The analysis utilized data, including treatment means (X̄), standard deviations (SD̄X), and sample sizes (n) derived from the experimental design (Rusinamhodzi et al. 2011). When standard error of the mean (SĒX) and coefficient of variation (CV%) were provided, they were converted to standard deviation (SD̄X) using Eqs. 1 and 2.

\(\:SD\:̄X\:=\:SĒX\:\times\:\:\surd\:n\:\:\:\)	\(\:Eq.1\)
\(\:SD\:̄X\:=\left(\right(X\:̄\times\:\:CV\%\left)\right)/100\)	\(\:Eq.2\)

In cases, where calculating SD̄X was not feasible, an approximation was made using the mean grain yield (X̄) as follows: SDˉX = 0.1×XˉSD̄X = 0.1 \times X̄SDˉX = 0.1×Xˉ (Luo et al., 2006). This data served as the basis for the meta-analysis performed in R software (Bosančić et al. 2018) employing the inverse variance method.

2.3.3 Effect size

To quantify the impact of various adaptation options on maize yield, we employed a standardized metric known as the response ratio (RR) (Yang et al., 2022 and Qin et al., 2021). This metric compares the yield under different adaptation technologies to that under conventional practices. To facilitate statistical analysis, we transformed the RR into its natural logarithm (LRR) Eq. 3.

\(\:LRR\:=\text{l}\text{n}\left(RR\right)=\text{l}\text{n}\left(\right(Yield\_(Adaptation\_Technology))/(Yield\_Conventional\:\left)\right)\) \(\:Eq.3\)

The LRR provides a standardized measure of effect size, allowing for comparisons across studies (Rodgers and Pustejovsky 2021). By using LRR for the effect size, we were able to standardize the metric, ensure consistent comparisons across studies with different units of measurement, facilitate statistical analysis, utilize standard statistical techniques for SLR, interpret the results, understand the magnitude and direction of the effects of various adaptation technologies on maize yield. To convert a log-transformed mean effect size to a percentage change, using Eq. 4

\(\:Percentage\:conversion\:=\:(e\_Mean\:Effect\:Size\:-1)\times\:\:100\) \(\:Eq.4\)

In meta-analysis, multiple observations from the same study or region introduce dependencies in the data. To address this, a multi-level model was employed, incorporating random effects for study IDs (to account for within-study variability) and regions (to capture between-study variability). This approach effectively manages hierarchical structures, ensuring robust estimation of the mean effect size while accurately partitioning variability across levels.

2.3.4 Heterogeneity and publication bias

Heterogeneity refers to the variability in effect sizes across studies, which may arise from differences in study design, populations, interventions, or other contextual factors (Borenstein et al. 2020). To assess heterogeneity in meta-analysis, methods like I² statistics and Q tests are commonly used to measure the degree of inconsistency in effect sizes (Borenstein et al. 2021). Heterogeneity in maize yield meta-analysis, was examined by the degree of inconsistency in effect sizes, identifying potential sources of variation.

To evaluate publication bias, we utilized several diagnostic methods. A funnel plot was generated to visually assess asymmetry, which suggested potential publication bias if present. Additionally, we performed Egger's regression test to statistically detect asymmetry, with significant results indicating possible bias (Furuya-Kanamori et al. 2018). For further confirmation, Begg's rank correlation test was also applied. Together, these methods provided a robust assessment of publication bias, enhancing the reliability of our findings (Nakagawa et al. 2022). Some of our observations showed evidence of publication bias, suggesting that selective reporting may have influenced certain results. To enhance the robustness of our analysis and remove outliers, we applied the trim-and-fill method (Kepes and Thomas 2018).

2.3.5 Meta-regression

To evaluate the effectiveness of various adaptation technologies for maize production under different agro-climatic conditions and hazard, a comprehensive meta-regression was conducted utilizing a mixed random-effect model (Lin and Chu 2020) specified as follows:

Y_i = β₀ + β₁SoilType + β₂T_value + β₃R_value + β₄AC_Zone + (1∣Study_iD)

In this model, the fixed effects comprised SoilType, T_value, a continuous variable indicating temperature levels; R_value, a continuous variable for annual rainfall; and AC_Zone, which categorized the agro-climate conditions. The inclusion of random effects for Study_iD allowed us to capture the variability across different studies.

3.1 Insights from bibliometric analysis

The South Asian region spans 11.84 m ha dedicated to maize, yielding 45.95 million tons annually (FAOSTAT 2022). The wet season (June – Oct/November) accounts for a significant 72.76% of maize production, while the dry season (November to April) contributes 27.24%. Mapping maize area production and publication revealed significant regional variability and bias across agro-climatic zones. Despite facing climate-related challenges like rainfall variability and heat stress, the PZ holds the largest area under maize (~ 31%) and contributes 34.3% of the region’s production, highlighting its critical role in maize production (Agricultural statistical database of India, Nepal, Bangladesh, Sri Lanka, Pakistan, FAOSTAT 2022). The NEPZ leads in research publications, reflecting a focus on addressing its unique challenges. However, bibliometric analysis indicates a noticeable gap in climate adaptation studies in PZ, with the majority of research concentrated in the NWPZ and NEPZ (Fig. 4 (a)).

3.1.1 Spatial distribution of adaptation options across agro-climatic zones

Mapping adaptation technologies and hazards across agro-climatic zones revealed significant regional variations in research publications across agro-climatic zones. A total of 1125 publications were used for the bibliometric analysis. The Fig. 4 (b) illustrates the connections between climatic stress factors, agro-climatic zones, and corresponding adaptation options. Each stress factor disproportionately affects specific zones, necessitating appropriate interventions. For instance, chilling stress is predominantly addressed in the NHZ and NWPZ using stress-tolerant varieties and plant growth regulators (PGRs), while drought and mild drought are mitigated across multiple zones in South Asia (CWZ, NEPZ, NHZ, NWPZ, and PZ) through adaptation options such as stress tolerant varieties, MC, irrigation and zero tillage/reduced tillage. Recently, EI has been used to mitigate the effect of multiple stress across the different zones.

3.1.2 Co-occurrence patterns of climatic hazards and adaptation technologies

The frequency of occurrence of various adaptation technologies in relation to climatic hazard, revealed significant research trends in the literature. Nutrient management, shift in sowing date, and drought-tolerant varieties emerged as the most frequently studied options, particularly for mitigating mild drought, heat and chilling stress, reflecting their prominence in addressing these challenges. Other technologies reported in recent years include ecological intensification, intercropping, in-situ moisture conservation and genotypic selection mainly for drought /heat stress resilience. The co-occurrence analysis shows a comprehensive mapping of adaptation options and their associations with climatic hazards, highlighting the frequency of these relationships in the literature. Drought emerged as the most frequently reported hazard, and was strongly linked with nutrient management, tolerant varieties, and genotypic selection. Rainfall variability was predominantly addressed through ecological intensification and in-situ water harvesting. Heat stress was commonly associated with drip irrigation and tolerant varieties. The results provide evidence from literature of the associations between climatic hazards and the respective adaptation options (Fig. 5).

3.2 Effect of adaptation options on maize yield

This study examined various agricultural adaptation practices and their mean effect size on maize yields from 44 publications. Results showed that 62% of observation reported positive yield responses, 15% negative, and 23% showed negligible change. IC and NM practices had the highest positive responses (100% and 70%, respectively). In contrast, Irrigation management exhibited the lowest positive responses (33%) and a high proportion of negligible change, suggesting mixed outcomes (Fig. 6, Supplementary file S2). Notably, IC technology need detailed research to collect more scientific evidence.

The analysis of adaptation options indicated a range of impacts on maize yield across the evaluated technologies. The result revealed that intercropping has the highest mean effect on maize equivalent yield, with an estimated increase of 45.6%, indicating strong certainty in its positive impact, however this is based on very limited number of observations from farmer’s field considered in present study. Following closely, a package of multiple adaptations showed a substantial mean effect of 44.6%, although this is accompanied by a wide range of impact ranging from 15–84.01%, reflecting considerable variability. Irrigation management also demonstrated significant improvements, with a mean increase of 13.2%. but its range of impact spans from 0.8–34.6%, suggesting yield gain in most cases. Additionally, variety selection yielded a mean increase of 10.3%, ranging from − 0.9–22.8%, indicating location specific uncertainty regarding its effectiveness. NM and in-situ MC showed significant positive impacts, with mean increases of 6.3% (range: 2.2–10.6%) and 6.8% (range: 3.1–10.6%), respectively. Zero Tillage/no till practices showed the lowest mean increase in maize yield at 4.2%, with a consistent impact ranging from 1.14–7.4%, suggesting a more modest but still positive effect (Fig. 7, supplementary file S2). The IC and I subgroups, which showed large effects but showed publication bias even after removing the bias using trim and fill techniques.

3.2.1 Soil texture

The study also evaluated the effect size of different adaptation options on maize yield in different soil types. Coarse and fine soil types consistently gave higher and more reliable yield improvements than medium textured soils. Among the various options, IC, NM, in-situ MC, and EI showed significant positive effects on maize yields, particularly in fine soil types. For instance, IC resulted in significant yield increases of approximately 64.06% in coarse soil type and 29.18% in fine texture soil. Similarly, NM achieved an estimated significant yield increase of 12.45% in fine soil. In-situ MC displayed improvements of 8.64% in fine textured soil and 5.72% in coarse textured soil. In contrast, medium soil showed negligible effects in various technologies, with yield estimates ranging from 0.23% for MA and 2.71% for MC, none of which were found to be statistically significant (Fig. 8, supplementary file S2). These results indicate that optimizing the choice of technology with soil type can lead to considerable enhancements in maize yield, emphasizing the importance of selecting appropriate practices for different soil conditions.

3.2.2 Agro-climatic zone

The data was also analysed under different agro climatic zones. Among the agro-climatic zones, the CWZ and NEPZ consistently exhibited greater and more reliable yield improvements compared to the NHZ and NWPZ. Technologies such as IC, NM, in-situ MC, MA, and variety selection demonstrated noteworthy positive effects on maize yields, particularly in the CWZ. For instance, IC resulted in significant increase in maize equivalent yield by approximately 25.56% in CWZ and 49.48% in NHZ. NM achieved an estimated yield increase of 8.44% in CWZ and 7.73% in NEPZ, indicating the importance of appropriate nutrient management for maximizing maize yields. In-situ MC also yielded significant improvements, with increases of 5.19% in CWZ and 12.20% in PZ (p ≤ 0.001), underscoring its importance. Conversely, NWPZ exhibited negligible effects from various technologies, with yield increase of 0.128% for zero tillage, indicating that practices commonly employed may not effectively address specific challenges (Fig. 9, supplementary file S2). These findings emphasized the necessity of optimizing the selection of adaptation options suited to specific environmental conditions in different agro-climatic zones for improving maize yield.

3.3 Effect of growing season on maize yield

The meta-analysis revealed significant positive effects of various adaptation options on maize yield in both wet and dry seasons (Fig. 10, supplementary file S1 & S2). In the wet season, the overall mean effect size for yield change was 7.73%, with notable contributions from specific practices such as irrigation management, which showed a mean effect size of 13.19%, and in-situ MC, with 45.63% increase. Conversely, the dry season exhibited an even higher overall mean effect size of 8.25%, primarily driven by NM, which demonstrated a substantial mean effect size of 10.92%. While zero tillage and MA proved more effective during the wet season, NM consistently enhanced yields across both the seasons. This comparative analysis underscores the importance of implementing season-specific adaptive technologies to optimize maize production.

3.3.1 Agro-climatic zones

The study also analysed the yield effect size in both the maize growing season in different agro climatic zones. The yield changes in maize varied across agro-climatic zones for both wet and dry seasons, with some notable seasonal differences (supplementary file S2). In the wet season, yield changes across zones were generally closer to the median, with fewer extreme outliers, indicating a more consistent performance. In contrast, the dry season showed more variability, especially in CWZ and NEPZ, and a greater spread of yield changes.

Among the different adaptation options, zero tillage resulted in yield increases of 9.65% in the CWZ and 4.79% in the NWPZ, while irrigation management showed exceptional efficacy in NWPZ with a 24.63% increase in wet season. IC performed well, yielding a 63.93% increase in maize equivalent yield in the NHZ and 29.12% in NWPZ. In-situ MC performed well in the PZ, achieving a 12.81% increase, and also had a smaller yet significant impact of 5.33% in NWPZ. MA options yielded a 12.04% increase in NWPZ and 5.51% in the NEPZ, while variety selection was particularly effective in NWPZ, achieving a 25.05% increase. In contrast, the dry season showed strong performance for zero/reduced tillage in CWZ with a substantial 20.91% increase, while NM yielded 9.74% in NEPZ and 18.73% in CWZ. MA options also showed a positive yield effect of 5.82% in NEPZ. These findings emphasize the importance of targeted interventions in specific zones, particularly CWZ and NEPZ, to enhance maize yield, highlighting the need for season and region-specific adaptation options due to the varying effectiveness across agro-climatic zones and seasons.

3.3.2 Soil texture

The study also analysed the seasonal performance of adaptation options in different soil types. In wet season, zero tillage in fine-textured soil showed a 7.80% increase, while EI recorded a 21.38% increase in fine-textured soil, whereas, 15.77% decrease in coarse-textured soil. The IC achieved a 16.91% increase in fine-textured soil, and Irrigation management yielded impressive gains of 49.48% in coarse-textured soil and 25.56% in fine-textured soil. Conversely, the dry season showed modest improvements, with zero/reduced tillage achieving a 6.89% increase in fine-textured soil, 9.67% increase in medium-textured soil and an 8.99% increase in coarse-textured soil, and In-situ MC exhibited increase of 7.25% in coarse-textured soil and 11.35% in fine-textured soil (Supplementary file S2).

3.4 Agro-climatic determinants of maize yield

The meta-regression analysis was used to assess the influence of various agro climatic variables on maize yield during the wet and dry seasons. The coefficients presented in Table 2 highlights the effects of each variable relative to its reference category. Fine-textured soils were found to have a significant positive impact on maize yields in both seasons, indicating enhanced productivity. In contrast, coarse-textured soils did not show a significant effect in the wet season but had a substantial positive influence in dry season. Temperature extremes negatively affected yields, with significant decreases observed when temperatures fell below the optimum level, reflected by negative estimates in both seasons. The analysis indicated that low rainfall had a detrimental impact in the wet season, while yields remained unaffected in dry. Furthermore, the analysis showed that maize yields had a significant positive effect in the NHZ, compared to the reference category (CWZ) during the wet season. The NWPZ exhibited a moderate increase in dry, while the PZ indicated a significant positive impact in wet but a negative influence in dry. Overall, these findings emphasize the importance of soil texture and agro climatic conditions in determining the effectiveness of adaptation technologies on maize productivity. The results suggest that implementing targeted management practices adapted to specific soil and climatic conditions could effectively enhance maize productivity across different agro climatic zones (Supplementary file S2).

Table 2

Parameter estimates from a mixed effect linear regression model comparing the maize yield potential of various adaptation technologies.
Explanatory variables		Estimate of wet season	SE	Estimate of dry season	SE
Intercept		-0.01	0.03	-0.07	0.11
Soil Type	Course textured	0.00	0.03	0.26***	0.04
Soil Type	Fine textured	0.12***	0.02	0.16**	0.05
Temperature	< Optimum	-0.12**	0.04	-0.15***	0.04
Temperature	> Optimum	0.03*	0.01	-	-
Rainfall	Low	-0.06*	0.03	0.05	0.09
Rainfall	High	0.01	0.01	0.17	0.18
Agro-climatic zones	NEPZ	0.04	0.03	0.10	0.08
	NHZ	0.21***	0.05	-	-
	NWPZ	0.03	0.03	0.22**	0.07
	PZ	0.09*	0.04	-0.08	0.10

***, **, and * denote significance levels of 1%, 5%, and 10%, respectively. The intercept incorporates the reference categories of the Medium textured soils, optimum temperature, suitable rainfall and CWZ (Supplementary file S2).

The bibliometric analysis highlighted a significant rise in research publications related to adaptation to climatic risk, particularly after 2007 with release of AR4 report of IPCC (2007), reflecting growing global efforts to address climate challenges. Experimentation remained the dominant methodology, though simulation/modelling studies are increasingly emerging as complementary approaches. Regionally, India leads with the highest number of publications, followed by Bangladesh and Pakistan, while countries like Nepal, Sri Lanka, Afghanistan, Bhutan, and the Maldives had limited publication. Drought is the most extensively studied hazard, followed by heat and floods. Among adaptation options, variety selection is the most studied, followed by agronomic practices, policy and insurance, soil health, and water management, with limited focus on other technologies. The analysis highlighted a growing focus on climate change adaptation technologies, particularly crop management practices. Among these, EI emerged as a new strategy, leveraging localized farming practices aligned to regional conditions to enhance maize productivity under climatic stress.

This research synthesized the effects of various adaptation technologies on maize yield across South Asia, focusing on the influence of agro-climatic zones, soil textures, and seasonal conditions. Our findings revealed that certain options offer significant improvements in maize yield under climatic stress, especially drought, heat, and rainfall variability. These include EI, V, I, IC, MC, MA, NM, and ZT, each targeting environmental challenges faced by maize in South Asia.

4.1 Effectiveness of Adaptation Options on Maize Yield

The observed pattern highlighted the varying effectiveness of adaptation options on maize yield, as reflected by their mean effect sizes and statistical significance. Among them, IC and I demonstrated the most substantial and significant impacts, whereas MA exhibited the smallest and least consistent effects. The ranking of adaptation technologies, based on their mean effect sizes, is as follows: IC (45.63%), I (13.19%), MC (6.77%), NM (6.29%), T (4.29%). Inter-cropping (IC) has emerged as a promising strategy for improving resource utilization, especially under high-temperature and water-stressed conditions. By creating microenvironments that buffer crops against climatic extremes, intercropping has been shown to enhance maize productivity (Lithourgidis et al. 2011). In contrast to mono-cropping, which benefits from reduced competition for resources, maize/legume intercropping faces competition that can limit yields. Studies by Pierre et al. 2022; Saudy et al. 2015 showed that IC offers ecological benefits, such as nitrogen-use rationalization and weed suppression, which can enhance sustainability despite the competition. Although the number of observations for IC were limited, the existing evidence highlights their potential to enhance yields and provide important ecosystem services. This underscores the promising role of this practice, particularly maize-legume intercropping, in contribution to ecological and sustainable crop production (Zhao et al. 2024). While further research is needed to expand the scope and validate these findings, the current evidence suggests significant potential for improving food security and environmental sustainability. Similarly, irrigation management, such as micro and supplemental irrigation, play a crucial role in mitigating drought and rainfall variability, ensuring sustained maize growth and yield (Gupta et al. 2023; Wang et al. 2023; Jat et al. 2019; Comas et al. 2019). However, despite IC and I demonstrating the highest effect sizes, their limited number of observations reduces their reliability for broader application. The meta-analysis also identified MC as another effective strategy, delivering a consistent and strong positive impact on maize yield. This aligns with previous research emphasizing the role of in-situ moisture conservation in mitigating stressors such as drought, mild drought, and rainfall variability through soil moisture retention and reduced evaporation (Demo and Asefa Bogale 2024; (Ngetich et al. 2014). Also evidenced by Ademe et al. (2018) and Chimdessa et al. (2019), in-situ MC practices effectively increased maize yield by reducing evapotranspiration, retaining soil moisture within the root zone and consistent water supply to the crop under drought or water-stressed conditions. NM and ZT also demonstrated notable improvements in maize yield although the effect was less compared to MC. Efficient NM, including balanced fertilization, enhances maize resilience by promoting root growth and nutrient uptake, essential for survival during drought conditions (Hussain et al. 2017 ; Aslam et al. 2012). Residue management and reduced tillage practices can reduce the impact of drought and rainfall variability, although their effectiveness is location-dependent and influenced by environmental conditions (Sarangi et al. 2020; Parihar et al. 2019). ZT broad bed planting with residue retention has been reported to enhance the grain yield (Saad et al. 2015, Ghosh et al. 2021), and carbon sequestration, contributing to improved maize productivity under drought and heat stress (Jayaraman et al. 2024, Bhatt et al. 2017). In contrast to our findings, previous studies have shown that ZT practices can negatively affect maize yield due to increased bulk density, reduced water intake, and higher weed infestations compared to conventional tillage (Mafongoya et al. 2015; Liu et al. 2022).

While the results of V, EI, and MA technologies were not significant in our study, their potential for mitigating climate-related stresses on maize production cannot be dismissed. These technologies have shown promise in other studies and are considered crucial components of broader technologies aimed at improving resilience and productivity in stress-prone regions. Despite the lack of significant findings in our analysis, the underlying mechanisms and the adaptability of these technologies under varying environmental conditions highlight their relevance for future research and implementation. Heat- and drought-tolerant maize varieties are equally important for mitigating drought-related stresses, ensuring sustained productivity in stress-prone regions. These varieties play a pivotal role in enhancing maize resilience under adverse climatic conditions, as corroborated by numerous studies (DeMeyer et al. 2024; Singh et al. 2024). On the other hand, EI and MA technologies showed comparatively less but positive improvement in maize yield. Recent literature has reported significant yield improvements with EI, averaging 48.6% over traditional methods and resilience under climatic stress by leveraging local farmers’ practices optimized to regional conditions (Rusere et al. 2019; Salakinkop et al. 2024; AICRP reports 2017–2020). Combining multiple adaptation options, such as irrigation, variety, and nutrient management, strengthens maize adaptability to climate-induced stresses (Rao et al. 2024 ; Grigorieva et al. 2023). However, our study found that MA had the smallest and least consistent impact. Shifting planting dates is often suggested as a strategy to adapt to heat stress or terminal heat impacts on maize, but such shifts have also been reported to induce chilling stress, resulting in yield loss under lower temperatures. This effect can be alleviated through the application of plant growth regulators (Waqas et al. 2017 ; Ahmad et al. 2014).

Our study confirms that different adaptation options vary in their effectiveness in improving maize yields under climatic stress. These findings are consistent with Challinor et al. (2014), who reported that crop-level adaptation technologies could increase simulated yields by 7–15%. Similarly, Maitra et al. (2021) and Al Mamun et al. (2024) emphasized the critical role of adaptation strategies, such as crop rotation and early-maturing varieties, in enhancing maize production efficiency amidst climate challenges. These results underscore the importance of adopting a combination of region-specific, context-driven technologies to enhance maize productivity and resilience to climate change across South Asia. The variations in the effectiveness of adaptation options are further influenced by soil texture, which plays a critical role in determining how these technologies interact with the soil's physical and chemical properties. Razzaghi et al. (2022) emphasized that soil texture significantly influences nitrogen (N) availability to plants, as it impacts N mineralization and rooting depth and distribution. Our analysed showed that NM performed well in fine textured soil where nutrient uptake was more optimised due to moisture retention and addressed the nutrient imbalance resulting in yield improvement. Our findings are supported by Ahmadi et al. (2011) which stressed upon the role of soil texture on nutrient uptake, NUE and leaching losses. Medium textured soils (S2), responded well to MC technologies due to their moderate capacity for water storage. Onyekwelu and Sharda (2024) simulated the impact of climate change on rainfed maize productivity, they found that root proliferation as an adaptation strategy improved yield by 27% and rainfall productivity by 28%, with better performance in silty clay loam and clay loam soils. The in-situ MC helped in improving water retention and nutrient availability of the soil, leading to a significant increase in yield (Jiang et al. 2024, Demo and Asefa Bogale 2024; Abdallah et al, 2021). IC also performed strongly in medium soils due to its ability to improve soil fertility and nutrient cycling through the integration of legumes, which fix nitrogen and enhance soil health. Most of the technologies performed well in coarse textured soil compared to other soil types with greater benefits from technologies like MC and IC. However, IC was found to be highly effective as it promotes better nutrient utilization through complementary plant interactions, while also reducing soil erosion and improving water availability (Maitra et al. 2021; Stomph et al. 2020). Irrigation management supports this by ensuring consistent water supply, which is critical in coarse soils that tend to drain quickly (Eisenhauer et al. 2021; (Wankmüller et al. 2024). Our findings align with previous research highlighting the importance of adapting technologies based on soil type to optimize productivity (Gabhane et al. 2023; Rehman et al. 2019).

In addition to the influence of soil texture, the effectiveness of adaptation options is also dependent on distinct climatic conditions and challenges of different agro-climatic zones, necessitating the adoption of region-specific technologies to optimize maize productivity. The meta-analysis evaluated the performance of various adaptation options for various climate hazards on maize yield across different agro-climatic zones and provided valuable insights into their effectiveness. In CWZ, where maize faces temperature stress, drought due to rainfall variability, I Management and V have emerged as the most effective technologies. Among the various options, I was found to be crucial in ensuring optimal water availability during dry spells, significantly enhancing maize growth and yield. Aryal et al. (2020) and Jat et al. (2019) demonstrated that integrating conservation agriculture with sub-surface drip irrigation (CA+) significantly improved crop productivity, water use efficiency in western zone saving up to 95.5% of irrigation water in maize-wheat rotations offering a sustainable solution for water-scarce regions. V Selection, focused on heat-resistant and drought-tolerant varieties, offering the best performance in adapting to fluctuating temperatures and water stress, thus outperforming other techniques in this zone. The study by Abdallah et al. (2021), and Jat et al. (2019) emphasizes the effectiveness of crop and varietal diversification for managing drought in the rainfed maize ecosystems of western zone providing higher yields and better water use efficiency during drought years. Maize hybrid developed for high-altitude ecologies in hilly zone, demonstrated significant yield potential, producing 7.0-8.2 t ha⁻¹ compared to the 2.5-4.0 t ha⁻¹ of local landraces (Ahangar et al. 2020). In both NHZ and NEPZ, MC and IC have shown the best results. These two technologies outperform others in these regions by improving soil moisture retention. Sarangi et al. 2020 reported improvement in maize yield by conserving soil moisture in the eastern plain zone using raised-bed sowing, followed by conventional tillage and zero-tillage. Intercropping not only optimizes land use but also reduces pest pressure and boosts resource efficiency, enhancing resilience to temperature extremes and thus offering significant benefits over other methods. In NWPZ, where heat stress and water scarcity are key challenges, Nutrient Management and Irrigation Management performed better than other adaptation options. Rehman et al. 2019, demonstrated that nutrient management treatments, such as salicylic acid and moringa leaf extracts, effectively mitigated chilling stress in maize, enhancing growth and yield in cold-prone regions like the NWPZ. Nutrient Management addresses soil fertility issues and ensures that maize can withstand heat stress, while irrigation management mitigates the impact of erratic rainfall and water scarcity, making these two options more effective than others in this zone. Whereas, in PZ, Moisture Conservation stands out as the most effective technology for improving maize yields by providing a more stable and resilient growing environment. Based on our analysis certain adaptation options were not studied in specific agro-climatic zones. For example, Irrigation Management (I) was not studied in NEPZ, NHZ, and PZ. IC was not evaluated in CWZ, NEPZ and PZ. Additionally, MA and V were not evaluated in PZ. The present findings suggest that these technologies may either be less relevant or underutilized in these zones due to factors such as local climatic conditions, agricultural practices, or resource constraints. Further research is needed to explore the reasons behind the limited application of these options in the respective regions.

Furthermore, while agro-climatic zones provide a framework for understanding regional variations, climatic factors such as temperature and rainfall play a pivotal role in determining the efficacy of adaptation technologies, highlighting the need for tailored technologies that address specific climatic hazards. Xu et al. 2024b projected a 21.4% reduction in maize yields in Northeast China, primarily due to rising temperatures. While elevated CO₂ and precipitation provided some compensation, adaptation options like optimizing planting dates had limited benefits due to heat stress, highlighting the need for specific approaches to mitigate climate impacts on maize. The results of meta-analysis highlight the significant influence of rainfall and temperature on the effectiveness of different adaptation options in maize. The categorization of rainfall into three distinct groups (R1, R2, R3) and temperature into three categories (T1, T2, T3) offers a more nuanced understanding of the impact of these climatic factors on maize yield improvement when employing various technologies. Based on rainfall: in R1 regions, IC and NM are the most reliable technologies, providing significant positive effects on maize yield. I and MC technologies are also effective in R1 and R3, showing promising results. ZT is suitable for R2 and R3 rainfall regions, demonstrating consistent positive effects in R3 and moderate impact in R2. EI, MA, and V should be avoided in high or moderate rainfall areas, as they show weaker or less consistent performance. Yasin et al. (2024) observed that the effects of water stress during pre-flowering and grain-filling stages in maize reduced grain yield more in non-drought tolerant varieties compared to drought-tolerant ones. Heat stress reduces yields, grain quality, and increases pest susceptibility and thus is a major challenge for maize (Ahmad et al. 2024). The analysis based on temperature suggested that: for T1 (25–30°C), IC and MC technologies are highly recommended, as they show significant positive effects on maize yield. In T3 (> 30°C), V technology should be prioritized, as it has the highest positive effect on maize yield. Waqas et al. 2021 suggested that cultural practices, exogenous protectants, and breeding climate-resilient crops can help in mitigating temperature related stress in maize. I technology also performed well in T1, making it a strong option for this temperature range. ZT and EI technologies are less effective, particularly in T2, where their impact is minimal. MA technology showed the lowest performance across all temperature categories in our analysis. For R1 and T1, IC, NM, and MC technologies are the most effective options for maize yield improvement. These findings suggest that the performance of adaptation options is strongly influenced by both rainfall and temperature.

4.2 Seasonal Effectiveness of Adaptation Options on Maize Yield

The wet and dry seasons represent two distinct maize growing periods in South Asia, each influenced by different climatic conditions. (Kumara et al. 2020) highlighted that the performance of maize genotypes varied significantly between the wet and dry seasons due to influence of seasonal climatic variations on trait expression. The wet season coincides with the monsoon season of the study region, bringing high temperatures and abundant rainfall that are conducive to maize growth, but also pose hazards such as floods and temperature extremes. In contrast, the dry season, from November to March, is characterized by cooler temperatures and reduced rainfall, creating a more stable environment but presenting challenges related to water scarcity and potential frost. Given these contrasting climatic conditions, maize cultivation in these seasons requires different adaptation technologies, with wet focusing on water management and dry emphasizing irrigation and temperature control. The meta-analysis revealed distinct patterns in yield improvements for both seasons, with specific adaptation technologies proving effective in each. In the wet season, I and MC technologies demonstrated significant positive effects, contributing to notable yield gains. (Bibe et al. 2016) found that wet maize growth and yield were significantly influenced by irrigation and fertigation management. Irrigation levels improved yield and demonstrated better water use efficiency. Conversely, in the dry season, NM emerged as a key factor in improving maize yield, particularly under cooler, drier conditions. (Singh et al. 2016) found that nutrient management significantly improved the productivity and profitability of dry maize hybrids Significant seasonal and regional variations in maize yield responses to adaptation technologies were observed across agro-climatic zones. In the wet season, ZT and I showed substantial positive effects, especially in the NWPZ and CWZ. In the dry season, NM and ZT were particularly effective, especially in the CWZ and NEPZ. (Opaluwa et al. 2015) highlighted that, different agricultural zones in Kogi State face unique challenges affecting maize production. The study concluded that these zone-specific constraints require targeted interventions to improve maize production in each region. This supports our findings and highlights the importance of seasonally specific, region-specific adaptation technologies to optimize maize yield, addressing the unique climatic challenges of each season.

4.3 Limitations of the study

While this research provides valuable insights into the effectiveness of various adaptation technologies for maize production under different climate stress, it is important to acknowledge certain limitations. Firstly, the analysis relies on existing published studies, which may have variability in data quality and experimental designs, potentially affecting the robustness of the conclusions. Additionally, the meta-analysis is constrained by the availability and scope of data from specific agro-climatic zones, which may not fully represent the diverse conditions across all maize-producing regions in South Asia. Furthermore, the study focuses primarily on yield-based outcomes, and while this is a key indicator, it does not account for other important factors such as economic viability, social acceptance, and long-term sustainability of these adaptation technologies. The study also terms certain technologies as adaptation options, but it should be noted that experimentation was not necessarily conducted under stress conditions, and loss reduction is not universally established across all studies. Finally, the lack of direct experimental evidence under real-world climate stress conditions may limit the generalizability of the findings, as the impact of adaptation options can vary with evolving climate scenarios and regional specific.

4.4 Future Directions

Future research should focus on evaluating the long-term impacts of adaptation options on maize yields under varying climate conditions, with an emphasis on the long term effectiveness of these practices over time. Expanding the scope of research to include diverse agro-climatic regions and a wider array of maize varieties would help make the findings more universally applicable. Moreover, incorporating modern technologies, such as precision agriculture and ecological intensification, alongside simulation models and remote sensing, would provide valuable insights into real-time climate effects and assist in fine-tuning adaptation technologies. In addition, examining the socio-economic aspects, including the influence of these technologies on farm income, labour dynamics, and community resilience, is critical for a well-rounded understanding of their potential benefits and challenges. Finally, exploring how policy frameworks support or hinder the adoption of these practices could provide important guidance for scaling up climate-smart approaches in agriculture.

This study demonstrated that targeted climate adaptation technologies significantly enhance maize yield resilience across different agro climatic zones of South Asia, with profound implications for food security and agricultural sustainability in the context of climate change. Technologies such as in-situ moisture conservation, irrigation, and inter cropping consistently perform better than others, leading to substantial yield improvements, particularly in drought-prone areas and regions with varied soil and rainfall conditions. Nutrient management also proves effective, optimizing nutrient uptake and boosting maize productivity under climate stress. However, Ecological intensification and variety selection show inconsistent or limited effectiveness across different agro-climatic zones. Similarly, zero tillage and multiple adaptations options demonstrate weaker impacts and need careful integration of combination of adaptation options based on regional conditions. To drive substantial improvements in maize productivity and climate resilience, the following policy actions are imperative: 1. Prioritize effective technologies: Implement and scale up technologies such as in-situ moisture conservation, irrigation, zero till in combination with mulching and integrated crop management in regions where they demonstrate clear benefits, thereby enhancing adaptation to climate-induced stress. 2. Investment in agricultural research and development: Focus on improving the scalability of these technologies and optimizing them for diverse agro- climatic zones and soil types. 3. Strengthen farmer support systems: Expand extension services, training programs, and technology transfer mechanisms to ensure smallholder farmers possess the necessary tools to implement climate-smart practices effectively. 4. Environmental Incentives: Provide incentives in form of carbon credit or any other mechanism for adoption of climate-resilient technologies by the farmers and encourage widespread adoption. By aligning policy with the proven effectiveness of these technologies, South Asia can not only safeguard maize production against the impacts of climate change but also foster a more resilient and sustainable agricultural sector. This approach will contribute to long-term food security, improve livelihoods, and support the region’s climate adaptation efforts.

Acknowledgement The authors thank ICAR and BISA for their continued support throughout this research. Additionally, the authors extend their gratitude to Dr. Ashok Kumar, ICAR-CIBA for facilitating extraction of valuable local literature and the ACASA (Atlas of Climate Adaptation in South Asia) project team for discussion and knowledge support.

Author Credit NJ: Conceptualizing, methodology, data curation, analysis, visualisation, and writing original manuscript, reviewing and editing; HV: data collection, compilation, curation & analysis, visualisation, and writing original manuscript, reviewing and editing; AD: Conceptualizing, methodology, data analysis, reviewing and editing; KB methodology; reviewing and editing; AB: reviewing and editing; BC: reviewing and editing; VV: methodology, reviewing and editing; CAR: reviewing and editing; PKA: Conceptualizing, reviewing, editing.

Funding This research was funded by the Bill and Melinda Gates Foundation (BMGF) under the Grant INV-042997

Data availability Data will be made available on reasonable request

Code availability Not applicable

Competing Interest: We declare that we have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics approval: Not applicable

Consent to participate: Not applicable

Consent for publication: Not applicable

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Effect of climate change adaptation options on maize yield across different agro-climatic zones in South Asia: A meta-analysis

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Literature search and PRISMA framework

2.2 Bibliometric analysis

2.3 Meta-analysis

2.3.1 Database

2.3.2 Data extraction

2.3.3 Effect size

2.3.4 Heterogeneity and publication bias

2.3.5 Meta-regression

3. Results

3.1 Insights from bibliometric analysis

3.1.1 Spatial distribution of adaptation options across agro-climatic zones

3.1.2 Co-occurrence patterns of climatic hazards and adaptation technologies

3.2 Effect of adaptation options on maize yield

3.2.1 Soil texture

3.2.2 Agro-climatic zone

3.3 Effect of growing season on maize yield

3.3.1 Agro-climatic zones

3.3.2 Soil texture

3.4 Agro-climatic determinants of maize yield

4. Discussion

4.1 Effectiveness of Adaptation Options on Maize Yield

4.2 Seasonal Effectiveness of Adaptation Options on Maize Yield

4.3 Limitations of the study

4.4 Future Directions

5. Conclusions and Policy Implications

Declarations

References

Supplementary Files

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