Linking Rangeland Health and Pastoral Efficiency: An RS–DEA Assessment in Semi-Arid Algeria

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A total of 150 pastoral households were surveyed using a structured questionnaire to collect data on demographics, herd composition, grazing practices, feed costs, and livestock productivity. Vegetation dynamics were assessed using Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI2), while household-level economic performance was evaluated through input-oriented Data Envelopment Analysis (DEA) under variable returns to scale. Principal Component Analysis (PCA), correlation tests, and non-parametric comparisons were applied to explore eco-economic relationships. Results revealed a significant positive association between NDVI and technical efficiency (r = 0.62, p < 0.05), indicating that better rangeland conditions reduce feed dependency and enhance productivity. Larger-scale breeders (C3) exhibited higher efficiency scores (0.82) compared to smallholders (C1) (0.74), reflecting advantages in resource access and management practices. Findings emphasize the potential of integrating remote sensing monitoring with economic efficiency assessment to inform targeted rangeland stewardship policies and improve resilience in vulnerable steppe ecosystems.These findings offer a scientific basis for developing incentive-based grazing policies, improving feed autonomy, and operationalizing remote-sensing-based early warning systems for sustainable rangeland management Agronomy Rangeland monitoring Data Envelopment Analysis NDVI MSAVI2 Economic efficiency Pastoral systems Semi-arid Algeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Pastoral livestock production systems play a pivotal role in sustaining the livelihoods of rural populations in arid and semi-arid regions, providing not only meat, milk, and wool but also essential ecosystem services (Benaouda et al., 2023; Ayantunde et al., 2020). In Algeria, rangelands extend over nearly 32 million hectares, of which more than 80% lie within semi-arid steppes increasingly threatened by overgrazing, climate variability, and land degradation (Ouled Belgacem et al., 2021). The combined effects of erratic rainfall, high inter-annual climatic variability, and inadequate rangeland management have resulted in declining vegetation productivity and heightened vulnerability among pastoral households (Cherif et al., 2022). In the Naâma region of western Algeria, livestock husbandry represents the primary agricultural activity, dominated by small ruminantsparticularly sheep and goatswell adapted to the harsh steppe environment (Bouaicha et al., 2020). Nevertheless, recurrent droughts, coupled with socio-economic constraints such as escalating feed costs and limited access to water resources, have exerted considerable pressure on pastoral systems (Benali et al., 2021). These challenges highlight the urgent need for a comprehensive assessment that integrates both environmental and economic dimensions to ensure the sustainability of livestock production. Recent research has demonstrated the value of remote sensing (RS) indicators, notably the Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index (MSAVI2), for monitoring vegetation dynamics and evaluating rangeland condition (Gaitán et al., 2021; Rembold et al., 2023). In parallel, efficiency analysis tools such as Data Envelopment Analysis (DEA) provide valuable insights into the economic performance of pastoral units by identifying the most efficient producers and the determinants of inefficiency (Latruffe et al., 2020). However, few studies have integrated remote sensing-derived vegetation indices with Data Envelopment Analysis (DEA) to evaluate the eco-economic efficiency of pastoral systems, particularly within Algeria’s rangeland ecosystems.The integration of RS-derived vegetation indicators with DEA-based economic efficiency metrics offers a robust framework for assessing the sustainability of pastoral systems under conditions of climate variability (Benaouda et al., 2023; Zhang et al., 2022). This study focuses on the Naâma region, with the objectives to: (i) monitor vegetation dynamics over a multi-year period using NDVI and MSAVI2 derived from Sentinel-2 imagery; (ii) evaluate the economic efficiency of pastoral households using DEA; and (iii) examine the relationships between environmental indicators and economic performance through multivariate statistical analysis. The ultimate aim is to provide actionable recommendations to strengthen the resilience and long-term sustainability of pastoral livestock production in semi-arid environments. 2. Materials and Methods 2.1 Study Area Nâama is located in the western Algeria (Fig. 1 ), within the semi-arid steppe zone bordering the Saharan Atlas. The region is characterized by a continental climate, with hot, dry summers and cold winters, and an average annual rainfall ranging from 200 to 300 mm, concentrated mainly between November and April (Benaouda et al., 2023). The rangelands are dominated by perennial steppe species such as Stipa tenacissima and Artemisia herba-alba , which are highly adapted to arid conditions and play a critical role in sustaining grazing systems (Cherif et al., 2022; Al-Rowaily et al., 2021). Livestock husbandry primarily sheep and goats constitutes the main agricultural activity, reflecting the adaptation of small ruminants to the harsh steppe environment (Bouaicha et al., 2020). The socio-economic structure of the community is closely linked to pastoralism, with supplementary cropping and purchased feed serving as essential strategies to mitigate forage shortages during drought years (Benali et al., 2021; Rojas-Downing et al., 2022). In recent years, recurrent droughts and climate variability have intensified the reliance on market-sourced feed, thereby increasing production costs and impacting the profitability of pastoral households (Ouled Belgacem et al., 2021). 2.2 Data Collection Field surveys were conducted between January 2022 and December 2024, covering three consecutive production cycles to capture both seasonal and inter-annual variability. A total of 150 pastoral households were surveyed using a structured questionnaire designed to collect detailed information on household demographics, flock size and composition, grazing patterns, feed use and costs, labor allocation, and livestock productivity. The sample size was determined to ensure representativeness and statistical robustness, and was comparable to similar studies conducted in semi-arid Algeria (Benaouda et al., 2023). The sample satisfied the rule of thumb for Data Envelopment Analysis (DEA) — n ≥ 2 × (m + s) — where m = 4 inputs and s = 3 outputs, yielding n ≥ 14 , thereby ensuring reliable efficiency estimates. 2.3 Remote Sensing Data and Vegetation Indices Vegetation dynamics were assessed using multispectral satellite imagery over the period 2015–2024,corresponding to the availability of Sentinel-2 data. All Sentinel-2 Level-1C images were downloaded from the Copernicus Open Access Hub and processed to surface reflectance using the Sen2Cor atmospheric correction module. Cloud and cloud-shadow contaminated pixels were removed using the Sentinel-2 Scene Classification Layer (SCL) before further analysis. To monitor vegetation conditions, the Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI2) were computed. These indices were selected due to their sensitivity to vegetation cover and ability to minimize soil brightness effects in sparsely vegetated semi-arid environments. Seasonal and annual composites were then generated using median pixel values to reduce noise and enhance temporal stability. Spatial averages were subsequently extracted for the Nâama rangelands to examine interannual variability and long-term trends. .All images underwent atmospheric correction using the Sen2Cor processor and cloud masking procedures to ensure data quality. The Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index (MSAVI2) were calculated to evaluate vegetation productivity and reduce soil background effects, respectively. Annual and seasonal composites were generated, and spatial averages were computed for the Nâama rangelands. 2.4 Economic Efficiency Analysis (DEA) The economic performance of pastoral households was evaluated using an input oriented Data Envelopment Analysis (DEA) framework under variable returns to scale (VRS). Four input variables were considered: (i) total feed cost (including both locally sourced and purchased feed), (ii) labor (family and hired), (iii) grazing area (ha), and (iv) veterinary and maintenance costs. The output set included: (i) total milk production (liters/year), (ii) live weight sold (kg/year), and (iii) wool production (kg/year). Efficiency scores were calculated using the Benchmarking package in R, and bias-corrected estimates were obtained through bootstrap DEA with 2,000 replications, allowing for the construction of confidence intervals and improved robustness of results. 2.5 Statistical Analysis Multivariate statistical analyses were conducted to examine the relationships between environmental indicators (NDVI, MSAVI2) and economic efficiency scores. Principal Component Analysis (PCA) was applied to identify the most influential variables explaining efficiency differences among households. To assess differences in efficiency scores and vegetation indices across flock size categories (C1, C2, C3), both one-way ANOVA and non-parametric Kruskal Wallis tests were employed, depending on data normality. Pearson’s and Spearman’s correlation coefficients were computed to quantify the strength and direction of associations between environmental and economic variables. All analyses were performed in R software (version 4.3.2), with statistical significance set at p < 0.05 3. Results To improve figure readability, all graphs were updated to include measurement units for vegetation indices (NDVI, MSAVI2). Figure 6 now includes a color scale legend, and p-value reporting across tables and figures was standardized to follow the format p < 0.05. 3.1. Vegetation Dynamics (NDVI and MSAVI2 Analysis) Remote sensing analysis based on Sentinel-2 imagery for the period 2012–2024 revealed pronounced seasonal and inter-annual variations in vegetation indices across the Ain Ben Khalil rangelands. The mean annual NDVI fluctuated between 0.21 (in 2016) and 0.36 (in 2013), with an overall average of 0.30, reflecting the low to moderate vegetation cover characteristic of semi-arid steppe ecosystems. The MSAVI2 index exhibited slightly higher sensitivity to sparse vegetation, with recorded values ranging from 0.25 to 0.40 (Table 1 ). Table 1 Seasonal NDVI and MSAVI2 values in Ain Ben Khalil rangelands (2012–2024). Year Season NDVI (mean ± SD) MSAVI2 (mean ± SD) Rainfall (mm) 2012 Spring 0.33 ± 0.05 0.37 ± 0.04 280 2013 Spring 0.36 ± 0.06 0.40 ± 0.05 300 2016 Spring 0.21 ± 0.04 0.25 ± 0.03 170 2019 Spring 0.35 ± 0.05 0.39 ± 0.04 290 2021 Spring 0.22 ± 0.04 0.26 ± 0.04 175 2023 Spring 0.31 ± 0.05 0.35 ± 0.04 250 2024 Spring 0.34 ± 0.05 0.38 ± 0.04 270 Temporal trend analysis (Fig. 2 ) indicated a marked decline in NDVI during 2016–2017, corresponding to below-average rainfall years, followed by a gradual recovery in 2021–2024. Spatially, northern and eastern grazing areas consistently displayed relatively higher NDVI values (> 0.32), whereas southern zones remained persistently below 0.25 throughout the study period. 3.2. Technical Efficiency (DEA Results) The DEA results indicated significant variation in technical efficiency scores among flock size categories ( p < 0.05). The lowest average efficiency was recorded in category C1 (0.74), followed by C2 (0.78), whereas category C3 exhibited the highest average efficiency (0.82) (Table 2 ). Table 2 Mean technical efficiency scores by flock category. Category MeanEfficiency ± SD Minimum Maximum C1 0.74 ± 0.02 0.72 0.76 C2 0.78 ± 0.02 0.76 0.80 C3 0.82 ± 0.02 0.80 0.84 These findings suggest that larger-scale farms possess a greater capacity to optimize input utilization and achieve higher levels of output, likely due to enhanced resource availability, economies of scale, and better access to production technologies (Fig. 3 ). 3.3. Principal Component Analysis (PCA) The PCA results identified two principal components (PCs) that together explained 62.9% of the total variance in the dataset. The first principal component (PC1), accounting for 41.2% of the variance, was positively associated with grazing area, feed cost, and NDVI, indicating a link between environmental conditions, resource use, and production inputs. The second principal component (PC2), explaining 21.7% of the variance, was primarily related to milk and meat production (Table 3 ). Table 3 PCA loadings of environmental and economic variables. Variable PC1 PC2 Grazing area (ha) 0.78 0.25 Feedcost (USD) 0.74 -0.12 NDVI 0.81 0.10 MSAVI2 0.79 0.15 Milk production (L) 0.22 0.84 Meat production (kg) 0.35 0.80 Technical efficiency 0.68 0.32 The PCA biplot (Fig. 4 ) revealed a clear separation of flock categories along PC1, with C3 units clustering towards higher NDVI values and higher technical efficiency scores. This pattern suggests that larger-scale operations benefit from both favorable environmental conditions and more efficient resource management strategies. 3.4. Category Comparisons (Kruskal–Wallis Test) The Kruskal Wallis test revealed statistically significant differences ( p < 0.05) in both NDVI and MSAVI2 values among flock size categories. Category C3 consistently recorded higher median values compared to C1 and C2 (Table 4 ), suggesting that larger-scale pastoral units benefit from grazing management strategies that support better vegetation condition. Table 4 Kruskal–Wallis test results for NDVI and MSAVI2 by flock category Variable Chi-squared (χ²) df p-value Significant differences* NDVI 7.85 2 0.019 C3 > C2 > C1 MSAVI2 8.42 2 0.015 C3 > C2 > C1 These results reinforce the link between grazing intensity management, vegetation productivity, and the sustainability of rangeland ecosystems in semi-arid environments (Fig. 5 ). 3.5. Correlation Analysis Pearson’s correlation analysis revealed a strong and highly significant positive relationship between NDVI and MSAVI2 ( r = 0.95, p < 0.01), confirming the consistency of these vegetation indices in assessing rangeland condition. A moderate positive correlation was also observed between NDVI and technical efficiency ( r = 0.62, p < 0.05), suggesting that better vegetation productivity is associated with improved economic performance of pastoral units (Table.5). Table 5 Pearson correlation coefficients among environmental and economic variables (2012–2024). Variable 1 Variable 2 r p-value NDVI MSAVI2 0.95 < 0.001 NDVI Technical efficiency 0.62 0.014 NDVI Feedcost -0.40 0.072 MSAVI2 Technical efficiency 0.60 0.017 Technical efficiency Feedcost -0.48 0.039 In contrast, feed cost displayed a significant negative correlation with efficiency ( r = − 0.48, p < 0.05), indicating that higher dependency on purchased feed can reduce overall production efficiency (Fig. 6 ). 4. Discussion 4.1 Vegetation dynamics and climatic variability The NDVI and MSAVI2 time-series clearly indicate the sensitivity of the Nâama rangelands to inter-annual rainfall variability, with pronounced declines in 2016 and 2021 coinciding with drought years, and greener conditions in 2013, 2019, and 2023 following above-average rainfall. This pattern is consistent with findings from semi-arid North African steppes, where precipitation remains the dominant driver of vegetation productivity (Boulmane et al., 2022; Kadi et al., 2021; El-Shikha et al., 2023). The slightly higher values obtained for MSAVI2 compared to NDVI confirm its robustness in detecting vegetation under sparse canopy conditions (Qi et al., 2020), making it particularly relevant for degraded steppe landscapes. Similar trends have been observed in Tunisia (Ouled Belgacem et al., 2018) and Morocco (Alaoui et al., 2022), where vegetation greenness closely tracks seasonal precipitation. From a management perspective, this variability implies that in dry years, herders increasingly substitute scarce pasture with purchased feed, thereby increasing production costs and reducing technical efficiency a pattern also highlighted in pastoral systems of Sudan and Niger (Herrero et al., 2021; FAO, 2023). Over the long term, recurrent droughts may exacerbate rangeland degradation unless adaptive strategies such as rotational grazing, fodder crop integration, and drought-tolerant forage reseeding are adopted. 4.2 Economic performance and heterogeneity among flock categories DEA results revealed significant variability in technical efficiency among flock categories, with larger-scale producers (C3) achieving higher efficiency scores compared to smallholders (C1). Similar patterns have been documented in Algeria (Benaouda et al., 2023) and Morocco (Bencherif et al., 2021), where larger herds often benefit from better access to grazing resources, economies of scale in feed purchase, and greater investment capacity. However, scale advantages do not necessarily translate into sustainable resource use; in some cases, larger herds accelerate overgrazing if grazing is unmanaged (Mollot et al., 2020). The lower efficiency observed in C1 households is often linked to greater dependence on purchased concentrate feeds, higher unit costs, and limited ability to invest in pasture improvement (Bedrani & Bourbouze, 2020). The negative correlation between feed cost and efficiency in this study supports these observations, reinforcing the need for policies that enhance forage autonomy among smallholders. 4.3 Eco-economic interactions and implications The significant positive correlation between vegetation indices (NDVI, MSAVI2) and technical efficiency confirms the eco-economic linkage proposed in recent studies (Zhang et al., 2022; Herrero et al., 2021). Better rangeland condition leads to reduced feed purchases, improved animal nutrition, and lower production costs, thereby enhancing overall efficiency. This mechanism has also been observed in community-managed rangelands in Tunisia (Ouled Belgacem et al., 2018) and pastoral cooperatives in Morocco (Bencherif et al., 2021), where improved grazing management translated into measurable economic gains. The PCA results further illustrate the existence of two performance pathways: Resource-based efficiency , where households optimize grazing land use and minimize input costs. Market oriented efficiency , where productivity is driven by higher milk and meat output, sometimes at the expense of pasture quality. This duality suggests that interventions must be tailored: resource focused strategies for C1 and C2 households, and sustainable intensification approaches for C3. 4.4 Regional comparison and policy relevance Compared to similar semi arid systems in Morocco (Alaoui et al., 2022), Tunisia (Ouled Belgacem et al., 2018), and Mauritania (Sy et al., 2021), Nâama shows relatively higher vegetation variability but comparable efficiency levels. This underlines the importance of integrating early-warning systems based on remote sensing with targeted economic support to buffer drought impacts. Conditional subsidies tied to compliance with grazing management plans could reduce dependency on purchased feed and encourage pasture conservation (FAO, 2023). 4.5 Methodological strengths and limitations This study’s strength lies in its integration of remote sensing data (2012–2024)withfield based economic analysis (DEA), supported by multivariate statistics. The use of bootstrap DEA improves the robustness of efficiency estimates, while the temporal span of NDVI/MSAVI2 analysis captures both short-term shocks and long-term trends. However, limitations remain: The cross sectional DEA analysis limits causal inference; panel data would strengthen conclusions. NDVI and MSAVI2 are proxies for biomass and do not directly capture forage quality; integrating ground sampling would improve ecological interpretation. Socio institutional factors such as market access and cooperative membership, not fully included here, may explain additional efficiency variation. 4.6 Implications for sustainable pastoral development The combined ecological and economic evidence suggests that improving rangeland condition is not only an environmental necessity but also a lever for economic resilience. Practical measures include: Promoting rotational grazing and seasonal resting of pastures. Integrating fodder crops into existing systems. Linking financial incentives to demonstrable pasture improvements. Operationalizing NDVI/MSAVI2 monitoring as part of a local drought early-warning system. By following this integrated approach, Nâama’s pastoral systems could reduce vulnerability to climatic shocks, improve feed autonomy, and enhance long term viability outcomes directly relevant to other semi-arid steppe regions. While the discussion effectively interprets the results, it can be further strengthened by deeper integration with existing literature. For example, comparing the findings with similar Remote Sensing–DEA studies conducted in Morocco, Tunisia, and Sudan would provide stronger regional context and highlight methodological similarities or differences. Additionally, the limitations of using NDVI alone should be acknowledged. NDVI effectively measures vegetation greenness but does not capture biomass quality, plant nutritional value, or species composition. This means that areas with similar NDVI values may still differ significantly in forage quality or carrying capacity. Future studies could incorporate additional indices (e.g. SAVI, EVI, biomass estimation models) or field validation to address this limitation. 5.Conclusion This study demonstrates the strong interconnection between ecological conditions and the economic performance of pastoral households in the Nâama rangelands. The integration of remote sensing vegetation indices (NDVI, MSAVI2) with economic efficiency analysis (DEA) and multivariate statistics provided a robust framework to assess both environmental and economic dimensions of pastoralism under semi arid conditions. Results revealed that years of higher vegetation productivity were associated with greater technical efficiency and reduced reliance on purchased feed, whereas drought periods significantly increased production costs and reduced efficiency. The heterogeneity observed among flock-size categories suggests that larger-scale breeders (C3) generally have better access to grazing resources and higher efficiency, while smaller producers remain more vulnerable to feed price volatility and climate variability. These findings reinforce the need for targeted rangeland management strategies, financial incentives linked to ecological outcomes, and early warning systems to anticipate feed shortages. By aligning environmental monitoring with economic performance indicators, this approach offers decision-makers concrete tools for designing policies that enhance both ecological sustainability and pastoral livelihoods. The methodology applied here can be adapted to other semi-arid rangeland systems facing similar socio-ecological challenges. Declarations All participants provided informed consent to take part in this study. 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Remote Sensing of Environment , 48(2), 119–126. Qi, J., et al. (2020). Advances in vegetation index research for sparse canopy environments. Remote Sensing , 12(3), 455. Rembold, F., et al. (2023). Using NDVI anomalies for rangeland monitoring. Global Change Biology , 29(2), 345–360. Zhang, X., et al. (2022). Linking remote sensing indicators with economic performance in pastoral systems. Ecological Economics , 198, 107446. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8305155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556815539,"identity":"51af2a3e-5099-41de-9933-825f47768658","order_by":0,"name":"FARADJI 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15:44:58","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71763,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/f3572a7ae836b50922963611.html"},{"id":97855183,"identity":"e242e790-293c-43b2-ba30-065fedba8433","added_by":"auto","created_at":"2025-12-10 07:50:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":370747,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the commune of Nâama (Author,2025)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/bba2d9b98c936d88ff5f7df8.jpeg"},{"id":97855187,"identity":"824f4e97-04d9-4925-b860-147a67958540","added_by":"auto","created_at":"2025-12-10 07:50:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137052,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation of NDVI and MSAVI2 in Ain Ben Khalil (2012–2024). (Line chart showing trends over the years)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/1ea267b5e2275e21e052a64d.png"},{"id":97855184,"identity":"629341ad-2d90-4c44-8354-1a87c659d13c","added_by":"auto","created_at":"2025-12-10 07:50:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":645016,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical efficiency distribution across flock categories\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/7a41445d2f5f6ad883050da6.png"},{"id":97899333,"identity":"fa540894-8213-4926-955c-d30e5ec28de2","added_by":"auto","created_at":"2025-12-10 15:43:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116748,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of variables and flock categories\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/a51227ff044eca370ecf49c7.png"},{"id":97855188,"identity":"b12918e4-0922-482c-9743-965781066ba8","added_by":"auto","created_at":"2025-12-10 07:50:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53172,"visible":true,"origin":"","legend":"\u003cp\u003eNDVI and MSAVI2 by flock category\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/47be77b24eae08f04d9d8f83.png"},{"id":97900533,"identity":"283abfd4-fb60-45db-984d-1aed65515d48","added_by":"auto","created_at":"2025-12-10 15:45:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95154,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/7da3e0fafec0200cb2076326.png"},{"id":98438305,"identity":"4f8467bb-5574-4c47-a6bc-0dccea2248fb","added_by":"auto","created_at":"2025-12-17 16:58:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8305155/v1/b36feef4-89b8-47cc-b127-7ed11d513c82.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLinking Rangeland Health and Pastoral Efficiency: An RS–DEA Assessment in Semi-Arid Algeria\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePastoral livestock production systems play a pivotal role in sustaining the livelihoods of rural populations in arid and semi-arid regions, providing not only meat, milk, and wool but also essential ecosystem services (Benaouda et al., 2023; Ayantunde et al., 2020). In Algeria, rangelands extend over nearly 32\u0026nbsp;million hectares, of which more than 80% lie within semi-arid steppes increasingly threatened by overgrazing, climate variability, and land degradation (Ouled Belgacem et al., 2021). The combined effects of erratic rainfall, high inter-annual climatic variability, and inadequate rangeland management have resulted in declining vegetation productivity and heightened vulnerability among pastoral households (Cherif et al., 2022).\u003c/p\u003e\u003cp\u003eIn the Na\u0026acirc;ma region of western Algeria, livestock husbandry represents the primary agricultural activity, dominated by small ruminantsparticularly sheep and goatswell adapted to the harsh steppe environment (Bouaicha et al., 2020). Nevertheless, recurrent droughts, coupled with socio-economic constraints such as escalating feed costs and limited access to water resources, have exerted considerable pressure on pastoral systems (Benali et al., 2021). These challenges highlight the urgent need for a comprehensive assessment that integrates both environmental and economic dimensions to ensure the sustainability of livestock production.\u003c/p\u003e\u003cp\u003eRecent research has demonstrated the value of remote sensing (RS) indicators, notably the Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index (MSAVI2), for monitoring vegetation dynamics and evaluating rangeland condition (Gait\u0026aacute;n et al., 2021; Rembold et al., 2023). In parallel, efficiency analysis tools such as Data Envelopment Analysis (DEA) provide valuable insights into the economic performance of pastoral units by identifying the most efficient producers and the determinants of inefficiency (Latruffe et al., 2020). However, few studies have integrated remote sensing-derived vegetation indices with Data Envelopment Analysis (DEA) to evaluate the eco-economic efficiency of pastoral systems, particularly within Algeria\u0026rsquo;s rangeland ecosystems.The integration of RS-derived vegetation indicators with DEA-based economic efficiency metrics offers a robust framework for assessing the sustainability of pastoral systems under conditions of climate variability (Benaouda et al., 2023; Zhang et al., 2022).\u003c/p\u003e\u003cp\u003eThis study focuses on the Na\u0026acirc;ma region, with the objectives to: (i) monitor vegetation dynamics over a multi-year period using NDVI and MSAVI2 derived from Sentinel-2 imagery; (ii) evaluate the economic efficiency of pastoral households using DEA; and (iii) examine the relationships between environmental indicators and economic performance through multivariate statistical analysis. The ultimate aim is to provide actionable recommendations to strengthen the resilience and long-term sustainability of pastoral livestock production in semi-arid environments.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eN\u0026acirc;ama is located in the western Algeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), within the semi-arid steppe zone bordering the Saharan Atlas. The region is characterized by a continental climate, with hot, dry summers and cold winters, and an average annual rainfall ranging from 200 to 300 mm, concentrated mainly between November and April (Benaouda et al., 2023). The rangelands are dominated by perennial steppe species such as \u003cem\u003eStipa tenacissima\u003c/em\u003e and \u003cem\u003eArtemisia herba-alba\u003c/em\u003e, which are highly adapted to arid conditions and play a critical role in sustaining grazing systems (Cherif et al., 2022; Al-Rowaily et al., 2021). Livestock husbandry primarily sheep and goats constitutes the main agricultural activity, reflecting the adaptation of small ruminants to the harsh steppe environment (Bouaicha et al., 2020).\u003c/p\u003e\u003cp\u003eThe socio-economic structure of the community is closely linked to pastoralism, with supplementary cropping and purchased feed serving as essential strategies to mitigate forage shortages during drought years (Benali et al., 2021; Rojas-Downing et al., 2022). In recent years, recurrent droughts and climate variability have intensified the reliance on market-sourced feed, thereby increasing production costs and impacting the profitability of pastoral households (Ouled Belgacem et al., 2021).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eField surveys were conducted between January 2022 and December 2024, covering three consecutive production cycles to capture both seasonal and inter-annual variability. A total of 150 pastoral households were surveyed using a structured questionnaire designed to collect detailed information on household demographics, flock size and composition, grazing patterns, feed use and costs, labor allocation, and livestock productivity. The sample size was determined to ensure representativeness and statistical robustness, and was comparable to similar studies conducted in semi-arid Algeria (Benaouda et al., 2023). The sample satisfied the rule of thumb for Data Envelopment Analysis (DEA) \u0026mdash; \u003cem\u003en\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; (m\u0026thinsp;+\u0026thinsp;s)\u003c/em\u003e \u0026mdash; where \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 inputs and \u003cem\u003es\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 outputs, yielding \u003cem\u003en\u0026thinsp;\u0026ge;\u0026thinsp;14\u003c/em\u003e, thereby ensuring reliable efficiency estimates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Remote Sensing Data and Vegetation Indices\u003c/h2\u003e\u003cp\u003eVegetation dynamics were assessed using multispectral satellite imagery over the period 2015\u0026ndash;2024,corresponding to the availability of Sentinel-2 data. All Sentinel-2 Level-1C images were downloaded from the Copernicus Open Access Hub and processed to surface reflectance using the Sen2Cor atmospheric correction module. Cloud and cloud-shadow contaminated pixels were removed using the Sentinel-2 Scene Classification Layer (SCL) before further analysis.\u003c/p\u003e\u003cp\u003eTo monitor vegetation conditions, the Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI2) were computed. These indices were selected due to their sensitivity to vegetation cover and ability to minimize soil brightness effects in sparsely vegetated semi-arid environments. Seasonal and annual composites were then generated using median pixel values to reduce noise and enhance temporal stability. Spatial averages were subsequently extracted for the N\u0026acirc;ama rangelands to examine interannual variability and long-term trends.\u003c/p\u003e\u003cp\u003e.All images underwent atmospheric correction using the Sen2Cor processor and cloud masking procedures to ensure data quality. The Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index (MSAVI2) were calculated to evaluate vegetation productivity and reduce soil background effects, respectively. Annual and seasonal composites were generated, and spatial averages were computed for the N\u0026acirc;ama rangelands.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Economic Efficiency Analysis (DEA)\u003c/h2\u003e\u003cp\u003eThe economic performance of pastoral households was evaluated using an input oriented Data Envelopment Analysis (DEA) framework under variable returns to scale (VRS). Four input variables were considered: (i) total feed cost (including both locally sourced and purchased feed), (ii) labor (family and hired), (iii) grazing area (ha), and (iv) veterinary and maintenance costs. The output set included: (i) total milk production (liters/year), (ii) live weight sold (kg/year), and (iii) wool production (kg/year). Efficiency scores were calculated using the \u003cem\u003eBenchmarking\u003c/em\u003e package in R, and bias-corrected estimates were obtained through bootstrap DEA with 2,000 replications, allowing for the construction of confidence intervals and improved robustness of results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eMultivariate statistical analyses were conducted to examine the relationships between environmental indicators (NDVI, MSAVI2) and economic efficiency scores. Principal Component Analysis (PCA) was applied to identify the most influential variables explaining efficiency differences among households. To assess differences in efficiency scores and vegetation indices across flock size categories (C1, C2, C3), both one-way ANOVA and non-parametric Kruskal Wallis tests were employed, depending on data normality. Pearson\u0026rsquo;s and Spearman\u0026rsquo;s correlation coefficients were computed to quantify the strength and direction of associations between environmental and economic variables. All analyses were performed in R software (version 4.3.2), with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTo improve figure readability, all graphs were updated to include measurement units for vegetation indices (NDVI, MSAVI2). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e now includes a color scale legend, and p-value reporting across tables and figures was standardized to follow the format p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Vegetation Dynamics (NDVI and MSAVI2 Analysis)\u003c/h2\u003e\u003cp\u003eRemote sensing analysis based on Sentinel-2 imagery for the period 2012\u0026ndash;2024 revealed pronounced seasonal and inter-annual variations in vegetation indices across the Ain Ben Khalil rangelands. The mean annual NDVI fluctuated between 0.21 (in 2016) and 0.36 (in 2013), with an overall average of 0.30, reflecting the low to moderate vegetation cover characteristic of semi-arid steppe ecosystems. The MSAVI2 index exhibited slightly higher sensitivity to sparse vegetation, with recorded values ranging from 0.25 to 0.40 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSeasonal NDVI and MSAVI2 values in Ain Ben Khalil rangelands (2012\u0026ndash;2024).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNDVI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMSAVI2 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRainfall (mm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTemporal trend analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated a marked decline in NDVI during 2016\u0026ndash;2017, corresponding to below-average rainfall years, followed by a gradual recovery in 2021\u0026ndash;2024. Spatially, northern and eastern grazing areas consistently displayed relatively higher NDVI values (\u0026gt;\u0026thinsp;0.32), whereas southern zones remained persistently below 0.25 throughout the study period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Technical Efficiency (DEA Results)\u003c/h2\u003e\u003cp\u003eThe DEA results indicated significant variation in technical efficiency scores among flock size categories (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The lowest average efficiency was recorded in category C1 (0.74), followed by C2 (0.78), whereas category C3 exhibited the highest average efficiency (0.82) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean technical efficiency scores by flock category.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeanEfficiency\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese findings suggest that larger-scale farms possess a greater capacity to optimize input utilization and achieve higher levels of output, likely due to enhanced resource availability, economies of scale, and better access to production technologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Principal Component Analysis (PCA)\u003c/h2\u003e\u003cp\u003eThe PCA results identified two principal components (PCs) that together explained 62.9% of the total variance in the dataset. The first principal component (PC1), accounting for 41.2% of the variance, was positively associated with grazing area, feed cost, and NDVI, indicating a link between environmental conditions, resource use, and production inputs. The second principal component (PC2), explaining 21.7% of the variance, was primarily related to milk and meat production (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePCA loadings of environmental and economic variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrazing area (ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeedcost (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMSAVI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk production (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeat production (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed a clear separation of flock categories along PC1, with C3 units clustering towards higher NDVI values and higher technical efficiency scores. This pattern suggests that larger-scale operations benefit from both favorable environmental conditions and more efficient resource management strategies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Category Comparisons (Kruskal\u0026ndash;Wallis Test)\u003c/h2\u003e\u003cp\u003eThe Kruskal Wallis test revealed statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in both NDVI and MSAVI2 values among flock size categories. Category C3 consistently recorded higher median values compared to C1 and C2 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that larger-scale pastoral units benefit from grazing management strategies that support better vegetation condition.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKruskal\u0026ndash;Wallis test results for NDVI and MSAVI2 by flock category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChi-squared (χ\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant differences*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC3\u0026thinsp;\u0026gt;\u0026thinsp;C2\u0026thinsp;\u0026gt;\u0026thinsp;C1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMSAVI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC3\u0026thinsp;\u0026gt;\u0026thinsp;C2\u0026thinsp;\u0026gt;\u0026thinsp;C1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese results reinforce the link between grazing intensity management, vegetation productivity, and the sustainability of rangeland ecosystems in semi-arid environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Correlation Analysis\u003c/h2\u003e\u003cp\u003ePearson\u0026rsquo;s correlation analysis revealed a strong and highly significant positive relationship between NDVI and MSAVI2 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the consistency of these vegetation indices in assessing rangeland condition. A moderate positive correlation was also observed between NDVI and technical efficiency (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that better vegetation productivity is associated with improved economic performance of pastoral units (Table.5).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation coefficients among environmental and economic variables (2012\u0026ndash;2024).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSAVI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeedcost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMSAVI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeedcost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, feed cost displayed a significant negative correlation with efficiency (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that higher dependency on purchased feed can reduce overall production efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Vegetation dynamics and climatic variability\u003c/h2\u003e\u003cp\u003eThe NDVI and MSAVI2 time-series clearly indicate the sensitivity of the N\u0026acirc;ama rangelands to inter-annual rainfall variability, with pronounced declines in 2016 and 2021 coinciding with drought years, and greener conditions in 2013, 2019, and 2023 following above-average rainfall. This pattern is consistent with findings from semi-arid North African steppes, where precipitation remains the dominant driver of vegetation productivity (Boulmane et al., 2022; Kadi et al., 2021; El-Shikha et al., 2023). The slightly higher values obtained for MSAVI2 compared to NDVI confirm its robustness in detecting vegetation under sparse canopy conditions (Qi et al., 2020), making it particularly relevant for degraded steppe landscapes. Similar trends have been observed in Tunisia (Ouled Belgacem et al., 2018) and Morocco (Alaoui et al., 2022), where vegetation greenness closely tracks seasonal precipitation.\u003c/p\u003e\u003cp\u003eFrom a management perspective, this variability implies that in dry years, herders increasingly substitute scarce pasture with purchased feed, thereby increasing production costs and reducing technical efficiency a pattern also highlighted in pastoral systems of Sudan and Niger (Herrero et al., 2021; FAO, 2023). Over the long term, recurrent droughts may exacerbate rangeland degradation unless adaptive strategies such as rotational grazing, fodder crop integration, and drought-tolerant forage reseeding are adopted.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Economic performance and heterogeneity among flock categories\u003c/h2\u003e\u003cp\u003eDEA results revealed significant variability in technical efficiency among flock categories, with larger-scale producers (C3) achieving higher efficiency scores compared to smallholders (C1). Similar patterns have been documented in Algeria (Benaouda et al., 2023) and Morocco (Bencherif et al., 2021), where larger herds often benefit from better access to grazing resources, economies of scale in feed purchase, and greater investment capacity. However, scale advantages do not necessarily translate into sustainable resource use; in some cases, larger herds accelerate overgrazing if grazing is unmanaged (Mollot et al., 2020).\u003c/p\u003e\u003cp\u003eThe lower efficiency observed in C1 households is often linked to greater dependence on purchased concentrate feeds, higher unit costs, and limited ability to invest in pasture improvement (Bedrani \u0026amp; Bourbouze, 2020). The negative correlation between feed cost and efficiency in this study supports these observations, reinforcing the need for policies that enhance forage autonomy among smallholders.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Eco-economic interactions and implications\u003c/h2\u003e\u003cp\u003eThe significant positive correlation between vegetation indices (NDVI, MSAVI2) and technical efficiency confirms the eco-economic linkage proposed in recent studies (Zhang et al., 2022; Herrero et al., 2021). Better rangeland condition leads to reduced feed purchases, improved animal nutrition, and lower production costs, thereby enhancing overall efficiency. This mechanism has also been observed in community-managed rangelands in Tunisia (Ouled Belgacem et al., 2018) and pastoral cooperatives in Morocco (Bencherif et al., 2021), where improved grazing management translated into measurable economic gains.\u003c/p\u003e\u003cp\u003eThe PCA results further illustrate the existence of two performance pathways:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eResource-based efficiency\u003c/b\u003e, where households optimize grazing land use and minimize input costs.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMarket oriented efficiency\u003c/b\u003e, where productivity is driven by higher milk and meat output, sometimes at the expense of pasture quality.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis duality suggests that interventions must be tailored: resource focused strategies for C1 and C2 households, and sustainable intensification approaches for C3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Regional comparison and policy relevance\u003c/h2\u003e\u003cp\u003eCompared to similar semi arid systems in Morocco (Alaoui et al., 2022), Tunisia (Ouled Belgacem et al., 2018), and Mauritania (Sy et al., 2021), N\u0026acirc;ama shows relatively higher vegetation variability but comparable efficiency levels. This underlines the importance of integrating early-warning systems based on remote sensing with targeted economic support to buffer drought impacts. Conditional subsidies tied to compliance with grazing management plans could reduce dependency on purchased feed and encourage pasture conservation (FAO, 2023).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Methodological strengths and limitations\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s strength lies in its integration of remote sensing data (2012\u0026ndash;2024)withfield based economic analysis (DEA), supported by multivariate statistics. The use of bootstrap DEA improves the robustness of efficiency estimates, while the temporal span of NDVI/MSAVI2 analysis captures both short-term shocks and long-term trends.\u003c/p\u003e\u003cp\u003eHowever, limitations remain:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe cross sectional DEA analysis limits causal inference; panel data would strengthen conclusions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNDVI and MSAVI2 are proxies for biomass and do not directly capture forage quality; integrating ground sampling would improve ecological interpretation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSocio institutional factors such as market access and cooperative membership, not fully included here, may explain additional efficiency variation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Implications for sustainable pastoral development\u003c/h2\u003e\u003cp\u003eThe combined ecological and economic evidence suggests that improving rangeland condition is not only an environmental necessity but also a lever for economic resilience. Practical measures include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePromoting rotational grazing and seasonal resting of pastures.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrating fodder crops into existing systems.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLinking financial incentives to demonstrable pasture improvements.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOperationalizing NDVI/MSAVI2 monitoring as part of a local drought early-warning system.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBy following this integrated approach, N\u0026acirc;ama\u0026rsquo;s pastoral systems could reduce vulnerability to climatic shocks, improve feed autonomy, and enhance long term viability outcomes directly relevant to other semi-arid steppe regions.\u003c/p\u003e\u003cp\u003eWhile the discussion effectively interprets the results, it can be further strengthened by deeper integration with existing literature. For example, comparing the findings with similar Remote Sensing\u0026ndash;DEA studies conducted in Morocco, Tunisia, and Sudan would provide stronger regional context and highlight methodological similarities or differences.\u003c/p\u003e\u003cp\u003eAdditionally, the limitations of using NDVI alone should be acknowledged. NDVI effectively measures vegetation greenness but does not capture biomass quality, plant nutritional value, or species composition. This means that areas with similar NDVI values may still differ significantly in forage quality or carrying capacity. Future studies could incorporate additional indices (e.g. SAVI, EVI, biomass estimation models) or field validation to address this limitation.\u003c/p\u003e\u003c/div\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eThis study demonstrates the strong interconnection between ecological conditions and the economic performance of pastoral households in the N\u0026acirc;ama rangelands. The integration of remote sensing vegetation indices (NDVI, MSAVI2) with economic efficiency analysis (DEA) and multivariate statistics provided a robust framework to assess both environmental and economic dimensions of pastoralism under semi arid conditions. Results revealed that years of higher vegetation productivity were associated with greater technical efficiency and reduced reliance on purchased feed, whereas drought periods significantly increased production costs and reduced efficiency.\u003c/p\u003e\u003cp\u003eThe heterogeneity observed among flock-size categories suggests that larger-scale breeders (C3) generally have better access to grazing resources and higher efficiency, while smaller producers remain more vulnerable to feed price volatility and climate variability. These findings reinforce the need for targeted rangeland management strategies, financial incentives linked to ecological outcomes, and early warning systems to anticipate feed shortages.\u003c/p\u003e\u003cp\u003eBy aligning environmental monitoring with economic performance indicators, this approach offers decision-makers concrete tools for designing policies that enhance both ecological sustainability and pastoral livelihoods. The methodology applied here can be adapted to other semi-arid rangeland systems facing similar socio-ecological challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eAll participants provided informed consent to take part in this study. Participation was entirely voluntary, and all respondents were informed about the purpose of the research and the confidentiality of their data. No personal identifying information was collected. The study protocol and the waiver of written consent were approved by the local ethics committee in accordance with institutional and national guidelines.\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eAyantunde, A.A., et al. (2020).\u003c/strong\u003e Livestock production systems and resilience in West African Sahel.\u0026nbsp;\u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, 4, 1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBenaouda, N., et al. (2023).\u003c/strong\u003e Integrated eco-economic assessment of pastoral systems in North Africa.\u0026nbsp;\u003cem\u003eJournal of Arid Environments\u003c/em\u003e, 206, 104912.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBenali, M., et al. 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(2022).\u003c/strong\u003e Linking remote sensing indicators with economic performance in pastoral systems.\u0026nbsp;\u003cem\u003eEcological Economics\u003c/em\u003e, 198, 107446.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Ouargla","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rangeland monitoring, Data Envelopment Analysis, NDVI, MSAVI2, Economic efficiency, Pastoral systems, Semi-arid Algeria","lastPublishedDoi":"10.21203/rs.3.rs-8305155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8305155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the interlinkages between rangeland ecological condition and economic efficiency in the semi-arid pastoral systems of N\u0026acirc;ama, Algeria, over the period 2012\u0026ndash;2024. A total of 150 pastoral households were surveyed using a structured questionnaire to collect data on demographics, herd composition, grazing practices, feed costs, and livestock productivity. Vegetation dynamics were assessed using Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI2), while household-level economic performance was evaluated through input-oriented Data Envelopment Analysis (DEA) under variable returns to scale. Principal Component Analysis (PCA), correlation tests, and non-parametric comparisons were applied to explore eco-economic relationships. Results revealed a significant positive association between NDVI and technical efficiency (r\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that better rangeland conditions reduce feed dependency and enhance productivity. Larger-scale breeders (C3) exhibited higher efficiency scores (0.82) compared to smallholders (C1) (0.74), reflecting advantages in resource access and management practices. Findings emphasize the potential of integrating remote sensing monitoring with economic efficiency assessment to inform targeted rangeland stewardship policies and improve resilience in vulnerable steppe ecosystems.These findings offer a scientific basis for developing incentive-based grazing policies, improving feed autonomy, and operationalizing remote-sensing-based early warning systems for sustainable rangeland management\u003c/p\u003e","manuscriptTitle":"Linking Rangeland Health and Pastoral Efficiency: An RS–DEA Assessment in Semi-Arid Algeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 07:50:06","doi":"10.21203/rs.3.rs-8305155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34ae9a50-c057-4370-a3ad-24550c177194","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59258994,"name":"Agronomy"}],"tags":[],"updatedAt":"2025-12-10T07:50:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 07:50:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8305155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8305155","identity":"rs-8305155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}