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DOE OSTI2026

Ozone pollution reduction partially offsets the negative impact of climate change mitigation efforts on global hunger

Studies warning of the potential negative effects of climate mitigation on food security through the competing use of land for bioenergy and afforestation have overlooked the impact of reduced ozone and its potential enhancement of crop yields. Here we use six global agro-economic models to compare the impacts of climate change with climate mitigation policy and ozone reduction on agriculture. We find that ozone reduction could reduce the negative impact of a 1.5 °C-consistent climate change mitigation policy on global hunger by 15% in 2050. Sub-Saharan Africa and India, where hunger is most severe, account for 56% of this global reduction. Our findings indicate that the negative effects of climate mitigation on global hunger could be partially offset by the ozone reduction impact.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Methane flux measurements in rice by static flux chamber and eddy covariance

Abstract Understanding methane (CH 4 ) fluxes from rice ( Oryza sativa L.) at the field scale is paramount to reducing environmental impacts while ensuring global food security. Greenhouse gas (GHG) measurements at the plot scale using static flux chambers (SFC) have long informed the understanding of flux dynamics and have largely been the basis of global flux estimates. However, in many parts of the world, the landscapes where agricultural fluxes are generated come from larger fields. Eddy covariance (EC) can measure trace gases on larger fields, but there are few studies available quantifying CH 4 emissions under typical practices at a field scale. Furthermore, few of these studies are from the U.S. Midsouth, the largest producer of U.S. rice. This study compares and quantifies field‐scale SFC and EC flux measurements on a large commercial system in northeastern Arkansas during the 2015 and 2016 growing seasons, following typical producer practices. Daily measured SFC CH 4 fluxes did not differ from EC‐daily CH 4 fluxes ( p = .108). Total season CH 4 emissions, calculated as the sum of daily fluxes ranged from 50 to 156 kg CH 4 ha −1 season −1 , with SFC reporting greater emissions than EC. Although SFC and EC‐daily flux measurements were similar early ( p = .382) and late ( p = .543) in the season, they differed mid‐season ( p < .001) with SFC consistently reporting greater fluxes than EC. The findings of this study help unify season long plot‐scale and field‐scale flux measurements and signify an advancement of our understanding of GHG fluxes from rice systems.

Reba, Michele L.↗

DOE OSTI2024

Modeling the impact of measured and projected climate and management systems on agricultural fields: Surface runoff, soil moisture, and soil erosion

Abstract As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion under different management systems in Central Oklahoma. The proposed model uses measured climate data and three downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at the water resources and erosion watershed to understand the impact of climate change and various climate conditions under three management systems: (1) continuous winter wheat (Triticum aestivum) under conventional tillage (WW‐CT; baseline system), (2) continuous winter wheat under no‐till (WW‐NT), and (3) cool and warm season forage cover crop mixes under no‐till (CC‐NT). The study indicates that the occurrence of agricultural drought is projected to increase while erosion rates will remain unchanged under the WW‐CT. In contrast, climate simulations imposed on the WW‐NT and CC‐NT systems significantly reduce runoff and sediment while preserving soil moisture levels. Especially, implementing the CC‐NT system can bolster food security and foster sustainable farming practices in Central Oklahoma in the face of a changing climate.

Environmental Sciences & Ecology↗

DOE OSTI2024

Phosphorus recycling by mineral-catalyzed ribonucleotide cleavage on iron and manganese oxides

Abstract Phosphorus is an essential element influencing both food security via plant fertilization, and water pollution through excessive phosphorus use, yet the phosphorus cycle in ecosystems is poorly known. In particular, beyond adsorption, the role of iron and manganese oxides in catalyzing the abiotic dephosphorylation of biomolecules is debated. Here, we studied the reactions of ribonucleotides, containing different phosphate bonding, with goethite, hematite, and birnessite. We employed both high-resolution mass spectrometry of solution species and molecular modeling simulations of ribonucleotide-mineral complexes. Results disclose an up to fivefold preferential hydrolytic cleavage of a phosphoanhydride bond over a phosphoester bond, indicating that mineral-catalyzed reactions reflect the hierarchy reported for the activity of phosphatase enzymes. The fourfold higher catalytic reactivity of goethite and birnessite versus hematite is explained by mineral-specific binding rather than surface area differences. Corresponding simulated adsorbate conformations at the water–mineral interfaces are proposed. Overall, our findings provide new insights on the catalytic recycling of organic phosphorus species by mineral oxides.

58 GEOSCIENCES↗

DOE OSTI2023

Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. Here, this study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash–Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt 2 ) is lower than that of Landsat estimation (10.4 ppt 2 ) and numerical simulations (9.6 ppt 2 ). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Chapter 20: Advances and Application of CRISPR-Cas Systems

A new gene-editing technique called clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins have revolutionized genome engineering because of its high efficiency, relatively low cost, and ease of use compared with other techniques such as zinc-finger nucleases and transcription activator-like effector nucleases. CRISPR-Cas systems have transformed biological research, quickly becoming the preferred method for engineering specific genome sequences in industrially relevant microbes, important food crops, and human cells. This powerful new tool has the potential for limitless applications ranging from the treatment of human diseases to improving food security to the generation of renewable and sustainable bioproducts and biofuels. In this chapter, we will discuss advances and applications for these CRISPR-Cas systems in biological engineering.

bacterial↗

DOE OSTI2023

Intensification differentially affects the delivery of multiple ecosystem services in subtropical and temperate grasslands

Intensification, the process of intensifying land management to enhance agricultural goods, results in “intensive” pastures that are planted with productive grasses and fertilized. These intensive pastures provide essential ecosystem services, including forage production for livestock. Understanding the synergies and tradeoffs of pasture intensification on the delivery of services across climatic regions is crucial to shape policies and incentives for better management of natural resources. Here, we investigated how grassland intensification affects key components of provisioning (forage productivity and quality), supporting (plant diversity) and regulating services (CO 2 and CH 4 fluxes) by comparing these services between intensive versus extensive pastures in subtropical and temperate pastures in the USDA Long-term Agroecosystem Research (LTAR) Network sites in Florida and Oklahoma, USA over multiple years. Our results suggest that grassland intensification led to a decrease in measured supporting and regulating services, but increased forage productivity in temperate pastures and forage digestibility in subtropical pastures. Intensification decreased the net CO 2 sink of subtropical pastures while it did not affect the sink capacity of temperate pastures; and it also increased environmental CH 4 emissions from subtropical pastures and reduced CH 4 uptake in temperate pastures. Intensification enhanced the global warming potential associated with C fluxes of pastures in both ecoregions. Our study demonstrates that comparisons of agroecosystems in contrasting ecoregions can reveal important drivers of ecosystem services and general or region-specific opportunities and solutions to maintaining agricultural production and reducing environmental footprints. Further LTAR network-scale comparisons of multiple ecosystem services across croplands and grazinglands intensively vs extensively managed are warranted to inform the sustainable intensification of agriculture within US and beyond. Our results highlight that achieving both food security and environmental stewardship will involve the conservation of less intensively managed pastures while adopting sustainable strategies in intensively managed pastures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Site-specific field management adaptation is key to feeding the world in the 21st century

Rapid climate change and growing population threaten global food security across the globe. Several studies have proposed early planting, increased irrigation, and increased fertilizer applications as climate adaptation strategies, yet none have considered combined and site-specific field management strategies as a comprehensive solution. Here, we analyzed non-irrigated wheat yield responses to climate change and field management adaptation using a mechanistic crop model evaluated against observed global non-irrigated wheat-yield over 3-year intervals spanning 13 years at 3749 sites (RMSE = 36 gC m -2 ). Early planting with later-maturing varieties provided the most benefit to future yields among the proposed field management adaptation strategies. Improved water use efficiency from increased CO 2 led to relatively low benefits of additional irrigation. Here we estimated that spatially heterogeneous adaption strategies had the potential to improve global wheat yields by 91% by 2100 compared to the present day. The yield improvements from combined field adaptation strategies were larger than the sum of improvements from the individual strategies. These synergistic benefits were shown to result from complementary processes regulating nutrient and water uptake, physiological tolerance to heat stress, and internal carbon and nutrient cycling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Biodiesel from oil produced in vegetative tissues of biomass – A review

Biodiesel is a green, renewable alternative to petroleum-derived diesel. However, using vegetable oil for bio- diesel production significantly challenges the food security. Progress in metabolic engineering, understanding of lipid biosynthesis and storage have enabled engineering of vegetative tissues of plants such as sugarcane, sor- ghum, and tobacco for lipid production. Such sources could be cultivated on land resources, which are currently not suitable for row crops. Besides achieving significant lipid accumulation, it is imperative to maintain the fatty acid and lipid profile ideal for biodiesel production and engine performance. In this study, genetic modifications used to induce lipid accumulation in transgenic crops and the proposed strategies for efficient recovery of oil from these crops have been presented. Here, this paper highlights that lipids sourced from vegetative biomass in their native form would pose significant challenges in biodiesel production. Therefore, different strategies have been presented for improving feedstock quality to achieve high-quality biodiesel production.

09 BIOMASS FUELS↗

DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗

DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

XERICO as a target for engineering stress-resilient crops: Mechanisms, applications, and future directions

XERICO's capacity to enhance ABA-driven stress responses across diverse crops, its regulatory crosstalk with other hormonal pathways, and its compatibility with advanced genetic engineering tools highlight its central role in sustainable agriculture. Leveraging XERICO in crop improvement programs aligns with the urgent need to mitigate the impacts of climate-induced stress in agriculture, offering a pathway toward resilient and high-yielding crops. Here, by enabling crops to withstand drought and other environmental stresses, XERICO-based biotechnological approaches hold transformative potential for global food security and environmental sustainability.

09 BIOMASS FUELS↗

DOE OSTI2020

A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication

We report accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Land use for bioenergy: Synergies and trade-offs between sustainable development goals

Bioenergy aims to reduce greenhouse gas (GHG) emissions and contribute to meeting global climate change mitigation targets. Nevertheless, several sustainability concerns are associated with bioenergy, especially related to the impacts of using land for dedicated energy crop production. Cultivating energy crops can result in synergies or trade-offs between GHG emission reductions and other sustainability effects depending on context-specific conditions. Using the United Nations Sustainable Development Goals (SDGs) framework, the main synergies and trade-offs associated with land use for dedicated energy crop production were identified. Furthermore, the context-specific conditions (i.e., biomass feedstock, previous land use, climate, soil type and agricultural management) which affect those synergies and trade-offs were also identified. The most recent literature was reviewed and a pairwise comparison between GHG emission reduction (SDG 13) and other SDGs was carried out. A total of 427 observations were classified as either synergy (170), trade-off (176), or no effect (81). Most synergies with environmentally-related SDGs, such as water quality and biodiversity conservation, were observed when perennial crops were produced on arable land, pasture or marginal land in the ‘cool temperate moist’ climate zone and ‘high activity clay’ soils. Most trade-offs were related to food security and water availability. Previous land use and feedstock type are more impactful in determining synergies and trade-offs than climatic zone and soil type. This study highlights the importance of considering context-specific conditions in evaluating synergies and trade-offs and their relevance for developing appropriate policies and practices to meet worldwide demand for bioenergy in a sustainable manner.

09 BIOMASS FUELS↗

DOE OSTI2021

Impoverishing Roots Will Improve Wheat Yield and Profitability Through Increased Water and Nitrogen Use Efficiencies

We report that more than a 60% increase in crop production is required by the 2050's to feed a growing world population. Understanding how plant functional traits and field management affect crop yields has the potential to improve agricultural productivity, minimize economic and environmental losses, and maximize food security. We explored the influence of winter wheat root characteristics and management on winter wheat growth, yield, and profit using a mechanistic and well-tested ecosystem and crop model, ecosys. We applied and further tested ecosys at an agricultural farm growing winter wheat in Ardmore, Oklahoma, United States. The model accurately predicted observed shoot carbon (R 2 = 0.95), soil moisture (R 2 = 0.67), soil temperature (R 2 = 0.91), and yield (percent error = 17%). Numerical optimization experiments were conducted to explore potential improvements of winter wheat yield and profit by modifying root characteristics, including root radius and root:shoot carbon transfer conductance, and fertilizer inputs. Our results show the potential for simultaneously improving winter wheat yields and profits. The optimum conditions were found to be in the range of root radius between 0.1 and 0.3 mm, carbon transfer conductance between 0.004 and 0.01 h -1 , and the currently applied fertilizer rate of 112 kg ha -1 . Under these conditions, improvements in yields and profits of up to approximately 25% and 110%, respectively, were modeled compared to those under baseline root traits. These improvements were achieved by impoverishing root structures, thereby increasing nutrient allocation to grains. Our results also demonstrate and motivate model structures that integrate the complex network of plant physiology, soil nutrient biogeochemistry, hydrology, and management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantifying Carbon Cycle Extremes and Attributing Their Causes Under Climate and Land Use and Land Cover Change From 1850 to 2300

The increasing atmospheric carbon dioxide (CO 2 ) mole fraction affects global climate through radiative (trapping longwave radiation) and physiological effects (reduction of plant transpiration). We use the simulations of the Community Earth System Model (CESM1-BGC) forced with Representative Concentration Pathway 8.5 to investigate climate-vegetation feedbacks from 1850 to the year 2300. Human-induced land use and land cover change (LULCC), through biogeochemical and biogeophysical processes, alter the climate and modify photosynthetic activity. The changing characteristics of extreme anomalies in photosynthesis, referred to as carbon cycle extremes, increase the uncertainty of terrestrial ecosystems to act as a net carbon sink. However, the role of LULCC in altering carbon cycle extremes under business-as-usual (continuously rising) CO 2 emissions is unknown. Here we show that LULCC magnifies the intensity, frequency, and extent of carbon cycle extremes, resulting in a net reduction in expected photosynthetic activity in the future. We found that large temporally contiguous negative carbon cycle extremes are due to a persistent decrease in soil moisture, which is triggered by declines in precipitation. With LULCC and global warming, vegetation exhibits increased vulnerability to hot and dry environmental conditions, increasing the frequency of fire events and resulting in considerable losses in photosynthetic activity. While most regions show strengthening of negative carbon cycle extremes, a few locations show a weakening effect driven by declining vegetation cover or benign climate conditions for photosynthesis. Increasing hot, dry, and fire-driven carbon cycle extremes are essential for improving carbon cycle modeling and estimation of ecosystem responses to LULCC and rising CO 2 mole fractions. Moreover, large aberrations in vegetation productivity represent potential and growing threats to human lives, wildlife, and food security.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Representing Global Soil Erosion and Sediment Flux in Earth System Models

Abstract Soil erosion produces enormous amounts of sediment, carbon, and nutrient fluxes from land to rivers, thus playing crucial roles in global biogeochemical cycles and food security. To predict soil erosion in the context of climate and land use changes, we explicitly parameterize cropland management actions (i.e., irrigation, conserved agriculture, and crop residue management) and geological factors (i.e., lithology and glacier) in the Energy Exascale Earth System Model (E3SM) soil erosion module. The erosion model is calibrated using a global‐scale regionalized parameter calibration method. The spatial variabilities of the modeled and the Revised Universal Soil Loss Equation (RUSLE) based benchmark soil erosion are consistent across vegetation, climate and soil properties. Compared with independent data, our model shows a bias reduction in 59% of the observations relative to the RUSLE‐based soil erosion, with 53% of the bias reduction exceeding 50%. This improvement is mainly due to a better representation of the topographic effect on soil erosion. Our results indicate that conserved agriculture practices have effectively reduced soil erosion in cropland by over 25% in the United States and Argentina. In contrast, irrigation has increased soil erosion in many Asian countries. For upland sediment flux, our model is consistent with the WBMsed benchmark data in inter‐basin variability but could be more skillful in simulating intra‐basin variability because it couples soil erosion and sediment flux explicitly. The developed model provides useful skills for more realistic predictions of soil erosion and river sediment dynamics under environmental changes.

58 GEOSCIENCES↗

DOE OSTI2023

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology‐Based Model

Abstract A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub‐Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent‐based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two‐fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi‐permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all‐year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

60 APPLIED LIFE SCIENCES↗