Agrivoltaic Suitability Assessment for the Philippines

While an MS Graduate Fellow under the supervision of Dr. Jeark A. Principe, Head of the Geomatics for Sustainability and Renewable Energy Laboratory at the University of the Philippines Diliman, Jessa I. Quesada focused her research on agrivoltaics as a decarbonization pathway for the Philippines with the following research questions:

This work addresses four research questions submitted to Renewable Energy Focus (Manuscript ID: REF-D-25-00868, under peer review):

• Have existing solar PV projects displaced croplands?
• Since solar PV power potential is highest over croplands, which areas in the Philippine croplands are best suited for agrivoltaics systems?
• What are the crop types in these suitable areas and how would they respond to shading typical in agrivoltaics systems?
• Do existing Philippine policies—particularly those on energy, agriculture, and land use—help or hinder the adoption of agrivoltaics as a decarbonization pathway in the country?

This comprehensive analysis of agrivoltaics potential in the Philippines employed four independent methodological components:

1. Geospatial analysis of existing solar installations and land cover changes to evaluate whether solar projects have displaced croplands
2. Suitability mapping integrating slope, cropland extent, and solar potential to identify areas best suited for agrivoltaics
3. Systematic literature review of crop compatibility with shading to assess which Philippine crops respond positively to partial shading conditions
4. Policy assessment across energy, agriculture, and land-use regulatory frameworks to identify opportunities and barriers to agrivoltaics implementation

• ArcGIS (proprietary): Used for all geospatial analysis and spatial visualization; all workflows transferable to QGIS (open-source).
• Python (open-source): Used for batch processing of geospatial data.
• Google Earth Pro: Used to verify and locate solar power plant coordinates through historical imagery.

• Digital Elevation Model: NASA SRTM (Shuttle Radar Topography Mission) 30-meter resolution. Source: https://dwtkns.com/srtm30m/
• Land Use/Cover Map: National Mapping and Resource Information Authority (NAMRIA, 2015 and 2020 data). Source: https://geoportal.gov.ph/
• Solar PV Output Potential
  • Global Solar Atlas
    • The solar potential map in raster format contains the long-term yearly average of the Philippines’ potential photovoltaic electricity production (PVOUT) in kWh/kWp, covering the period from 2007 to 2018. It has a 30-arcsecond spatial resolution which was resampled to 30-meter spatial resolution. The PVOUT is calculated by Solargis algorithms using the following data: Global irradiation at optimum tilt (GTI) and air temperature (TEMP). Source: https://globalsolaratlas.info/
  • Effective output power potential PV (PPV) map of the Philippines based from the work of Principe et al., (2023) (available upon request at https://www.surelab.dge.upd.edu.ph/contact-us)
    • The PPV map accounted for the AHI-8 shortwave radiation (SWR), ERA5 hourly ambient temperature, dust accumulation based from AHI-8 aerosol property product, and precipitation data to account for natural cleaning effects. We resampled the raster to a 30-meter spatial resolution from its original 5-kilometer resolution.
• Administrative Boundaries: Global Administrative Areas (GADM). Source: https://gadm.org
• Crop Production Data:
  • Philippine Statistics Authority (PSA)
    • Major Vegetables and Root Crops Quarterly Bulletin
    • Major Non-Food and Industrial Crops Quarterly Bulletin
    • Major Fruit Crops Quarterly Bulletin
    • Corn Production Survey
    • Crops Statistics of the Philippines 2019–2023
  • Philippine Rice Research Institute (PhilRICE). Source: https://palaystat.philrice.gov.ph
• Solar Projects Database: Department of Energy list of existing power plants (ground-mounted solar PV facilities). Source: https://doe.gov.ph/sites/default/files/pdf/electric_power/04_LVM%20Grid%20Summary_31_mar_2024.pdf

Solar projects were located using the Department of Energy's publicly available list of existing power plants, cross-referenced with Wiki-Solar (https://wiki-solar.org/) and Google Earth Pro for coordinate verification. A total of 62 ground-mounted solar PV power plants were identified and mapped. Land cover analysis compared the 2015 NAMRIA land cover map (derived from Landsat 8 imagery, 30-meter resolution, 2014-2016) with the 2020 map (derived from Sentinel-2 imagery, 10-meter resolution, 2019-2021). Both use the same 12-category classification scheme (closed forest, open forest, mangrove forest, brush/shrubs, grassland, perennial crop, annual crop, open/barren, built-up, marshland/swamp, fishpond, inland water). The 2015 map was used as baseline to account for pre-construction land cover classifications.

42 of 62 solar projects (67%) were located on croplands according to 2015 land cover data.

The GIS workflow integrated three datasets to identify suitable areas:

• Slope constraint: Only areas with slope ≤ 10° were included, following findings that 10° is the optimum tilt angle for fixed solar panels in the Philippines. This constraint also helps identify sites that optimize solar potential, minimize project costs due to gentler slopes, and are suitable for most crops.
• Cropland extent: Only current croplands (perennial and annual crop classifications) were analyzed
• Solar potential: Effective potential PV (PPV) output map resampled to 30-meter resolution

1. Created binary suitability mask for slope ≤ 10°
2. Isolated existing croplands while preserving hectare values
3. Combined constraints with solar PV output data to produce raster map quantifying potential solar generation capacity for suitable agricultural lands
4. Used Python script to count non-zero pixels in provincial datasets and convert to hectares (30m × 30m pixels = 0.09 hectares each)

10,092,814 hectares total suitable for agrivoltaics across the Philippines (178 times the 56,478 hectares required to meet 2050 solar energy targets). Only 0.6% of Philippine croplands would be needed via agrivoltaics to achieve the country's target of 37.41% solar capacity by 2050.

• Luzon: 4.4 million hectares (43.7%)
• Visayas: 2.2 million hectares (21.8%)
• Mindanao: 3.5 million hectares (34.5%)

• Isabela (467,609 ha)
• Quezon (430,378 ha)
• Negros Occidental (404,617 ha)
• Cotabato (357,996 ha)
• Bukidnon (345,337 ha)

National average annual effective PPV output is 674.18 MW with only 1.08% variation between highest (Mindanao at 677.93 MW) and lowest (Luzon at 670.67 MW), indicating consistent solar potential throughout the archipelago.

Literature Search Methodology: - Reviewed 47 major Philippine crops based on Philippine Statistics Authority classifications: cereals (rice, corn); fruit crops (13 species); non-food and industrial crops (8 species); and vegetable and root crops (22 species) - Search terms: "shading" AND "[crop name]" across peer-reviewed journal articles, reviews, and conference papers - Inclusion criteria: Studies examining shading effects on crop yield - When direct agrivoltaic evidence was unavailable, artificial shading studies were used as proxies given comparable sunlight reduction conditions

• Shade tolerant: Shading improved yield/growth or crop adapted well to shading
• Shade intolerant: Shading decreased yield, growth, or quality
• Conditionally shade tolerant: Crop responded positively under specific conditions (shade levels, cultivars, climates, growth stages, irrigation) but not in all circumstances
• Inconclusive: Mixed evidence or no available literature

10 shade-tolerant crops (primarily perennials): banana, mandarin, tamarind, abaca, oil palm, rubber, taro, and others

9 shade-intolerant crops (primarily vegetables): carrots, cauliflower, eggplant, mung bean, okra, snap beans, sweet potato, cassava, coconut

20 conditionally shade-tolerant crops (mixed annual and perennial): corn, rice, asparagus, broccoli, cabbage, cacao, coffee, ginger, lettuce, mango, onion, orange, peanut, pechay, pineapple, potato, sugarcane, tomato, and others

8 inconclusive: calamansi, cashew, durian, lanzones, mangosteen, papaya, rambutan, watermelon

Perennial crops dominated the shade-tolerant category while annual crops dominated the conditionally shade-tolerant category. This suggests overhead PV systems (suitable for perennials) and interspaced PV systems (suitable for fast-growing annuals with rotational cropping) represent two viable agrivoltaics configurations for the Philippines.

Examined energy, agriculture, and land use policies from three regulatory bodies: Department of Energy (DOE), Department of Agriculture (DA), and Department of Agrarian Reform (DAR). Policies categorized by potential impact on agrivoltaics:

• Helpful: Clear and direct benefits for agrivoltaics adoption
• Potentially helpful: Possible benefits but requiring interpretation
• Potentially hindering: May obstruct or limit adoption

• 3 renewable energy policies are helpful (RA 9513, DC2018-09-0027, DC2017-12-0015)
• 3 land use policies are potentially helpful (though with ambiguities)
  • Revised Rules and Regulations Governing Conversion of Private Agricultural Lands to Non-Agricultural Uses
  • 2002 Comprehensive Rules  On Land Use Conversion
  • Comprehensive Agrarian Reform Law of 1998
• 2 agricultural policies are potentially helpful
  • Agriculture and Fisheries Modernization Act of 1997
  • Reorganization Act of the Department of Agrarian Reform
• 1 policy (RA 9700, prohibiting irrigable land conversion) is potentially hindering

The Philippines has no explicit agrivoltaics-specific policies, creating ambiguity in how existing regulations apply to dual land-use systems. Policy paradox: While energy policies encourage solar expansion and some land use policies allow flexibility, most current development occurs through complete agricultural conversion rather than integration.

This methodology is fully transferable to other countries through these five steps:

• Obtain DEM data (free: NASA SRTM or national equivalents)
• Source land cover maps (free: Copernicus, national mapping authorities, or Landsat/Sentinel)
• Access solar potential maps (free: Global Solar Atlas, PVOUT, or national databases)
• Collect crop production statistics from national agricultural ministries

• Adjust slope threshold based on regional topography and solar panel standards
• Define "cropland" according to local land use classification systems
• Determine solar potential thresholds relevant to local context

• Conduct systematic literature review specific to region's major crops
• Include local agronomic studies and traditional knowledge
• Account for region-specific climate, soil, and environmental conditions

• Execute same GIS workflow (slope mask → cropland isolation → solar potential overlay)
• Use Python scripts for batch processing across administrative units
• Validate results with stakeholder consultations

• Align analysis with local administrative boundaries (provinces, districts, municipalities)
• Aggregate results to policy-relevant scales
• Present findings in formats useful for regional planning

• Processed datasets could be published on online data repositories with DOI for long-term archiving
• QGIS workflow documentation with screenshots could be created as tutorial
• Python analysis scripts could be shared on GitHub with detailed README files
• Regional suitability maps could be made publicly available through web mapping interface

[This manuscript presents the complete methodology, detailed results, and policy recommendations for all four research components listed above.]

Solar energy deployment increasingly competes with prime agricultural lands, creating conflicts between energy goals and food security.  To resolve these competing demands, our study identified where agrivoltaic systems—combining solar energy and agricultural production on the same land—should be strategically deployed across the Philippines.  Using geospatial analysis which integrates terrain suitability, solar photovoltaic (PV) potential, and crop compatibility with shade-tolerant crops, we identified 10.09 million has of cropland suitable for agrivoltaics, representing 81.8% of the nation's agricultural land.  Regions in the Mindanao island emerged as premier agrivoltaic deployment zones, combining maximum crop compatibility (15 shade-tolerant crops), high solar PV potential (683-687 MW), and substantial suitable areas (587,000-715,000 has).  These findings provide actionable recommendations for strategic agrivoltaic deployment that advances both food security and renewable energy goals in the Philippines simultaneously.

The Book of Abstracts can be downloaded here.

Mindanao region in the Philippines faces dual challenges of climate-induced agricultural vulnerabilities and an urgent need to transition from its 68:32 fossil fuel-dominated power mix to 50:50 renewable energy by 2030. Using geospatial data, we assessed the potential for agrivoltaic systems—which combine agricultural production with solar energy generation—across Mindanao's 13.8 million hectare landscape. Our analysis revealed 3.2 million hectares of suitable agricultural land, with SOCCSKSARGEN (21% of total land area) and Northern Mindanao (20.85% of total land area) showing the highest suitability for agrivoltaics. Studies also showed how major regional crops have varying compatibility with agrivoltaic systems: banana (contributed by Northern Mindanao at 27.9% of the Philippine production) and rubber (contributed by SOCCSKSARGEN at 38.8%) demonstrated positive responses to partial shading, while cassava and coconut (contributed by Northern Mindanao at 39.8% and 13.0%) showed negative effects. This study provides a foundation for evidence-based planning of climate-resilient agricultural systems that integrate renewable energy infrastructure while maintaining food security in Mindanao.

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This work was published in the 32nd IIS Forum proceedings (Institute of Industrial Science, The University of Tokyo): "Earth Observation, Disaster Monitoring and Risk Assessment from Space." View the proceedings here.

Analysis of 62 existing solar projects confirmed that 67% have displaced agricultural croplands, validating concerns about the land-use conflict between solar energy expansion and agricultural preservation. The evidence supports the urgent need for alternative approaches like agrivoltaics.

10.09 million hectares of suitable cropland (81.8% of Philippine agricultural land) identified as suitable for agrivoltaics—178 times more area than required to meet 2050 solar targets. Strategic deployment would require only 0.6% of croplands.

Out of 47 major Philippine crops evaluated, 10 are shade-tolerant, 20 are conditionally compatible, and 9 are shade-intolerant. This diversity enables crop-specific agrivoltaics system design for different regions.

Existing policies inadvertently encourage complete agricultural conversion rather than agrivoltaic integration, despite some policies containing language that could be interpreted as supportive. Policy reforms are essential to enable agrivoltaics as a decarbonization pathway.

Mindanao emerges as a premier agrivoltaics zone, combining high solar potential, substantial suitable areas, and excellent crop compatibility. Regional strategies should be tailored to local conditions.

This research identified suitable areas through geospatial analysis and a literature review of crop compatibility. Our findings demonstrate that technical potential exists for widespread agrivoltaics adoption at a national scale. However, adoption requires addressing broader factors including policy reforms, financial mechanisms, public awareness, and crop-specific agronomic research in Philippine contexts.

Future participatory research could investigate how different social, political, technological, economic, environmental, and cultural factors influence adoption readiness across different geographic and economic contexts. Site- and crop-specific field studies are particularly needed to validate our literature-based crop compatibility assessments under actual Philippine agrivoltaic conditions.