Agrivoltaic Crop Yield Tradeoff Calculator

Balancing Food and Energy on the Same Land

Agrivoltaics combines solar panels and crops on the same piece of land. Instead of converting a whole field into a solar farm, panels are installed so that crops can still be grown underneath or between rows. This creates a tradeoff: you gain clean electricity, but shading from the panels may reduce crop yields.

This calculator is designed to give farmers, landowners, planners, and researchers a quick way to explore that tradeoff using a simple, transparent model. It is not a full feasibility study, but it helps you see how much crop yield might be sacrificed and how much solar revenue could be gained under different design assumptions.

How the Tradeoff Model Works

The calculator uses your inputs to estimate both agricultural and energy outcomes on a field of area A (in hectares). It then compares a baseline "no panels" scenario with an agrivoltaic scenario where part of the field is shaded by panels.

1. Baseline crop yield and revenue

• Field area (A): total cultivated area in hectares.
• Baseline crop yield (Y0): tonnes per hectare under current management without solar panels.
• Crop price: revenue per tonne of harvested crop.

The baseline (no panels) total yield is:

Baseline yield = A × Y₀ (tonnes)

and the corresponding baseline daily revenue is:

Baseline crop revenue = A × Y₀ × crop_price

2. Shading, yield reduction, and shade sensitivity

• Panel coverage (f): percentage of the field area that is covered by solar panels.
• Shade sensitivity factor (s): a value between 0 and 1 that reflects how strongly the crop responds to shading on the panel-covered portion.

The model assumes that the unshaded portion of the field maintains full yield, and the shaded portion experiences a proportional reduction determined by s. In effect, a higher shade sensitivity factor means the crop loses more yield per unit of shaded area.

The resulting total crop yield under panels is estimated as:

Y = A × Y₀ × (1 − (f / 100) × s)

where:

• A = area in hectares
• Y₀ = baseline yield per hectare (t/ha)
• f = panel coverage (%)
• s = shade sensitivity factor (0–1)

3. Solar energy generation and revenue

• Solar irradiance (G): average daily solar energy on a horizontal surface in kWh/m²/day.
• Panel efficiency (η): percentage of incoming solar energy converted to electricity.
• Electricity price (p): revenue per kWh of electricity sold or offset.

The panel-covered area (in m²) is A × 10,000 × (f / 100). The model then estimates the daily energy output as:

E = A × 10,000 × (f / 100) × G × (η / 100) (kWh/day)

The corresponding daily energy revenue is:

Energy revenue = E × p

4. Risk of significant yield loss

To highlight agronomic risk, the calculator includes a simplified risk score for the probability that yield drops by more than 20% relative to baseline. The risk score is computed with a logistic function based on the ratio of current yield to baseline:

Interpreting this score:

• Near 0: low risk that yield falls more than 20% below baseline.
• Mid-range (30–70): moderate risk; panel coverage and shade sensitivity may be pushing limits for that crop.
• High (above 70): substantial risk that agrivoltaic configuration could severely impact yields.

Interpreting the Results

After you click the calculate button, the tool summarizes both agricultural and energy outcomes:

• Adjusted crop yield (t): estimated total harvest under the given panel coverage and crop shade sensitivity.
• Baseline crop yield (t): what you would expect without panels.
• Yield change (%): how much yield is lost or preserved relative to baseline.
• Daily energy output (kWh): approximate electricity production from the panels.
• Crop revenue and energy revenue ($/day): estimated daily income contributions from crops and power.
• Net revenue difference: how much better or worse off you are compared with keeping the field fully in crops.
• Risk score: a qualitative indicator of agronomic risk from shading.

Use these outputs to understand whether the gain in energy revenue compensates for any loss in crop revenue and to see how sensitive your system is to changes in coverage, crop choice, or prices.

Worked Example: Typical Mixed-Use Field

Consider a 1 ha field growing a moderately shade-tolerant forage crop.

• Field area A = 1 ha
• Panel coverage f = 30%
• Baseline crop yield Y₀ = 5 t/ha
• Crop price = $200/t
• Shade sensitivity factor s = 0.8
• Solar irradiance G = 5 kWh/m²/day
• Panel efficiency η = 18%
• Electricity price p = $0.10/kWh

Baseline scenario (no panels)

Baseline yield:

Baseline yield = 1 × 5 = 5 t/day-equivalent (if you treat the yield on a dailyized basis for comparison)

Baseline crop revenue:

5 × 200 = $1,000 (per harvest cycle, or scaled to your time basis)

Agrivoltaic scenario

Adjusted yield under panels:

Y = 1 × 5 × (1 − (30 / 100) × 0.8)

Y = 5 × (1 − 0.24) = 5 × 0.76 = 3.8 t

Crop revenue under panels:

3.8 × 200 = $760

Energy output:

Panel area = 1 × 10,000 × (30 / 100) = 3,000 m²

E = 3,000 × 5 × (18 / 100) = 3,000 × 5 × 0.18 = 2,700 kWh/day

Energy revenue:

2,700 × 0.10 = $270/day

Comparing outcomes

The agrivoltaic configuration reduces crop revenue from about $1,000 to $760 (a $240 loss), but generates roughly $270/day in energy revenue. Even after accounting for lost crop revenue, the net daily position may be positive, depending on how you scale the crop revenue to a daily basis and how frequently you harvest.

By adjusting the inputs (especially panel coverage, crop type/shade sensitivity, and prices) you can see whether agrivoltaics is more attractive financially for your specific situation.

Comparing Scenarios

The table below illustrates how different combinations of panel coverage and shade sensitivity factors can affect yield and energy outcomes qualitatively. These are not exact values from the calculator, but they show general patterns you might observe.

How to Use This Calculator Effectively

• Start with realistic values: Use local yield data, crop prices, and irradiance estimates from reliable sources (e.g., national meteorological services or PV design tools).
• Explore different coverage levels: Run multiple scenarios (e.g., 10%, 30%, 50% coverage) to see how rapidly yield and revenue change.
• Test crop choices: Represent more shade-tolerant crops with lower shade sensitivity factors and compare results against more sensitive crops.
• Use your actual electricity price: Include feed-in tariffs, net metering credits, or on-site consumption savings where relevant.
• Focus on relative changes: Treat the model as an indicator of direction (better/worse) rather than precise financial projections.

Model Assumptions and Limitations

This is a simplified planning tool. It intentionally abstracts away many details of real agrivoltaic systems. When interpreting results, keep the following assumptions and limitations in mind:

• Uniform shading: The model treats all shaded area as having the same effect on yield. In practice, shading patterns vary by time of day, season, panel height, and row spacing.
• No seasonal variation: Inputs like irradiance and yield are treated as averages. The model does not resolve month-by-month or seasonal effects.
• Single crop response: Crop response to shading is condensed into a single shade sensitivity factor. Real responses are non-linear, crop-specific, and depend on management practices.
• Simple energy model: The energy estimate assumes constant performance based on irradiance and panel efficiency. It does not include temperature effects, inverter losses, soiling, or downtime.
• Revenue only, no costs: Investment costs, maintenance, financing, land leasing structures, and policy incentives are not modeled. The tool compares revenues only.
• No environmental co-benefits: Potential benefits such as reduced water use, improved microclimate, or biodiversity impacts are not quantified.
• Site-specific design ignored: Panel tilt, orientation, tracking systems, and structural design strongly affect real performance but are not represented here.

Because of these simplifications, the calculator is best used for preliminary exploration and education. Always complement the outputs with detailed agronomic and engineering assessments before making investment decisions.

Data Sources and Further Reading

The modeling approach here is inspired by published agrivoltaic research that examines crop performance under partial shading and the economics of dual land use. For more rigorous design and evaluation, consult peer-reviewed studies, local extension services, and professional solar developers familiar with agrivoltaic projects in your region.

Use this tool as a starting point for conversations with agronomists, energy planners, and financiers about how to configure agrivoltaic systems that respect both food production and renewable energy goals.