Project Brief: VCS Agroforestry Across Three Indian States​

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The project implements reforestation across 13,500 hectares of degraded private farmlands in Andhra Pradesh, Telangana, and Orissa — three states with substantial areas of land that have degraded under decades of rainfed subsistence agriculture, leaving soils depleted, yields declining, and farming families caught in a low-productivity trap with limited options.

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The model is a deliberate attempt to break that trap. Resource-poor smallholder and marginal farmers who would otherwise continue low-productivity subsistence agriculture are offered a pathway to raise tree plantations on their land, linking them to end users of wood products — primarily the paper industry — through a coordinated supply chain that involves agronomists, industry partners, social enterprises, and NGOs.

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The project implements a range of environment-friendly agroforestry practices and Natural Climate Solutions across the participating farmlands. Various sustainable land management techniques — agroforestry, crop rotation, composting, integrated pest management, cover cropping, drip irrigation — are introduced alongside tree planting, building soil health and agricultural productivity in parallel with the growing carbon stock.

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The carbon accounting follows VCS methodology AR-ACM0003, with emission reductions calculated through quantification of Above Ground Biomass, Below Ground Biomass, and Soil Organic Carbon. The project is estimated to generate approximately 94,200 VCS credits per year on average across a 20-year crediting period — a meaningful volume that reflects both the scale and the long-term commitment involved.

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What makes this project distinctive in the agroforestry carbon space isn't the carbon methodology — AR-ACM0003 is well-established — it's the participation model. Small and marginal farmers aren't beneficiaries of this project in any passive sense. They're the people implementing it, managing the land, making planting decisions, adopting new practices, and ultimately determining whether 13,500 hectares of degraded farmland becomes a functioning carbon sink or remains degraded. Their engagement, knowledge, and continued participation over 20 years is the most important variable in the entire project.

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That's why the monitoring framework goes well beyond tree counts.

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Why Smallholder Farmer Projects Need Three Layers of Monitoring

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Most agroforestry MRV frameworks focus primarily on the carbon layer. Species planted, numbers recorded, growth measurements over time, biomass calculations. That's what the methodology demands and that's what verifiers examine most closely.

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But a project operating on smallholder farmland across three Indian states, involving resource-poor farming families who are simultaneously planting trees and managing their agricultural land, has three distinct monitoring needs that require three distinct survey types.

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The first is carbon verification data. What trees were planted, which species, how many, in which location. This is the direct input to the AR-ACM0003 calculation. It needs to be GPS-verified, species-specific, and counted accurately.

The second is sustainable practice documentation. Agroforestry projects that simply plant trees without changing the underlying agricultural practices are fragile. If farmers are simultaneously depleting soil through unsustainable cropping whilst trees grow alongside, the long-term land health that supports both carbon permanence and project viability is undermined. Documenting which sustainable practices are being adopted — crop rotation, composting, integrated pest management, cover cropping — provides evidence that the agricultural intervention is working, not just the tree planting component.

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The third is community wellbeing monitoring. This is the layer most commonly absent from VCS agroforestry MRV, and it's the one that co-benefit claims, SDG assertions, and increasingly sophisticated carbon buyers are starting to examine most carefully. Are the smallholder farmers actually better off? Has household income improved? Has food security changed? Is the conservation project affecting their economic circumstances positively, negatively, or not at all?

The three surveys described below address each of these layers in sequence, creating a monitoring package that supports the carbon calculation whilst simultaneously building the community co-benefit evidence base that distinguishes high-quality credits from basic ones.

The Tree Planting Survey: Verifying What Was Planted, Where, and When

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The Tree Planting Survey is the primary carbon verification instrument. It captures the field data that feeds directly into the AR-ACM0003 biomass calculation and provides the GPS-tagged planting records that verifiers need to confirm that reforestation is happening where claimed, with what species, in what numbers.

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Survey configuration: GPS location enabled. Green colour coding with Nature icon for visual identification on the platform. Continuous survey type — not one-time — allowing farmers to submit records across the planting season as activity occurs rather than in a single retrospective batch. 20 reward points per submission. Currency reward of 5 units per submission enabled, providing direct incentive for consistent data collection. Survey active from September to December 2025.

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Survey Field 1 — Common Tree Species (Multiple Choice, custom input allowed): Options include Teak (Tectona grandis), Neem (Azadirachta indica), Mahogany (Swietenia mahagoni), Acacia (Acacia spp.), Baobab (Adansonia digitata), Pine (Pinus spp.), Eucalyptus (Eucalyptus spp.), and Other.

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The species list is significant. These aren't generic options — they reflect the actual species palette relevant to agroforestry in the target states, including both commercial species like Teak and Eucalyptus that feed the paper industry supply chain, and ecologically significant species like Neem and Acacia with demonstrated benefits for soil health and biodiversity. The inclusion of scientific names alongside common names matters for carbon verification: AR-ACM0003 requires species-specific allometric equations for biomass calculation, and species identity at the point of planting creates the audit trail that connects field records to the equations used in the carbon calculation.

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Custom input is allowed, which is important in a multi-state project where local naming conventions vary. A farmer in Orissa might name a species differently than one in Andhra Pradesh. The custom input option captures planting that doesn't fit the preset list without forcing farmers to select inaccurate categories.

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Survey Field 2 — Number Planted (Value, Required): A numeric required field. The count of trees planted in this submission. Required status means this field cannot be skipped — every submission must include a tree count. This is the most basic carbon verification data point and making it mandatory ensures there are no records without it.

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Survey Field 3 — Photos and Videos Upload (Photo/Video): Photo and video quality set to Low (640x480, 50% quality). This is a deliberate and important design decision. Many of the farmers participating in this project are located in rural areas of Andhra Pradesh, Telangana, and Orissa where mobile data connectivity is limited and expensive. Low quality image compression means GPS-tagged photographic evidence of planting activity can be uploaded even on slow or intermittent connections, without consuming data that farmers can't afford. The visual evidence is preserved; the bandwidth burden is minimised.

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Survey Field 4 — Audio Recordings for Birds (Audio Recording): Audio quality set to Low (16 kbps, mono). Bird audio recording in a tree planting survey might initially seem unexpected. It's actually one of the more thoughtful elements of the monitoring design. Bird species presence and diversity is a well-established indicator of ecosystem health and habitat quality.

 

As planted trees mature, returning bird populations indicate that the reforested areas are developing genuine ecological function, not just carbon stock. For a project claiming biodiversity co-benefits alongside carbon credits, audio-recorded bird species provide independent ecosystem health evidence. Low audio quality settings again reflect the connectivity constraints of rural field data collection — 16 kbps mono audio uploads successfully where higher quality recordings might fail.

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The Agriculture Survey: Documenting the Farming Practices That Make Agroforestry Work

Tree planting and agricultural practice improvement don't happen in isolation in an agroforestry project. They happen on the same land, managed by the same farmers. The Agriculture Survey captures the agricultural side of the intervention — what crops are being grown, what sustainable practices are being implemented, and how the farming system is evolving alongside the growing tree plantation.

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Survey configuration: GPS location enabled. Orange colour coding with Star icon — visually distinct from the Tree Planting Survey on the platform dashboard. Continuous survey type. 25 reward points per submission — slightly higher than the Tree Planting Survey, reflecting the greater complexity and time required to complete it. Currency reward of 5 units per submission. Same timeframe: September to December 2025.

 

Survey Field 1 — Common Crop Species (Multiple Choice, custom input allowed, Required): Options include Rice (Oryza sativa), Maize (Zea mays), Cassava (Manihot esculenta), Millet (Pennisetum glaucum), Sorghum (Sorghum bicolor), Oil Palm (Elaeis guineensis), Wheat (Triticum aestivum), and Other.

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The crop list reflects the actual staple and cash crop landscape of the three target states. Including scientific names alongside common names serves the same purpose as in the tree survey — it enables precise identification that can be matched against regional agricultural data for verification purposes, and accommodates the naming variation across Andhra Pradesh, Telangana, and Orissa where the same crop may be referred to differently.

The required flag on this field means every agriculture survey submission must include crop identification. This is the baseline variable that contextualises everything else in the survey — sustainable practice adoption means different things depending on whether the crop is rice, maize, or sorghum.

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Survey Field 2 — Number Planted (Value, Required): A numeric count of planting events or individual plants in this submission. Required, ensuring no survey is submitted without quantifiable planting data.

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Survey Field 3 — Photos and Videos Upload (Photo/Video, Low quality): Same low-quality setting as the tree planting survey, again reflecting rural connectivity constraints. GPS-tagged photographic evidence of agricultural activity provides the visual audit trail that connects reported practices to real field locations.

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Survey Field 4 — Audio Recordings for Birds (Audio Recording, Low quality — 16 kbps mono): The same biodiversity monitoring function as in the Tree Planting Survey. In the context of agricultural land, bird species data provides evidence about the ecological health of the farming environment and whether the transition to agroforestry practices is supporting biodiversity alongside productivity.

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Survey Field 5 — Sustainable Farming Practices Implemented (Multiple Choice, custom input allowed): Options include Crop rotation, Organic farming, Agroforestry, Integrated pest management (IPM), Composting, Drip irrigation, Cover cropping, and Other.

This is the most analytically valuable field in the Agriculture Survey for co-benefit verification purposes. It directly documents which sustainable land management practices the project is introducing and which farmers are adopting. The list covers the spectrum of practices that the project implements — from water management (drip irrigation) to soil health (composting, cover cropping, crop rotation) to biodiversity-friendly pest control (IPM) to the agroforestry integration itself.

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For a VCS AFOLU project claiming Improved Forest Management and Agricultural Land Management co-benefits, this field provides the documented evidence that sustainable practices aren't just described in the project design document — they're being recorded at farm level by the people implementing them.

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Multiple selection is available, which matters because farmers typically implement several practices simultaneously. A farmer might be doing crop rotation, composting, and drip irrigation together. Single-choice formats would force an artificial selection between practices that are genuinely co-occurring.

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The SDG 1 Survey: Measuring Whether Conservation Is Actually Reducing Poverty

The SDG 1: No Poverty Survey is the community wellbeing layer that transforms this from a standard agroforestry MRV package into a comprehensive co-benefit documentation system.

It addresses a question that sits at the heart of every smallholder-focused carbon project: does participating in a conservation initiative actually improve the economic circumstances of resource-poor farming families, or does it primarily generate carbon credits whilst leaving household poverty largely unchanged?

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For a project explicitly designed to improve livelihood opportunities for resource-poor farmers — linking them to wood product markets, introducing sustainable farming practices, providing access to carbon revenue — this question isn't peripheral. It's the central claim of the entire project design.

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Survey configuration: GPS location enabled. Blue colour coding with Person icon — visually signalling that this survey is about people, not land or trees. Critically, this is configured as a One-Time Survey rather than continuous. This is a deliberate and important design choice. Household poverty and income data is a baseline and endpoint measurement — it doesn't make sense to collect it repeatedly like planting records. One comprehensive household survey per participating family creates the baseline against which future outcomes can be compared.

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20 reward points per submission. No currency reward for this survey — consistent with the principle that community wellbeing data should be reported honestly, not incentivised so heavily that it creates pressure to report positively.

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Survey Field 1 — Household ID (Text, optional): An optional text field for a household tracking identifier. Optional status is important — anonymous participation is protected. Households that want to be tracked over time can provide an ID; those who prefer to remain fully anonymous can skip it. This flexibility is essential for the honest data quality that community wellbeing surveys require.

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Survey Field 2 — Name of Head of Household (Text, optional): Also optional. Same principle — voluntary identification, not mandatory disclosure.

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Survey Field 3 — Number of Household Members (Value, Required): A required numeric field. Household size is essential context for interpreting income and expenditure data — a monthly income of X means something very different for a household of three than one of eight. This field ensures every record can be appropriately contextualised.

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Survey Field 5 — What Is the Main Occupation of the Head of Household (Text, Required): A required open text field. Open text rather than multiple choice for occupation captures the reality that rural household occupations are often hybrid and don't fit neat categories. A farmer who also does seasonal wage labour and some small trading will describe their situation more accurately in open text than by selecting a single category.

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Survey Field 6 — What Are the Main Sources of Income for Your Household (Multiple Choice): Options include Agriculture, Livestock/Fishing, Wage labour (on farms/elsewhere), Small business/Trade, Remittances, Social welfare/Assistance, and Other (Specify). Multiple choice with custom input. This multi-select format reflects the income diversification reality of most smallholder households in rural India — income rarely comes from a single source, and the mix matters for understanding how the conservation project affects overall household economic resilience.

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Survey Field 7 — In the Past Month, What Was the Approximate Total Income (Text, Required): Required text field for approximate monthly household income. The past-month timeframe is practical — it's recent enough to be accurately recalled, and monthly income is the unit most commonly used in poverty threshold assessments.

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Survey Field 8 — In the Past Month, What Was the Approximate Total Expenditure (Text, Required): Required text field for approximate monthly household expenditure. Income and expenditure together give a picture of household financial health that neither alone provides — a household with rising income but also rising essential costs may not be materially better off. The gap between income and expenditure is the measure of economic headroom.

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Survey Field 9 — In the Past 12 Months, How Often Did Your Household [experience food insecurity] (Multiple Choice: Never, Rarely...): A food security frequency question. The 12-month recall period is standard in food security measurement — it captures seasonal variation that a monthly question would miss. For a project working with subsistence farming households, food security is often a more meaningful poverty indicator than income, which can be highly variable and partially non-monetised.

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Survey Field 10 — Has the Establishment of the Conservation Project Affected [your income/livelihoods] (Multiple Choice: Significantly increased, Slightly increased, No change, Slightly decreased, Significantly decreased, Don't know/Too early to tell): This is the most direct impact attribution question in the entire survey package. It asks communities to assess whether the project has affected their economic circumstances — with a response scale that includes negative outcomes (slightly and significantly decreased) alongside positive ones.

The inclusion of negative response options is not just methodologically honest — it's strategically important. A survey that only offers positive and neutral options produces data that verifiers and sophisticated carbon buyers will discount as unreliable. The willingness to collect and report negative responses, if they occur, is what makes positive responses credible.

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The "Don't know/Too early to tell" option is equally important. For households in early project years, it may genuinely be too soon to assess economic impact. Forcing a directional response from people who legitimately can't yet assess the impact introduces noise into the data. Capturing honest uncertainty is better data quality than manufactured certainty.

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Survey Field 11 — In the Past 12 Months, Has Any Member of Your Household [sought outside employment related to the project] (partial text visible): A final question exploring employment creation — whether the project has generated income opportunities beyond the participating farmer's own land, which is one of the stated livelihood benefits of the agroforestry model.

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