By Tim van den Pol, Portfolio Manager, Farmland Strategy at Van Lanschot Kempen IM
What changes decisions, and why that matters
Farmland investing is increasingly framed through data, dashboards and technology. As scrutiny has grown, investors are less focused on whether data exists and more on where it actually changes decisions.
For investors, this is where the advantage becomes clear. The value is not in having more data, but in using it to challenge assumptions early and filter out weaker opportunities in order to commit capital with a clearer understanding of where the real risks sit.
Outcomes are driven by how land is managed in practice, with crop selection, water availability and operator capability shaping performance across assets and regions. Data is used to narrow options, test assumptions and, in some cases, reject opportunities. The process is less about building a case and more about testing whether it holds up under real-world conditions.
Data does not make the case, it tests whether it holds.
Where data changes decisions in practice
In practice, this shows up in how decisions are structured and tested. Rather than being used continuously, data tends to play a more specific role at key points in the investment process, particularly when determining whether an opportunity should proceed and whether its assumptions hold up.
Before investing, the focus is on whether an opportunity should proceed at all.
What data is used for in the investment process
Turning information into discipline
A credible investment process shows how information moves through decisions. In farmland investing, that typically means structured gates that translate research and modelling into consistent decision-making.
The three-gate solution for farmland
This structure is attractive to investors because it is legible. It shows where data is used, what it is used for, and where challenge is built in. Most importantly, it shows that a deal can be declined even after significant work, because decisions are designed to change when assumptions do not hold.
Crop selection and regional fit
Crop selection begins with what a region can sustain over time, not just what can be grown in theory. Some high- value crops can technically be grown in southern Europe, but water intensity and long-term viability can outweigh potential returns.
A simple example illustrates the discipline. Avocados can be grown in parts of southern Europe, but they are water intensive in a water-sensitive region. In that context, this leads to a decision not to pursue them, with crops better aligned with local conditions often preferred.
These decisions are rarely driven by a single dataset. They come from a combination of climate analysis, market insight and local understanding of how land performs over time. The outcome is a clearer view of what can realistically be owned and managed over time, with unsuitable opportunities filtered out early.
This stage is not driven by automation alone. It combines market data, yield information and cross regional analysis to assess competitiveness. Where analysis suggests a crop performs better elsewhere, the approach is to move on rather than force a fit.
- Crop choices grounded in long-term regional suitability
- Fewer high-input opportunities in water-constrained areas
- Earlier rejection of marginal opportunities
Due diligence: where decisions change
The value of data becomes clearer during due diligence. This is often where the investment case is tested in detail and where assumptions are most likely to shift. What appears viable early on can change once underlying risks are examined more closely.
This is where issues most often emerge in practice. Deals can fall through as risks become clearer, particularly around water, environmental exposure and operator or tenant structure.
Decisions in practice
These examples illustrate how information is used to test assumptions and, where necessary, change the outcome:
- Water risk (New Zealand)
Initial assessment of a proposed permanent crop development suggested that water access had been secured through licensed boreholes. Further investigation, including hydrological analysis, showed the aquifer was connected to protected river systems with minimum environmental flow requirements, which could restrict access in drier conditions. This introduced uncertainty around future water availability. The long-term regulatory position remained uncertain, particularly given the potential for tighter controls on water abstraction. This led to a decision not to invest due to water risk.
- Operator structure (blueberry tenant)
Initial yield assumptions supported a commercial blueberry operation under a tenant structure. Detailed analysis of lease terms, cost base, debt levels and projected cash flows showed the model relied on leased land and debt-funded expansion. Scenario testing of these assumptions under different pricing and growth conditions raised concerns about how downside would be shared if growth slowed or financing conditions changed. The opportunity was declined. The strength of the yield case did not offset structural risk.
- Crop and location fit (Southern Europe)
Initial screening identified high-value permanent crop potential in a southern European region. Further analysis of water requirements, input intensity and local resource constraints highlighted long-term sustainability concerns. Detailed assessment showed the crop required significantly higher water use than is typical for the region, particularly compared with established alternatives such as olives and citrus. A more suitable crop profile was identified, and the original opportunity was not pursued. The mismatch between crop requirements and local conditions outweighed the initial return case.
From decisions to ownership
Once an investment is made, the focus shifts from selection to how assets are managed over time. Data continues to play a role, but its use shifts. It is applied less to support a single decision and more to tracking performance, testing assumptions and maintaining a consistent view across the portfolio.
What matters at this stage is not the volume of information, but how it is used. Information needs to be comparable over time and grounded in how assets are actually performing, rather than presented as a one-off.
This is also where the reality of private markets becomes clear. Yield and cost data is often highly crop-specific and region-specific. It is not always available in a consistent, standardised form, and there is no simple marketplace for buying granular, comparable data at scale. As a result, insight comes from a combination of operator reporting, local partners, ongoing observation and informal exchanges across the market.
This reflects the reality of the asset class. Capability lies in how information is interpreted and applied, particularly when data is uneven or incomplete. The advantage comes from applying that judgement consistently across different crops and geographies.
Farm visits and on-the-ground observation are part of that reality. Seeing operations at harvest time, understanding how packing facilities work, and assessing water systems such as aquifers are practical ways of testing assumptions that no spreadsheet can fully capture.
Technology on the ground
Farmland has always evolved alongside technology, from tractors to increasingly automated and data-supported systems. Today, data and technology are part of day-to-day management, but their value depends on how they are applied in practice.
Where it is used
- Monitoring crop performance and field variability through satellite imagery
- Tracking weather patterns and seasonal conditions
- Analysing climate-related risk, particularly water availability and yield sensitivity
- Supporting reporting and oversight across farms and regions
What it improves
Technology can improve visibility across assets and over time. It can help sense-check conditions against expectations and identify change earlier, particularly when portfolios span multiple regions. It also supports consistency by making it easier to track the same indicators over time, rather than rebuilding reporting each year.
What it does not change
Weather, biology and local conditions still drive outcomes. Operators still vary widely in capability. Data is often fragmented. Technology supports decision-making, but does not replace judgement in practice.
In practice, even relatively simple tools can add value. A basic dashboard tracking rainfall, sunshine and frost conditions can materially improve yield thinking, yet this is not consistently applied across the market. More advanced approaches, such as integrating weather data, satellite imagery and farm-level inputs into predictive yield models, remain unevenly applied rather than standard practice.
There is also growing interest in using data to better understand broader environmental factors such as soil condition, water systems and ecosystem performance. In practice, this remains early stage. The tools are developing, but they are not yet consistent enough to be relied on across investments.
What matters in practice
A credible farmland strategy is not defined by how much data it can report. It depends on how information is used to support decisions.
In practice, the strongest signals are visible in how decisions are made:
- Screening that rules out weak opportunities early, based on crop-region fit, water constraints and competitiveness
- A decision process where modelling is followed by verification, and where due diligence can change the outcome
- Evidence that operator and tenant risk is treated as central, not secondary
- Ownership monitoring that prioritises repeatable indicators and comparability, rather than novelty
- Technology applied selectively to improve visibility, without over reliance on models.
Farmland does not require perfect information. It requires a process that works under imperfect conditions, where discipline and judgement remain central. In that context, data matters most when it changes what is done, not when it adds complexity.
Final observation
The strongest approaches do not treat technology as a story. They use information to narrow choices, test assumptions and manage assets consistently through changing conditions. This is visible across the investment process, from how opportunities are filtered early to how assumptions are tested and, where necessary, rejected.
Over time, the difference becomes clear. Data that is presented but not used rarely changes outcomes. Data that is applied with discipline often leads to fewer investments and a clearer understanding of risk before capital is committed.
That distinction matters. In farmland, outcomes are driven by decisions that play out in real conditions, across crops, operators and environments. The value of data is not in how much is collected, but in how clearly it informs those decisions.
Do you want to know more?
In a world where food security, climate change and biodiversity loss are becoming ever more urgent, the strategic value of farmland is increasing rapidly. In our whitepaper Natural Capital 3.0: A higher standard, we show how sustainably managed farmland portfolios can contribute to long-term returns as well as a positive impact on nature and society.
Disclaimer
This communication is provided for information purposes only and does not constitute investment advice, an investment recommendation, research, an offer or an invitation to buy or sell any financial instruments. It provides insufficient information for an investment decision. Van Lanschot Kempen Investment Management NV (VLK Investment Management) is licensed as a manager of various UCITS and AIFs and authorised to provide investment services, and is subject to supervision by the Netherlands Authority for the Financial Markets (AFM). The information in this document may change without
notice and no rights may be derived from it. Past performance is not a reliable indicator of future results, and the value of investments may fluctuate.
Farmland / permanent crop strategy–specific risks
Investing in farmland and permanent crop strategies involves exposure to illiquid assets, assets that may be difficult to value, and assets subject to agricultural, operational, market and climate related risks.
Returns may be affected by weather events, crop yields, commodity markets, biological risks, input cost volatility, interest rate movements, regulatory developments, and sustainability related factors. Under unusual market conditions these risks can increase significantly, which may limit the ability of the investor or the fund to sell assets at preferred times or valuations. The ability of the fund to respond to market movements may be impaired, and adverse price movements may occur when assets are sold.
Use of investment cases and individual properties
Any references to specific farms, properties, operating partners or other entities are included solely as illustrations of the fund’s investment approach. They do not constitute a recommendation to buy or sell any financial instruments associated with those entities.
VLK Investment Management may hold positions in the companies or assets mentioned.
Use of investment cases and individual properties
Any references to specific farms, properties, operating partners or other entities are included solely as illustrations of the fund’s investment approach. They do not constitute a recommendation to buy or sell any financial instruments associated with those entities.
VLK Investment Management may hold positions in the companies or assets mentioned.
Sustainability considerations Sustainability related objectives and assessments form part of the strategy’s investment process but do not guarantee improved outcomes or fund performance. Sustainability risks may materially affect investment values. Regulatory requirements or market standards relating to sustainability may change over time, which can influence investment decisions, classifications or reported outcomes.
Rights and permissions
No part of this publication may be reproduced or distributed without prior written permission from Van Lanschot Kempen Investment Management NV. All rights are reserved.
