Agricompas

Agricultural Data Analytics: Knowledge and decision support

Blog

Innovation, growth and data analytics

by

By Nicolás González, Business Development Manager

The world is changing. Every day we see how new technologies are being developed and many of us may have come to dread the idea of being displaced by a machine. But that is not the way of things. Technology is meant to make our lives easier, and we believe that the coexistence between traditional methods and newly developed ones is possible, if not meant to be.

In recent years, agricultural data analytics has become one of the top edge tendencies in terms of sustainable development globally. This means that there is a lot of research and a lot of projects currently trying to understand agricultural dynamics and how to use information in order to optimize processes and achieve sustainable objectives.

Although this is a very beautiful statement, the reality growers are facing on a daily basis, and how this information is to be gathered, processed and used, represents a huge challenge not only for farmers but for the whole agricultural value-chain.

Agricompas drones collect images of farms for innovative data analysis

Agriculture in LMIC countries still relies on manual labor. The culture of innovation, technology, insurance, good financial practices and data analytics is still in a juvenile stage.

Nevertheless, governments and private capital are incentivizing fast growth through technology and new services are becoming more common every day.

Innovation and Market Growth

For us as an agro data analytics company, innovation is the only way to break through such barriers and use our knowledge to evolve the growing market. This is our main drive, our oxygen and our compass. As Harvard Business Review’s article: Breaking down the barriers to innovation states:

To us, innovation doesn’t mean mere inventiveness. In our work we define it as: something different that creates value.

As we work with agricultural big-data, we face the challenge of gathering, processing and delivering useful information in the context of market needs and opportunities. By doing this, we look to make our EcoProMIS platform a value generator for growers, governments, and financial and insurance institutions.

Analytics for Decision Support

In order to capture what really drives the agricultural market in terms of financial services, risk management and productivity optimization, our innovation process aims to understand the market and develop tailored solutions that make the decision-making process more efficient, thus giving business intelligence the recognition and merit it deserves for the immense toolkit it provides us with.

We believe that by bringing sustainable agriculture into the digital era, better conditions for growers may be achieved. We believe that when real value is generated it can also be garnered.

We believe that corporate institutions can see a benefit as well, using high quality information and business intelligence, improving their margins, creating new delivery methods, enhancing R&D and, finally, increasing sales.

Our EcoProMIS platform sends farmers notifications using a suite of apps

With the richness of precise, accurate and relevant information, we enable an increase to the market size of agro insurance and provide the much-needed agronomic crop management data necessary for a new and creative product development ecosystem.

The world is changing. And so are we. Breaking through the barriers of convention, we have come to innovate and leave the world in better shape than how we found it. We believe that a new era for sustainable agriculture and analytics has come at last.

Connecting growers with an essential service in the modern world

by

By Richard Strange, Head of Engineering at Agricompas

As individuals we all grow in wisdom and capability when we take time to reflect on our actions and find lessons to apply to tomorrow’s challenges. We look at our achievements and the memories that are anchored around them. We use them to guide us in becoming better in both our professional and personal lives. Modern business is very much the same. In the modern world, when a business moves, the byproduct of their actions is data.

Agricultural Data Gap

Whether it is a financial officer’s log of transactions, the record of work hours from an employee’s timesheet, or the number of clicks a website receives each day. It is rare to find a part of a business that isn’t measured or collected, either directly or by proxy through other measures. Yet in agriculture, little information is available around many crucial farming practices that often mean the difference between a bumper crop or financial devastation for families and communities.

It is not enough to say that you have an employee, or a website, or an invoice. The crucial questions are if the employee is doing their work, if the website is drawing attention, if the invoice is correct. Yet farmers are not able to answer critical questions about their own farms. They have sown their seeds, yet cannot say how many are germinating. They apply fertiliser, yet cannot tell if it is cost-effective. By leaving agriculture behind in this wave of data-driven business, the world is abandoning millions of farmers in data poverty, and powerless to compete against their wealthier first-world counterparts.

EcoProMIS Collects Quality Data

In leading the EcoProMIS project, the aim of Agricompas is to make a difference by empowering farmers with the knowledge they need, from sensor to survey to satellite to weather to drone data. But with each additional source of data, the difficulty of pulling them together increases exponentially. I’m the Head of Engineering at Agricompas, and I’m responsible for all the data EcoProMIS gathers. My job is to work out how we pull all this information together, understand it and then provide the information to those that need it.

There are two approaches to tackling a challenge like ours. Firstly, you can manually handle the data, with a team of analysts pushing round files via email, shared folders and collaborative spreadsheets. This does come with the advantage of immediate productivity and visibility. But there’s little certainty over the quality and completeness of data, and no way to be sure what information is where. The second option is to invest time and effort into a fully-fledged platform for data. It must allow the scientists we work with and the farmers that we support to put in and take out the information they need effortlessly.

Advanced Data Platform Prevents Errors

Only recently, the failure of the first, manual approach was highlighted by the loss of the records of 16,000 positive COVID-19 cases by the UK government. Was it a catastrophic server failure? the act of a malicious hacker? The truth was far more mundane. An analyst had opened the spreadsheet holding the list of COVID-19 cases in an old version of Excel, slicing 16,000 rows of data off without ever realising their mistake. Suppose this approach cannot work reliably in the hands of a team as well-staffed as the Public Health England team. How can we trust our own information in a similar system? We owe our growers and our own team better than that.

The cost of getting your data platform wrong (https://www.bbc.co.uk/news/uk-54412581)

Over the last six months, the EcoProMIS team has been carefully creating a central platform that can look after farmer data responsibly and safely. A system of databases, redundant servers and security measures means that data doesn’t get forgotten, doesn’t get destroyed and doesn’t get leaked. Over the coming months, we are combining our suite of analytics, models and AI with new apps.

These apps will allow farmers to provide and see their data about their farms and help them make the right agricultural decisions. We already have the first app in early tests, with a knowledge presentation app in the works for release by the new year.

Connected Growers

As we evolve our platform and grower apps through close feedback with early users, we will be able to put more power back into the hands of growers, irrespective of their literacy or agricultural experience.

Agricompas and the EcoProMIS project exist to level the playing field and make agriculture fairer for farmers in the most challenging economic, environmental and social settings. I am incredibly proud of what our technical team has achieved to make that happen.

Data Analytics Make Sense

by

By Simon Dzurjak, Data Analyst at Agricompas

With the advent of the big data era, the way we gather insights about processes affecting our everyday lives has changed dramatically, and the domain of agriculture and food production is no different.

As a result of the widespread availability of various sensors, from satellites and UAVs, to climate and soil measurement hardware on farms, we can now gather data at unparalleled rates and volumes. However, in its raw state, the data we collect often provides little, if any, insight. These raw measurements need to be processed and analysed through complex pipelines to be turned into a valuable product.

The engine responsible for driving the change of raw data into worthwhile insights is data analytics.

Data Analysis for EcoProMIS

Since joining Agricompas in July, I have been working on ensuring that the EcoProMIS data which is stored and arrives on our cloud platform, is as high quality as possible. Analytics and predictions are only ever as good as the data from which they are sourced, hence rigorous checking of data quality is always a necessary layer in data intensive projects. To help with this, I wrote a number of programs generating data quality statistics, which will ensure that any analytics we produce will be reliable and trustworthy.

The statistics produced by these scripts are then visualised and fed into internal dashboards, which provide a quick and intuitive way of keeping an eye on our data and making sure that it looks the way we expect it to.

Data analysis is an essential part of our work on the EcoProMIS project.

Eddy Covariance Data

So far, one of my most enjoyable (but challenging) experiences was working with large eddy covariance tower datasets. Eddy covariance towers are brilliant at logging and producing rich atmospheric chemistry flux datasets allowing us to understand the minute gas exchange processes in agricultural fields. This data is important for our crop modellers, allowing them to produce various models capable of predicting crop phenology and yields.

However, due to the way that the hardware transmits the data, it is common for gaps in data to form (e.g. as a result of signal loss). Luckily, with the power of statistics, we can accurately discern the missing values and fill the gaps in the data. Working closely with the crop modelling team, after many discussions and much research, we have successfully automated this process, and allowed ourselves to further iterate and improve on it if need be in the future.

Integrating Diverse Data

As for the future, I also look to my past and hope to bring some of my previous experience in working with remote sensing and Earth observation data into our platform. Integrating and tying all the data types together will be a big challenge, however I am certain that it will bear fruit and truly create a platform which will make farming easier, more precise and more sustainable.

With advances in computational power and the high quality data available to us, I believe that data analytics and artificial intelligence will play a key role in revolutionising agriculture, and it is rewarding to be a part of a team making this happen. Combining multiple sources of data together into a single product will allow us to create a platform capable of supporting decisions via accurate insights and improving the ease with which farmers grow crops like never before.

Why the eddy covariance technique is an ally in the search of sustainable agriculture

by

By Agricompas Crop Model Team

In recent decades, agriculture has been under the scrutiny of society and the scientific community due to the negative impact that it generates on the environment. These impacts are of many types, including deforestation, eutrophication of water bodies, the reduction of biodiversity due to the intense use of pesticides, and the emission of greenhouse gases (GHG).

In relation to GHG emissions, these are released in the process of manufacturing inputs, such as, fertilizers. Also included are the GHGs released as a result of the transport process: first of inputs towards the production areas, and then of the product towards the consumption areas.

An eddy covariance system recording greenhouse gases emissions on a commercial rice field at Colombia for EcoProMIS project. (Agricompas)

A Complicated Task

But the most complicated task from a methodological point of view is to determine the GHGs that are released during the production stage. Among the GHGs released to the atmosphere during the production phase, the most important are carbon dioxide, methane (in systems where the soil is in anaerobic conditions), nitrous oxide, and ammonia.

Methodological difficulties are associated with the fact that these emissions are determined by dynamic factors such as climate, soil characteristics, and management practices, especially fertilization and irrigation.

Since it is impossible to survive without agriculture, efforts have focused on developing and implementing production systems able to maximize yields while reducing negative effects on the environment. A prerequisite for advancing in this direction is to measure the GHGs generated during agricultural production cycles.

Static Chambers

To understand better the methodological challenges involved in determining these gases under field conditions, let us take methane as an example. This gas is generated as a product of the decomposition of organic matter in the soil under non-oxygen conditions, typical of crops such as flooded rice.

Traditionally, static dark chambers have been used to collect samples that are later analysed by the gas chromatography technique in specialized laboratories.

This technique has a high sensitivity to determine low methane fluxes, is easy to handle, and has a low cost. But its main disadvantages are related to the low spatial representativeness and the inability to generate data at different time scales.

In other words, the measurements only represent the gas flux in a small area and at a specific time point, which leads to the question: can this technique generate data to represent what happens in inherently heterogeneous and dynamic agricultural systems?

Eddy Covariance

It is in this context that the technique of eddy covariance appears, as an alternative way to measure, among other variables, methane flows with greater spatial and temporal representativeness.

This technique employs a complex assembly of sensors arranged in a tower (which is why they are usually called eddy covariance towers) that records variables that ultimately allow the determination of the exchange of gases and energy between the crop and the atmosphere.

Although the foundations of the technique and data processing are complex, it provides useful information in the search for more sustainable agricultural systems.

This is because, in addition to determining GHG emissions, such as methane and carbon dioxide, the eddy covariance technique also provides information about the flow of energy between the soil, the plant, and the atmosphere. This means that information is also useful to improve the water use efficiency since the measurements allow the determination of water fluxes from the crops to the atmosphere (evapotranspiration).

All of this information is comparable in terms of accuracy with data obtained by reference instruments such as lysimeters. Therefore, the technique of eddy covariance is currently a powerful ally in the search for more sustainable agricultural systems.

Use with EcoProMIS

The EcoProMIS project has four eddy covariance towers in Colombia, two recording data on rice crops, and two on oil palm crops. The data collected by these stations are being processed to calibrate crop models that allow, in addition to predicting yields, to estimate GHG emissions.

Together with our partners (CIAT, Cenipalma, Fedearroz, IWCO, Pixalytics and Solidaridad), the final objective of the project is to generate “knowledge and decision support” to orient stakeholders towards sustainability.

Measuring the real impact of EcoProMIS

by

Editor: In this article, the EcoProMIS Monitoring and Evaluation specialist explains how and why we are measuring the impact of our rice and oil palm project in Colombia. Written by Elizabeth Sweitzer, CIAT.

Often in the realm of research for development there is a tendency to focus on results. Indeed, this is the focus of results-based management (RBM), arguably the leading management methodology for international development. In an effort to uphold accountability and transparency (especially in the face of fiscal austerity), RBM defines clear results and products and demonstrates how interventions achieve those results.

But results are just the beginning. Results tell us what products and services the project provided. In order to tell the full story, we also need consider how these results generate and sustain impact. Impact on the other hand explains the changes in behavior we see as a result of those products and services, and how they contribute to holistic improvements for individuals, communities and landscapes.

Why measure impact?

Simply put: we want to tell the full story.

It’s one thing to report on the number of farmers that participated in EcoProMIS trainings – a result. When we measure or forecast impact, we explain how those farmers used those trainings to improve their productivity and livelihoods. We learn about how knowledge services shared on mobile applications help to improve on-farm management decisions. We can estimate how financial savings amongst farmers help improve gender equity, household nutrition, and education outcome.

And that’s where I come in. I’m Elizabeth Sweitzer, the Monitoring and Evaluation Specialist for EcoProMIS. Known by most as an “M&E Specialist”, my work revolves around assessing performance. I am based at CIAT in Cali, Colombia. I help EcoProMIS measure the progress of interventions, the efficacy of knowledge services we provide to farmers, and more to help tangibly understand results and impact.

My work also helps us understand the nature of results, for example were they positive or negative? Intended or unintended? What was learned in the process, what will we change moving forward? In doing so, I help to understand, forecast or even measure the impact of the project.

How do we measure impact?

During the project, we keep robust monitoring systems verified by a ‘logframe’ (logical framework) to test the attainment of our results. Process evaluations keep track of how we are doing, and provide opportunity to pivot course where needed. Learning questions are defined and keep us on our feet, helping us reflect. We also plan to be able to tell the “happily ever after”, by developing plans to measure the sustained impact of our interventions, how people change over time and what practices they truly adopt and make habit of.

EcoProMIS team conducting interviews with growers

Measuring impact with EcoProMIS

Working alongside a team of researchers, implementers, managers, extension agents and more, we work to find dynamic tools and mechanisms to measure this impact for a number of different stakeholders. As EcoProMIS is made up of a dynamic team of specialists with backgrounds in socio-economic, environmental, business, and management backgrounds, we possess dynamic ways of measuring impact.

Our metrics come from a host of different qualitative and quantitative studies, and are promoted by an idea that iterative monitoring and evaluation is essential throughout the project lifecycle and even after. We look forward to measuring lasting impact and helping farmers develop sustainable, profitable, and productive lifestyles.

How can Value Chain Analysis be used to make cocoa farming more sustainable?

by

A smallholder farmer in Colombia cut opens a cocoa pod with a machete. Each pod contains around 20 to 30 seeds. (photo credit, FEDECACAO)

By Deborah Foy, Agricompas

Over the past few months, the team at Agricompas has been working closely with FEDECACAO in Colombia to conduct value chain analysis for cocoa. This blog introduces the concept of value chain analysis and highlights how value chains can be used to make cocoa farming more sustainable.

What is Value Chain Analysis?

At its heart, value chain analysis describes the pathway of cocoa from bean to bar. It extends all the way from pre-farm input providers right through to end consumers, linking different actors in the chain along the way and highlighting where there are both threats and opportunities.

Typically, the main segments of a value chain are summarised in a value chain map. This map becomes a graphical representation of the value chain that showcases the functions of stakeholders, their relationships and the agents supporting the process. A simplified value chain map for cocoa is shown below:

The Main Stages of a Cocoa Value Chain

A typical cocoa bean value chain involves the operation of four major segments – Primary Production, Commercialisation, Processing and Manufacturing, and Market.

1. Primary Production

Farmers are at the primary production stage of cocoa-based supply chains. In Colombia, this group encompasses over 52,000 cocoa smallholders. Whilst the area planted by the farmers varies between regions, a typical cocoa farm in Colombia is grown on an average plot size of less than three hectares. Farmers in the primary production stage can be classified in many ways, including:

  • Farm management practices (e.g. diversification of production)
  • Percentage of shade cover
  • Whether they are certified
  • Whether their production methods are traditional or there is a high degree of technification

2. Commercialisation

Commercialisation can include individual purchasers, producer associations such as our local partner, the Federación Nacional de Cacaoteros (FEDECACAO), and the purchasing agents of large firms. This stage typically includes transportation, quality control, fermenting and drying, as well as differentiation (e.g. certification of producers that have met criteria of an independent evaluating entity).  Some agents active in the commercialisation of cocoa – including our partner FEDECACAO – are involved in technical assistance and in the distribution of farming inputs such as fertiliser.

3. Processing and Manufacturing

In this stage, cocoa moves from commercialisation to firms involved in the transformation of cocoa and in the production of cocoa-based products (e.g. chocolate bars and cosmetics). In Colombia, two large domestic companies – Casa Luker and Nutresa – dominate the processing and manufacturing of cocoa. Together, they purchase between 80-90% of Colombia’s cacao production. Most central cocoa traders in Colombia have commercial relationships with one or other of these two major chocolate companies.

4. Markets

In this last stage, the processed cocoa is sold for internal (domestic) consumption or sent to export destinations. Colombia is unusual in that 81% of the final product is sold domestically, largely for drinking chocolate preparations (FEDECACAO 2019). However, exports of Colombia cocoa have been increasing since 2012. The country currently exports fermented and dried cocoa beans, shells and cacao residues, cacao paste, cacao butter, cacao fat or cacao oil, cacao powder without the addition of sugar and other food preparations containing cacao to around 72 countries.

Colombian exports of cocoa beans 2008-2018 in metric tons (FEDECACAO, 2019)

How is Value Chain Analysis Useful for Sustainable Agriculture?

Value chain mapping has many uses. Key among them are the following:

  • Identifying points of economic inefficiency: This includes economic analysis of points in the chain where resources are not being used in an optimal way.
  • Identifying the value added at each step of the chain: In cocoa, value added at each step of the value chain is typically unequally distributed – meaning that benefits from price increases tend to benefit the agroindustry more than smallholder farmers.
  • Identifying smallholder farmers who adopt practices that lead to sustainable agricultural outcomes: This can improve productivity through the creation of price premiums and incentive structures. The value chain can also help large firms to promote climate smart agriculture at primary production stage (e.g. through the reduction of greenhouse gas emissions).
  • Identifying how value chain links can be improved: Better value chain links (e.g. between producer associations and buyers) can also improve farmer access to extension services, information about prices and certification, as well as inputs for climate adaptation and resiliency.

In Colombia, there are multiple forces that shape the supply and demand of cocoa. At Agricompas, we’re using value chain analysis tools to map out the roles and responsibilities of different actors involved in the sector. We’re using these tools to gain insight into the net income derived by farmers and other stakeholders from cocoa production and trade. Ultimately, understanding how the value chain works will help us to identify ways to make it more sustainable.