Sustainability increases if ethanol is made from sugarcane juice: Study

Sugarcane cultivation has benefitted from entrenched policies that incentivised production for decades

Sugarcane — a cash crop that requires large amounts of water and land to cultivate — has enjoyed immense political patronage and while it was responsible for making India become the second-largest producer of sugar, the country has witnessed a tremendous challenge to its resources.

Producing ethanol from sugarcane juice instead of molasses can help India meet its nutrition requirements and make resources like land and water more sustainable, said a July 24, 2020 study published in journal Environmental Research Letters.

The first-of-its-kind comprehensive analysis of India’s sugar industry — by researchers from Stanford University, United States — showed how the country must now move towards a more sustainable cultivation of sugarcane.

The country’s use of sugar dated back to the 1950s, when it was used for meeting the population’s basic calorie requirements.

These requirements, however, are now fulfilled, with poor populations receiving a full calorie intake as well, according to Rosamund Naylor, co-author and William Wrigley Professor in Stanford’s School of Earth, Energy & Environmental Sciences.

The Union government is, thus, becoming more concerned about nutrition because illness, disabilities and death are caused by micro-nutrient deficiency, prevalent among a large section of the Indian population.

Sugarcane cultivation — which benefitted from entrenched policies that incentivised production for decades — uses up more land and water, and, thus, reduces the use of these resources for foods that are rich in micro-nutrients, said the study.

Most populations can buy sugar at subsidised rates, but do not have access to adequate protein and micro-nutrients that are needed for cognitive growth, said Naylor.

A shift to using the crop as a source of energy generation can, thus, be beneficial for not just increasing access to nutrients, but also help in transitioning to renewable energy.

The Union government’s goal of increasing the ethanol-to-blending rate to 20 per cent by 2030 from a current six per cent can be achieved if it uses sugarcane juice to create ethanol, the study said.

A national biofuel policy that encourages ethanol production from sugarcane juice will help free up land and irrigation water, allowing for the cultivation of food crops that are rich in micro-nutrients.

India’s biofuel policy only recently allowed the use of sugarcane juice in ethanol production, in addition to molasses, the study pointed out.

“If the energy industry continues to use molasses as the bioethanol feedstock to meet its target, it would require additional water and land resources and result in the production of extra sugar,” said co-author Anjuli Jain Figueroa, a post-doctoral researcher in Earth system science.

Using sugarcane juice, however, can allow for the target to be met without needing water and land beyond current levels, said Figueroa.

Government spending to subsidise sugar can also be alleviated, allowing the government to sell sugar below cost, if sugarcane juice is used to produce ethanol, the study pointed out.

The study found sugarcane occupied only four per cent of Maharashtra’s total cropped area, but guzzled 61 per cent of the state’s irrigation water in 2010-11.

“This resulted in about a 50 percent reduction of river flow over that period,” said co-author Steven Gorelick, the Cyrus Fisher Tolman Professor at Stanford Earth. Gorelick also pointed out that the state — which is prone to floods — will find future water management challenging.

There are no reliable sugarcane maps either, pointed out lead author Ju Young Lee, a PhD student in Earth system science. “Using remote sensing data, I am developing current time-series sugarcane maps in Maharashtra – an important step forward,” she said.

The study also pointed out how institutionalised political interests in sugar production have threatened the country’s food, water and energy security over time.


Crop-boosting AI can benefit many fields

A team from the University of Illinois has stacked together six high-powered algorithms to help researchers make more precise predictions from hyperspectral data to identify high-yielding crop traits. Credit: RIPE project.

Machine learning algorithms developed to select high-yield food crops could be applied to ‘hyperspectral analysis’ in other disciplines, from astronomy to espionage

— by University of Illinois at Urbana-Champaign

To help researchers better predict high-yielding crop traits, a team from the University of Illinois have stacked together six high-powered, machine learning algorithms that are used to interpret hyperspectral data. They demonstrated that this technique improved the predictive power of a recent study by up to 15 percent, compared to using just one algorithm.

Hyperspectral data comprises maps of the full light spectrum — not just the visible wavelengths — and has many other applications, from understanding the health of the Great Barrier Reef to tracking the rate of loss of the Amazon rainforest.

“We are empowering scientists from many fields, who are not necessarily experts in computational analysis, to translate their enormous datasets into beneficial results,” said first author Peng Fu, a postdoctoral researcher at Illinois, who led this work for a research project called Realizing Increased Photosynthetic Efficiency (RIPE). “Now scientists do not need to scratch their heads to figure out which machine learning algorithms to use; they can apply six or more algorithms–for the price of one–to make more accurate predictions.”


RIPE for the picking of high-yield crops

RIPE, which is led by Illinois, is engineering crops to be more productive by improving photosynthesis, the natural process all plants use to convert sunlight into energy and yields. RIPE is supported by the Bill & Melinda Gates Foundation, the U.S. Foundation for Food and Agriculture Research (FFAR), and the U.K. Government’s Department for International Development (DFID).

READ MORE:  Crops Are Harvested Without Human Input, Teasing the Future of Agriculture

In a recent study the team introduced spectral analysis as a means to quickly identify photosynthetic improvements that could increase yields. In this new study, published in Frontiers in Plant Science, the team improved their previous predictions of photosynthetic capacity by as much as 15 percent using machine learning, where computers automatically applied these six algorithms to their dataset without human help.

“I’ve loved seeing what’s possible when you can use computational power to exploit the data for all its worth,” said co-author Katherine Meacham-Hensold, a RIPE postdoctoral researcher at Illinois, who led the previous study in Remote Sensing of Environment. “It’s exciting to see what a data analyst like Peng can do with my data. Now other non-data-analyst scientists can test several powerful algorithms to figure out which one will help them leverage their data to the fullest extent.”

Stacks of applications

Further studies will prove the relevance of this stacked algorithm technique to the plant science community and other fields of study.

“By applying the expertise of data analysts to address the needs of plant physiologists like myself, we ended up refining a technique that is relevant to other hyperspectral datasets,” said co-author Carl Bernacchi, a RIPE research leader and scientist with the U.S. Department of Agriculture, who is based at Illinois’ Carl R. Woese Institute for Genomic Biology. “The next step is to test more stacked machine learning algorithms on datasets from many more crop species and explore the utility of this technique to estimate other parameters, such as abiotic stresses from drought or disease.”

“As scientists, we should try to use our domain knowledge to explain advanced performance from machine learning methods,” said co-author Kaiyu Guan, an assistant professor in Illinois’ College of Agriculture, Consumer, and Environmental Sciences (ACES). “Combining computational methods and domain disciplines allows us to possibly unravel what causes the measurable differences in hyperspectral datasets–which is an unsolved mystery in our work and worth future exploration.”

Original article: Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms


How Smart can Agriculture be

There are many issues that need to be addressed to make the local agriculture tech-friendly

Agriculture contributes around 20 per cent of Pakistan’s Gross Domestic Product (GDP). With a population growth rate of 2.4 percent, the country’s population of 210 million poses a threat to food security. A look at the prices of agricultural products shows that vegetables are getting out of the reach of people who are spending a major part of their income on buying food.

A major concern is that the arable land per capita is decreasing due to various reasons, including use of agricultural land for non-farming purposes, degradation of land due to unhealthy agricultural practices, low yields because of obsolete and outdated practices, excessive use of pesticides, low-quality seeds and unfriendly government policies on new technology, etc.

While the developing world is fast adopting agriculture technology, the situation in Pakistan is far from satisfactory. We hear about recurrent crop failures, low yields, and destroyed crops due to extreme weathers and water shortage, etc. Cotton production over the last few years is much lower than the demand is, forcing Pakistan’s textile industry to import it to overcome the shortage. The reasons for this situation include: diseases, water shortage, smuggled hybrid and Bt cotton seeds that do not suit the soil and unsatisfactory performance of agricultural research institutes.

Pakistan is 10 to 15 years behind countries, like Brazil and Argentina in adopting modern tools in agriculture. But this does not mean that Pakistan’s farmers are living in an age of obsolete farming techniques. On the contrary, over 95 percent of the cotton crop in Pakistan is genetically modified (GMO) cotton or Bt. The cotton seed oil from the crop is added to locally-produced edible oil whereas the residual oil-seed cake (khal banola) is consumed by the livestock sector.

Maize crop is also consumed locally in much the same way as GMO cotton. Almost 70 percent of national grain output goes into poultry feed, another 10 percent of it goes into silage for livestock, 10 percent is used in wet milling, 5 percent is utilised as seed for planting fodder and a miniscule 5 percent is consumed directly as flour. As per official figures, 95 percent of the yield is Hybrid Maze, which has increased the yield four times over the last 25 years.

So the question here is what must the government do to adopt technology and apply safeguards if it thinks options like allowing GM seeds in food products are disastrous.

There is a perception among local and international companies that Pakistan is under the influence of local agricultural input companies who do not want to lose their market to the big ones with huge R&D budgets. The tech-based companies, on the other hand, claim that without adopting high-yield seeds, it would be impossible to produce sufficient food to feed the ever-rising population.

Using new technology is difficult for other reasons as well. For example, local farmers complain that they cannot use intelligent drones to spray pesticides on their crops because they have to seek prior approval from the local administration to use them. This is no easy task, they claim.

Dr Muhammad Afzal, Executive Director, CropLife Pakistan — a subsidiary of CropLife Global, explains that growing population, climate change, scarcity of water and changing lifestyles continue to pose challenges to the national food security, emphasising the need to promote sustainable means to grow food and embrace technological innovations that enable the same. These challenges call for a total overhaul of the agricultural production process in our country and taking informed decisions enabled by data and technology.

A major concern is that the arable land per capita is decreasing due to various reasons, including use of agricultural land for non-farming purposes.

Afzal says leading global companies dealing in agricultural inputs are members of Crop Life, which spend around USD 6.1 billion on Research & Development globally. The organisation, he says, was set up in 1968 with the aim to represent research-based companies in areas of crop protection, seeds and biotech. It also claims that in Pakistan 100 percent of the chemistries introduced so far have been developed by Crop Life members which form basis of 3,700 registrations.

Major breakthroughs have come in the form of hybrid seeds that have increased yields and created immunity in plants against excessive heat, water shortage, prolong droughts and so on. Several Pakistani institutes are experimenting with seeds to increase shelf life of agricultural products as well as introduce late varieties, as in the case of mangoes. Longer shelf lives and late arrivals make it possible to send products to longer distances.

Anyhow, things are not as simple as these look on paper and there are certain issues that need to be addressed to make local agriculture tech-friendly. In January 2019, the Ministry of National Food Security and Research (MNFS&R) suspended the GM maize hybrid registration process at its penultimate stage due to which introduction of new varieties was delayed. The ministry raised a wide range of objections, including concerns about GM maize efficacy, health and environmental safety and possible adverse impact on trade to defend its decision.



The industry, however, challenges the claim. It says the objections are recent ones and that the government did not object to adoption of GM which are in use in Pakistan for the last two and a half decades. A representative of a seed company points out that as per the ministry’s own published data, Punjab’s average crop yield was just under 13 maunds per acre in 1992-93, when cultivation of open pollinated maize varieties was the norm. At present, he says, average yields show four-fold increase, largely due to the widespread adoption of high quality hybrid seed and farmer education programmes.

“Over 95 percent of maize area in Punjab has been converted into hybrid maize cultivation and further increase in yields will be marginal unless a new innovative technology is introduced for the purpose,” he claims. As per Pakistan Economic Survey 2018-19, average yields are increasing at a nominal 2.5 per cent annually, a rate insufficient to meet the growing demand, especially of the poultry industry that consumes almost 70 percent of the produce. “Approval of new varieties is a must to meet the fast-growing demand of maize.”

Jens Hartmann, Regional Head for Asia Pacific (APAC) for the Crop Science division of Bayer, says a third of arable land has been lost due to erosion or pollution in the past 40 years, while our production pattern continues to be too resource-intensive. Without plant science and technology, he says, farmers would need an extra 376 million hectares to grow the same amount of food — let alone double the production.

“Since we cannot simply create more farmland at the expense of natural habitat, to grow enough food using less natural resources, we will need to adopt a holistic and integrated approach to agriculture,” adds Hartmann.

The contentious issues notwithstanding, biotechnology has been identified as one of the six priority areas in the country’s science and technology policy. A National Policy and Action Plan for biotechnology was developed and incorporated in the Midterm Development Framework (2005-2010). Advancement in biotechnology is also a part of Vision 2025 and National Food Security Policy 2018.

“At present, there are around 500 scientists conducting biotechnology research with huge teams, 45 universities and R&D organisations working on biotech and a large number of universities teaching biotechnology,” says Dr Muhammad Zafar, adding that the fruits of these endeavours must reach the farmers.

Shahzada Irfan Ahmed


Digital Agriculture: New Tools for Science on the Farm

The value of “connected agriculture” in making life easier for farmers, and helping them reduce the environmental impact of their practices, is no longer unproven. Another of its advantages is beginning to emerge: by producing large amounts of data of near- research quality, it helps align agronomic and zootechnical research closer to farmers’ requirements, and could better inform agricultural public policies. Digital agriculture is a concept which is beginning to reach grass-roots level, as the high visibility of the subject at the Paris Agriculture Fair demonstrated. In the gloomy atmosphere generated by the feeling of “agri-bashing” experienced by many farmers, it was one of the few subjects that gave a positive and attractive picture of recent developments in agriculture.

Digital farming tools were first designed to make life easier for farmers (GPS guidance, herd monitoring sensors), and to help them optimise their farming practices on an environmental level (connected weather stations, crop models used to optimise input usage). They have also strengthened ties with the consumer, who, thanks to the development of traceability and social networks, can now put a face and a name to the food he buys.

More behind the scenes, connected agriculture is also beginning to have a new beneficial effect, which could in future play an even more positive role for the agricultural world: bringing farmers closer to the research world… and as a result to the policy makers who make use of it.

When science has to be done at the farm level

This development is already a reality in some areas of R & D in digital agriculture. To take the example of herd monitoring sensors: they were first developed to detect unusual and clearly identified events, such as detecting temperatures or calving. These initial applications were developed in a conventional research setting, top down from the lab to the field: algorithms for event detection were developed in tests in experimental farms at research or technical institutes, then tested on a small range of farms before being launched commercially.

As these initial applications have reached maturity, research is now focused on analyses of the daily behaviour and welfare of animals: for example, measuring time spent standing or lying, feeding and rumination times. In these areas it is important to detect more subtle changes in the “daily life” of animals, compared to their ordinary activity.

That makes it very difficult to develop this type of algorithm on experimental farms, where the usual activities of livestock (milking, being put out to pasture, etc.) are frequently disturbed by experiments that modify their usual behaviour, and generate movements or immobility that would not happen in a commercial farm. This type of work should therefore be carried out directly in the field, with experiments in controlled conditions being used only as spot checks in a minority of situations. This is an example of an inversion of the classic relationship between scientific experimentation and field data.

From the lab to the vineyard… and back!

Another example of linking research to farmers’ concerns is the use of mechanistic crop models in decision support tools. These models, derived from agronomic research, are increasingly being used for yield forecasting and management of the required inputs (irrigation, fertilisation). Similar epidemiological models are also used to predict the occurrence of diseases or pests threatening crops, in order to position pesticide treatments most accurately.

By design, the result of extensive research in ecophysiology, these models are sufficiently robust and predictive to lend themselves to plausible simulations on the potential effect of changing practices for agro-ecological reasons, or to adapt to climate change. They also have the advantage of objectively quantifying the environmental conditions to which crops are exposed.

Evapotranspiration is a classic example of a simple indicator for measuring crop water demand, which can then be used as a benchmark to check whether irrigation by the farmer has avoided water waste. However, it remains a relatively basic indicator, which is relevant only in the simplest cases: those where we are only seeking to maintain the yield potential by avoiding water deficit for the crop. For some produce, irrigation issues are more complex, because a small, well-controlled water deficit improves the quality of production: the best known case being vines, where the ideal method, defined by the specifications of the wine appellation, aims to create a moderate water deficit during the maturation of the grape, with varying degrees of severity depending on the type of wine you want to produce.

In this case, irrigation management requires much more complex models than a simple evapotranspiration calculation, and they will use not only climate data, but also soil characteristics and the volume of vegetation in the vineyard. At first glance this is once again a top-down approach to the maximisation of research value, from the laboratory to the field. But the use of these models on farms then permits valuable feedback, which will bring the theoretical work closer to the practice of farmers or their consultants.

The Vintel software, developed by iTK in partnership with (among others) INRA and CIRAD, offers a good example of these two-way exchanges between lab and field. Designed to optimise precision irrigation on vines, it is based on a model derived from research work, based on a classic indicator in research of conventional water stress, the basic leaf water potential.

This indicator is the most reliable for measuring the moisture condition of a vine plant, but its measurement is fiddly, which limits its use in vineyards: it has to be measured at dawn with a specific instrument, the pressure chamber. Some wine consultants, particularly in California, use pressure chambers to advise winegrowers. However, they use these measures at noon for convenience, but also to better understand the water deficit of the plot at the time of the day when it is at its height.

This way of measuring is much less common in research, and so originally it was impossible to develop a mechanistic model to simulate it. Vintel was initially released with a model which only estimated the basic leaf water potential. A few years of use of this first version, by consultants expert in the use of the midday potential measurement, then allowed the development of a second model for midday leaf water potential, combining meteorological data and indicators from the base potential, without going through the laboratory process again.

This example clearly demonstrates the new complementarity between research and digital tools for farmers: it is obviously the data from the field that made it possible to develop a model of midday leaf water potential, in line with the habits of winegrower technicians. But that alone would not have been enough to develop a reliable statistical model: only combining them with indicators from a mechanistic model derived from research could lead to the development of a model robust enough to be entrusted to winegrowers and consultants.

“Medium Data” vs Big Data

A few years ago, the explosion of Big Data technologies, and their introduction into the agricultural world, gave rise to a rather binary view split between two scientific approaches:

  • On the one hand, the classic approach of agronomic or zootechnical research, relying on high-quality but relatively sparse experiments, to develop predictive models that can be used in decision support, based on the human expertise of researchers,
  • On the other hand, the new data-centred Big Data approaches applying machine learning techniques (machine learning, deep learning) to massive volumes of data from new sensors deployed in agriculture (combine yield sensors, data collected by milking robots),

The enthusiasm for Big Data was based on the assumption that deep learning would allow the development of reliable predictive models, despite the “noise” generated by the information from the masses of data collected, which exceed what human expertise is capable of analysing. In fact, this hope quickly came up against the major pitfall of machine learning techniques: their lack of user-friendliness…both for end users (farmers or breeders), and service designers! Machine learning certainly now makes it possible to define seemingly satisfactory decision rules or models from any sufficiently large dataset.

But without knowing the “reasoning” underlying these models, even their designers are unable to predict to what extent these rules or models can be used in new contexts: a rather distressing uncertainty when developing new agricultural services beyond the region where they were initially proven, or in new climate situations.

In addition to its sensitivity to unpredictable climate risks, agriculture has another unfortunate feature for machine learning: the real data that can be accumulated on the ground is far from covering all possible combinations of cultivation techniques. The technical strategies used by farmers are influenced by their habits, experience and the expertise of their consultants, and are therefore absolutely implicitly limited by human rationales.  The situation is in this regard completely different from areas such as machine learning applied to games such as chess or go: in the latter case, the algorithm is able, based on the rules of the game, to test all possible and imaginable combinations, even those that a human expert would not think of. In agriculture, artificial intelligence is hampered by the fact that the available data is the result of human reasoning, which prevents it from finding original “solutions” to invent new practices.

The result of these constraints is that purely data-driven approaches are struggling to make a decisive breakthrough in agricultural decision support. The future is undoubtedly, as we have seen from Vintel’s example, the combination of data-driven approaches and mechanistic models to integrate human expertise into Artificial Intelligence. This new vision, hybrid AI, has been chosen as one of the major themes of ANITI, the new Institute of Artificial Intelligence currently being created in Toulouse… and agriculture has been identified as one of its priority areas of application.

This close interconnection between scientific expertise and farm data has an obvious corollary: the need to bridge the gap between Big Data and research data. This is the mission of what can be called “Medium Data”: well-founded data from farms, or at least from plots run under conditions similar to those of farms. Until now, this role of producing intermediate data has been entirely devolved to experiments at agricultural development agencies: technical institutes, chambers of agriculture, cooperatives[4]. Digital agriculture will allow for the emergence of a new category of “medium data”: data of near-research quality, but spread across hundreds or thousands of farms.

Between the scientific data of research, high quality but sparse, and the “Big Data” of sensors embedded on agricultural equipment, connected agriculture allows the emergence of a “Medium Data”: data of near-research quality, acquired on farms, and not small, unrepresentative experimental plots. It is this continuum of data that will fuel hybrid artificial intelligence (a combination of machine learning and mechanistic human expertise), one of the most promising avenues in today’s AI.

Information to ground agricultural public policy

We have seen, with the example of irrigation, that agricultural decision support tools lend themselves well to the creation of objective indicators of crop needs: the same approach is easily transferable to fertilisation, as well as crop protection. Epidemiological models, already used to advise optimal treatment dates for diseases and pests, could also be used at plot level to quantify the still vague and subjective idea of “threat of disease”  Such indicators would be valuable in improving the monitoring of Ecophyto, the plan to reduce the use of pesticides launched in 2010 following the “Grenelle de l’Environnement” debate.

It’s not overstating the case to say that, almost 10 years after its inception, the plan is far from the 50% reduction target (“if possible”) assigned to it: pesticide consumption shows no significant changes on the national average. Even more disturbing, even the plan’s flagship farms, the Dephy network, are a long way from achieving the expected goal. In view of what can hardly be described other than as a failure, the Académie d’Agriculture de France has recently made recommendations to improve the management of the Ecophyto Plan, including the creation of this kind of indicator of health pressure on crops. Digital agriculture could also play a major role in another of the Academy’s proposals: annual surveys of agricultural practices, the only references that can be used to calculate farmers’ pesticide consumption in any detail.

Indeed, the current indicator for the Ecophyto Plan, NODU, is not suited to an agronomic interpretation, which would allow calculation of the potential reduction in pesticide use at farm level. Another indicator, the TFI, would allow for this calculation, although it is currently calculated only every three years, due to the cost of the surveys currently required to collect the data. This still remains the situation, but plot management software enables the automatic calculation of this indicator for farmers who have the equipment. A representative network of farms equipped with this software would therefore allow the annual TFIs to be calculated at a lower cost and cross-linked with the health pressure indicators mentioned above. It should thus be possible to follow up the Ecophyto plan with greater accuracy… and probably to redefine a more realistic set of goals for it, differentiated by crop and region!

Participatory science, which draws on the knowledge of its future users and civil society stakeholders, is one of the key trends in current research. INRA has also been heavily involved in this area. However, much participatory science work remains very asymmetrical: researchers are often the only players putting forward the theories based on the informal and unorganised knowledge of the stakeholders involved in the project. Connected agriculture offers a unique opportunity for farmers to take ownership of research topics that affect them, producing data for themselves which is as understandable for them as for the researchers who will make use of it. Beyond its impact on the daily work of farmers, it therefore has great potential to bring research closer to their needs and enable politicians to better understand their practices. This is how agriculture will be able to meet society’s many expectations of it.