Where to find data on different crops

Where to find data on different crops

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Hello I'm not sure if this is the proper place for this question. If not, I am happy to move it.

I'm trying to find a source of data on different crops, is there some governmental database that houses this kind of data? After looking for a couple hours on google and reading a few different articles, I have not really found what I was looking for.

For example, consider the crops maize, wheat, sorghum and sugarcane. I could be looking at the different kinds of carbohydrates an their proportions in these crops, or I could be wanting to know the amount of water needed to produce some yield, what kind of soil each crop likes as well as how different hybrids and mutants have different needs or qualities.

How to find data on infectious disease outbreaks

Over the past couple of weeks I have been investigating the current landscape of publicly available data on infectious disease outbreaks. This data has been reported through several channels. Below is a discussion regarding various datasets, what data is collected and how it is presented, how they are financed, and where they can be found.

1. WHO/ UN databases

The World Health Organization (WHO) has a breadth of datasets available for public access on their webpage. There are some datasets available for specific infectious diseases/ groups of diseases, including: HIV/AIDS, tuberculosis, malaria, neglected tropical diseases, cholera, influenza, meningitis, and sexual transmitted infections. For a more comprehensive dataset on a number of infectious diseases, the ‘Global Health Estimates 2015’ includes disease specific mortality rates, by sex and age. The data is collected and reported from 2000-2015. The data is available in .csv and .xls files, ensuring user-friendly data extraction. This work is funded by the WHO, and can be accessed here.

2. IHME- Global Burden of Disease (GBD) series

The Lancet has published the largest observational epidemiology study on the burden of disease, conducted from the Institute of Health Metrics Evaluation (IHME) out of the University of Washington. The most recent iteration of the study was conducted in 2016. The burden of disease is commonly measured by mortality, morbidity, incidence, and prevalence in these databases. The collected and reported data dates to 1970 until 2016 and spans 333 diseases and injuries. The data reported include communicable and non-communicable diseases. The databases are presented through multiple articles, allowing supplementary material, and figures and images to be accessed individually. These supplementary materials are not interactive and can only be accessed through PDF formats making it difficult to extract large amounts of data. Although there are a vast number of diseases, not all are present in the datasets- for example: cholera. Each study article was individually funded by several funders, some key organizations include: The Gates Foundation, the National Institutes of Health, the World Bank, the National Science Foundation, and the Indian Council of Medical Research. IHME has made a results tool available online which can filter the 2016 data by location, year, age, sex, and burden measurement across the identified causes/ diseases. This tool allows the data to be extracted onto a .csv file. In addition, all the data sources are available here.

3. Nature Study

The original study published by Jones et al (2008), ‘Global Trends in Emerging Infectious Diseases’, has since been updated by Allen et al (2017), ‘Global Hotspots and Correlates of Emerging Zoonotic Diseases’. The data in this study is collected through an extensive literature review, collecting data from 1940 onwards. The study identifies the spatial, temporal and biological characteristics of a disease during its initial emergence in the human population. The study seeks to identify why diseases emerge within the human population, rather than providing metrics of mortality and morbidity for each outbreak. Supplementary information includes the dataset created and is made available online via the Nature journal. The initial study was funded by NSF, NIH, The New York Community Trust, V. Kann Rasmussen Foundation and Columbia University Earth Institute fellowship. The updated study was funded by the United States Agency for International Development (USAID) and the Department of the Defense, Defense Threat Reduction Agency.

The Global Infectious Diseases and Epidemiology Network (GIDEON) supplies infectious disease outbreak data to its subscribers, and can accessed here. GIDEON was founded in 1992 and is available as a web based application and an ebook series. The data is collected from peer-reviewed publications, national health ministry reports, and other key global health players (e.g. WHO & CDC). The system is updated frequently to ensure that the data is as accurate and relevant as possible. There are two main categories within GIDEON: infectious disease and microbiology. The database is accessible by a 15-day free trial, and after a monthly subscription fee of $99.90 (1-year contract) or $199.90 (monthly rolling bases). GIDEON is a private organization and funded through these subscription fees. Although GIDEON requires a subscription fee, it has been used in a number of published studies with databases made available. Most notably, Smith et al compiled a comprehensive dataset from GIDEON that spans over a 33-year period (1980-2013).

5. HealthMap

HealthMap was established in 2006 at Boston Children’s Hospital to provide real-time surveillance of infectious disease outbreaks. The software uses freely available, informal online data sources, including but not limited to: ProMED, WHO, OIE, FAO, Google News, and EuroSurveillance. The data is displayed through a map, each point indicating an outbreak. The data can be filtered by disease, location, source, species, and date. Alternatively, the data can be viewed through a list format or over a time series graph. The data can be accessed online or through their mobile app “outbreaks near me”. The data source is made available primarily through funding by: Google, the Gates Foundation, Unilever, USAID, Amazon, Merck, Twitter, CIHR, CDC, Defense Threat Reduction Agency (DTRA), IARPA, and the U.S. National Library of Medicine. HealthMap can be accessed here .

The Program for Monitoring Emerging Disease (ProMED) is a program from the International Society for Infectious Diseases and tracks infectious disease outbreaks and acute exposures to toxins. The data is collected through media reports, official reports, online summaries, local observers, and others. The information submitted by individuals must be accompanied by affiliation identification, and is screened by the ProMED team prior to posting. ProMED is an archived database of infectious disease reports, which makes it difficult to extract large amounts of data efficiently. The program was created to increase communication among the international infectious diseases community, and encourages discussion. ProMED is available through an online website and allows individuals to subscribe to one or more of their “lists” in order to receive updated outbreak reports via email. The lists identify which topic areas are of interest within the ProMED database. ProMED collaborates with the HealthMap at the Boston Children’s Hospital. The funding for ProMED is primarily made available by the Wellcome Trust, Skoll Global Threats Funds, Google, the Gates Foundation, the Rockefeller Foundation, the Oracle Corporation, and the Nuclear Threat Initiative. ProMed can be accessed here.

Biology's Big Problem: There's Too Much Data to Handle

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Twenty years ago, sequencing the human genome was one of the most ambitious science projects ever attempted. Today, compared to the collection of genomes of the microorganisms living in our bodies, the ocean, the soil and elsewhere, each human genome, which easily fits on a DVD, is comparatively simple. Its 3 billion DNA base pairs and about 20,000 genes seem paltry next to the roughly 100 billion bases and millions of genes that make up the microbes found in the human body.

Original story* reprinted with permission from Quanta Magazine, an editorially independent division of whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.*And a host of other variables accompanies that microbial DNA, including the age and health status of the microbial host, when and where the sample was collected, and how it was collected and processed. Take the mouth, populated by hundreds of species of microbes, with as many as tens of thousands of organisms living on each tooth. Beyond the challenges of analyzing all of these, scientists need to figure out how to reliably and reproducibly characterize the environment where they collect the data.

“There are the clinical measurements that periodontists use to describe the gum pocket, chemical measurements, the composition of fluid in the pocket, immunological measures,” said David Relman, a physician and microbiologist at Stanford University who studies the human microbiome. “It gets complex really fast.”

Ambitious attempts to study complex systems like the human microbiome mark biology’s arrival in the world of big data. The life sciences have long been considered a descriptive science — 10 years ago, the field was relatively data poor, and scientists could easily keep up with the data they generated. But with advances in genomics, imaging and other technologies, biologists are now generating data at crushing speeds.

One culprit is DNA sequencing, whose costs began to plunge about five years ago, falling even more quickly than the cost of computer chips. Since then, thousands of human genomes, along with those of thousands of other organisms, including plants, animals and microbes, have been deciphered. Public genome repositories, such as the one maintained by the National Center for Biotechnology Information, or NCBI, already house petabytes — millions of gigabytes — of data, and biologists around the world are churning out 15 petabases (a base is a letter of DNA) of sequence per year. If these were stored on regular DVDs, the resulting stack would be 2.2 miles tall.

“The life sciences are becoming a big data enterprise,” said Eric Green, director of the National Human Genome Research Institute in Bethesda, Md. In a short period of time, he said, biologists are finding themselves unable to extract full value from the large amounts of data becoming available.

Solving that bottleneck has enormous implications for human health and the environment. A deeper understanding of the microbial menagerie inhabiting our bodies and how those populations change with disease could provide new insight into Crohn’s disease, allergies, obesity and other disorders, and suggest new avenues for treatment. Soil microbes are a rich source of natural products like antibiotics and could play a role in developing crops that are hardier and more efficient.

Life scientists are embarking on countless other big data projects, including efforts to analyze the genomes of many cancers, to map the human brain, and to develop better biofuels and other crops. (The wheat genome is more than five times larger than the human genome, and it has six copies of every chromosome to our two.)

However, these efforts are encountering some of the same criticisms that surrounded the Human Genome Project. Some have questioned whether massive projects, which necessarily take some funding away from smaller, individual grants, are worth the trade-off. Big data efforts have almost invariably generated data that is more complicated than scientists had expected, leading some to question the wisdom of funding projects to create more data before the data that already exists is properly understood. “It’s easier to keep doing what we are doing on a larger and larger scale than to try and think critically and ask deeper questions,” said Kenneth Weiss, a biologist at Pennsylvania State University.

Compared to fields like physics, astronomy and computer science that have been dealing with the challenges of massive datasets for decades, the big data revolution in biology has also been quick, leaving little time to adapt.

“The revolution that happened in next-generation sequencing and biotechnology is unprecedented,” said Jaroslaw Zola, a computer engineer at Rutgers University in New Jersey, who specializes in computational biology.

Biologists must overcome a number of hurdles, from storing and moving data to integrating and analyzing it, which will require a substantial cultural shift. “Most people who know the disciplines don’t necessarily know how to handle big data,” Green said. If they are to make efficient use of the avalanche of data, that will have to change.

Big Complexity

When scientists first set out to sequence the human genome, the bulk of the work was carried out by a handful of large-scale sequencing centers. But the plummeting cost of genome sequencing helped democratize the field. Many labs can now afford to buy a genome sequencer, adding to the mountain of genomic information available for analysis. The distributed nature of genomic data has created its own challenges, including a patchwork of data that is difficult to aggregate and analyze. “In physics, a lot of effort is organized around a few big colliders,” said Michael Schatz, a computational biologist at Cold Spring Harbor Laboratory in New York. “In biology, there are something like 1,000 sequencing centers around the world. Some have one instrument, some have hundreds.”

David Relman, a physician and microbiologist at Stanford University, wants to understand how microbes influence human health.

Image: Peter DaSilva for Quanta Magazine

As an example of the scope of the problem, scientists around the world have now sequenced thousands of human genomes. But someone who wanted to analyze all of them would first have to collect and organize the data. “It’s not organized in any coherent way to compute across it, and tools aren’t available to study it,” said Green.

Researchers need more computing power and more efficient ways to move their data around. Hard drives, often sent via postal mail, are still often the easiest solution to transporting data, and some argue that it’s cheaper to store biological samples than to sequence them and store the resulting data. Though the cost of sequencing technology has fallen fast enough for individual labs to own their own machines, the concomitant price of processing power and storage has not followed suit. “The cost of computing is threatening to become a limiting factor in biological research,” said Folker Meyer, a computational biologist at Argonne National Laboratory in Illinois, who estimates that computing costs ten times more than research. “That’s a complete reversal of what it used to be.”

Biologists say that the complexity of biological data sets it apart from big data in physics and other fields. “In high-energy physics, the data is well-structured and annotated, and the infrastructure has been perfected for years through well-designed and funded collaborations,” said Zola. Biological data is technically smaller, he said, but much more difficult to organize. Beyond simple genome sequencing, biologists can track a host of other cellular and molecular components, many of them poorly understood. Similar technologies are available to measure the status of genes — whether they are turned on or off, as well as what RNAs and proteins they are producing. Add in data on clinical symptoms, chemical or other exposures, and demographics, and you have a very complicated analysis problem.

“The real power in some of these studies could be integrating different data types,” said Green. But software tools capable of cutting across fields need to improve. The rise of electronic medical records, for example, means more and more patient information is available for analysis, but scientists don’t yet have an efficient way of marrying it with genomic data, he said.

To make things worse, scientists don’t have a good understanding of how many of these different variables interact. Researchers studying social media networks, by contrast, know exactly what the data they are collecting means each node in the network represents a Facebook account, for example, with links delineating friends. A gene regulatory network, which attempts to map how different genes control the expression of other genes, is smaller than a social network, with thousands rather than millions of nodes. But the data is harder to define. “The data from which we construct networks is noisy and imprecise,” said Zola. “When we look at biological data, we don’t know exactly what we are looking at yet.”

Despite the need for new analytical tools, a number of biologists said that the computational infrastructure continues to be underfunded. “Often in biology, a lot of money goes into generating data but a much smaller amount goes to analyzing it,” said Nathan Price, associate director of the Institute for Systems Biology in Seattle. While physicists have free access to university-sponsored supercomputers, most biologists don’t have the right training to use them. Even if they did, the existing computers aren’t optimized for biological problems. “Very frequently, national-scale supercomputers, especially those set up for physics workflows, are not useful for life sciences,” said Rob Knight, a microbiologist at the University of Colorado Boulder and the Howard Hughes Medical Institute involved in both the Earth Microbiome Project and the Human Microbiome Project. “Increased funding for infrastructure would be a huge benefit to the field.”

In an effort to deal with some of these challenges, in 2012 the National Institutes of Health launched the Big Data to Knowledge Initiative (BD2K), which aims, in part, to create data sharing standards and develop data analysis tools that can be easily distributed. The specifics of the program are still under discussion, but one of the aims will be to train biologists in data science.

“Everyone getting a Ph.D. in America needs more competency in data than they have now,” said Green. Bioinformatics experts are currently playing a major role in the cancer genome project and other big data efforts, but Green and others want to democratize the process. “The kinds of questions to be asked and answered by super-experts today, we want a routine investigator to ask 10 years from now,” said Green. “This is not a transient issue. It’s the new reality.”

Not everyone agrees that this is the path that biology should follow. Some scientists say that focusing so much funding on big data projects at the expense of more traditional, hypothesis-driven approaches could be detrimental to science. “Massive data collection has many weaknesses,” said Weiss. “It may not be powerful in understanding causation.” Weiss points to the example of genome-wide association studies, a popular genetic approach in which scientists try to find genes responsible for different diseases, such as diabetes, by measuring the frequency of relatively common genetic variants in people with and without the disease. The variants identified by these studies so far raise the risk of disease only slightly, but larger and more expensive versions of these studies are still being proposed and funded.

“Most of the time it finds trivial effects that don’t explain disease,” said Weiss. “Shouldn’t we take what we have discovered and divert resources to understand how it works and do something about it?” Scientists have already identified a number of genes that are definitely linked to diabetes, so why not try to better understand their role in the disorder, he said, rather than spend limited funds to uncover additional genes with a murkier role?

Many scientists think that the complexities of life science research require both large and small science projects, with large-scale data efforts providing new fodder for more traditional experiments. “The role of the big data projects is to sketch the outlines of the map, which then enables researchers on smaller-scale projects to go where they need to go,” said Knight.

The cost of DNA sequencing has plummeted since 2007, when it began falling even faster than the cost of computer chips.

Image: Peter DaSilva for Quanta Magazine

Small and Diverse

Efforts to characterize the microbes living on our bodies and in other habitats epitomize the promise and the challenges of big data. Because the vast majority of microbes can’t be grown in the lab, the two major microbiome projects — the Earth Microbiome and the Human Microbiome — have been greatly enabled by DNA sequencing. Scientists can study these microbes mainly through their genes, analyzing the DNA of a collection of microbes living in the soil, skin or any other environment, and start to answer basic questions, such as what types of microbes are present and how they respond to changes in their environment.

The goal of the Human Microbiome Project, one of a number of projects to map human microbes, is to characterize microbiomes from different parts of the body using samples taken from 300 healthy people. Relman likens it to understanding a forgotten organ system. “It’s a somewhat foreign organ, because it’s so distant from human biology,” he said. Scientists generate DNA sequences from thousands of species of microbes, many of which need to be painstakingly reconstructed. It’s like recreating a collection of books from fragments that are shorter than individual sentences.
“We are now faced with the daunting challenge of trying to understand the system from the perspective of all this big data, with not nearly as much biology with which to interpret it,” said Relman. “We don’t have the same physiology that goes along with understanding the heart or the kidney.”

One of the most exciting discoveries of the project to date is the highly individualized nature of the human microbiome. Indeed, one study of about 200 people showed that just by sequencing microbial residue left on a keyboard by an individual’s fingertips, scientists can match that individual with the correct keyboard with 95 percent accuracy. “Until recently, we had no idea how diverse the microbiome was, or how stable within a person,” said Knight.

By Greg Watry
To meet a projected population of 9.8 billion by 2050, global food production needs to grow an estimated 70 percent. Rising patterns of extreme weather are challenging food security. To adapt and feed the world, we need stronger crops.

A Shifting Environment

Glaciers are melting, sea levels are rising, wildfires are blazing and droughts are intensifying. Our earth is in an alarming state of flux. For food producers, these climatic shifts could prove devastating.

“Modern crop varieties are usually bred to a particular latitude and longitude as well as a particular climate pattern,” says Siobhan Brady, associate professor of plant biology. “When these patterns change, it can negatively affect plant growth. Too little water, too much water, high temperatures, increased levels of carbon dioxide—these can all alter plant yield.”

California is the nation’s leader in agricultural production and exports, with food producers generating $45 billion in output in 2016. According to the Department of Food and Agriculture, the Golden State provides one-third of the country’s vegetables and two-thirds of its fruits and nuts.

“Over a few thousand years human beings have selected varieties with traits that have allowed agriculture to flourish and human civilizations to grow and evolve,” says Neelima Sinha, professor of plant biology.

Through domestication and selective propagation, agricultural producers have bred crops for efficiency, selecting lines that produce higher yields and better tastes. This interference by human hands has weakened some crops, causing some to lose adaptive traits that once helped them flourish in environments rife with stressors, like droughts and parasites. California’s intensified natural disasters and droughts undoubtedly spell trouble for the state’s crops.

UC Davis plant biologists are looking to wild relatives for hints to strengthen crops, with the hope that the key to resilience lies in their genetics.

College of Biological Sciences Associate Professor Siobhan Brady and Professor Neelima Sinha in the Department of Plant Biology research and refine successful plant traits. David Slipher

Mining the Best Plant Traits

The arid Andean Region of South America is home to one tough wild tomato species. Drought-, salt- and pathogen-tolerant, Solanum pennellii is an ideal study in extreme plant adaptations. Strong roots and waxy-skinned fruits and leaves help S. pennellii cope with desert challenges. UC Davis researchers are immersed in understanding S. pennellii’s durability.

“The root structure is an understudied area of the plant,” says Brady. “It’s below ground, but it’s responsible for bringing up all the water and nutrients to the above-ground part of the plant, which in turn is necessary for our nutrition.”

Brady studies S. pennellii and its domesticated relative Solanum lycopersium, the garden tomato. Unlike garden varieties, the desert tomato’s roots continuously accumulate the compound suberin in its outer root layer, which enables water retention. The compound is only expressed in garden tomatoes during times of intense drought.

With cutting-edge genomics tools, Brady and her team seek the key to suberin production in the desert tomato. This data could help identify the mechanisms to introduce resilient root traits in other crops.

“If it can be expanded to other crops by breeding for a particular factor that might increase its presence, then it could provide the garden tomato’s roots with more waterproofing and a better ability to withstand drought,” Brady says.

Illustration inspired by the field notebook of the late UC Davis researcher Sharon Gray.

Tricky Parasites

A shifting climate isn’t the only threat to producing bountiful harvests. More than 4,000 different species of parasitic plants leech nutrients from host plants, leaving destruction in their wake.

“Parasitic plants are hugely significant in the world, especially in the lesser developed parts of the world,” says Sinha. “We have things like Striga, or witchweed, which causes incredible devastation.”

The parasitic plant genus Striga has been known to cause up to 90 percent loss in crop yields. According to the International Maize and Wheat Improvement Center, the weed affects more than 49 million acres of cropland in sub-Saharan Africa. It attaches to cereals like maize and sorghum, costing an estimated $1 billion in losses annually.

Witchweed actively hunts its prey. When sorghum roots are low in phosphorous, they secrete a molecule called strigolactone, which tells the roots to uptake more of the nutrient. But strigolactone acts like a smoke signal for witchweed.

“Strigolactone can be hijacked by this parasitic plant,” says Brady. “When the Striga sense the strigolactone, it will grow towards the sorghum root, penetrate it and hijack all its resources.”

Since witchweed evolved in tandem with sorghum, some sorghum crops have developed resistance to the parasite. Around 70 percent show some degree of tolerance or resistance.

Brady and her colleagues investigate the genetic mechanisms of resistant sorghum strains. She recently started a project to genetically define how sorghum roots interact with the soil microbiome to either facilitate or suppress witchweed growth.

"There is one sorghum line which has been shown to be defective in strigolactone biosynthesis,” says Brady. “Without this hormone, the Striga roots can’t emerge from their seeds, invade the host plant and take it over.”

Meanwhile, Sinha studies Cuscuta, also known as dodder. Common in California, the parasitic plant grows like cobwebs of orange spaghetti as it envelops its host. Sinha studies the genetic interactions between dodder and host plants.

“This plant has no roots, and it doesn’t photosynthesize. It doesn’t have any leaves, so everything it needs to grow it gets from the tomato,” says Sinha. “It’s a significant problem for California agriculture.”

Like Brady, Sinha is trying to understand at the genetic level what makes certain crops resistant or susceptible to dodder. By investigating plant genomes, Brady and Sinha hope to find the genetic triggers that enable plant defenses. With this knowledge, researchers could introduce these traits into our crops, ensuring their survival in a shifting climate.

Understanding the Language of Plants

Plants produce an array of chemical compounds to cope with environmental challenges. From defending against predators to attracting pollinators, these chemicals, called metabolites, number in the hundreds of thousands and vary from species to species. Each plant is unique in its chemical repertoire, making them the envy of organic chemists.

“To me, the chemistry in plants is much like a language,” says Philipp Zerbe, assistant professor of plant biology. “They use chemicals to communicate, where we use words.”

Zerbe works to decode the diversity of plants by mapping the genes, enzymes and pathways that form their complex metabolic machinery. He’s particularly interested in terpenoids, the largest and most diverse class of metabolites.

Terpenoids perform many functions, from regulating growth and development to protecting plants from environmental stresses like drought and salinity.

“If we can find the genes that are involved in this chemical defense, we can target these genes very precisely,” says Zerbe. “If we listen carefully and understand plants’ language, we may translate this knowledge into new avenues to improve crop resistance and ultimately provide for our burgeoning population.”

To Zerbe and other biologists, the advent of targeted genome editing with technologies like CRISPR/Cas9 has been a game-changer. It’s now possible for plant biologists to pinpoint the genes and pathways involved in specific metabolite production, which could then be introduced into crop varieties.

“Our efforts at the moment are focused on trying to fundamentally understand the enzymatic circuitry that plants use to form the remarkable diversity of metabolites and how these contribute to plant health,” says Zerbe. “Some of these metabolites have been known for decades, but now we have the means to decode their production.”

As the earth’s climate shifts, humanity must be proactive. To maintain food security, we must develop more resilient crops. Studying the strategies of wild and parasitic plants could prove essential in preparing our crops for a changing future.

At UC Davis, plant biologists are making the foundational, multidisciplinary discoveries that will help adapt food production to thrive in a new world.

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Optimal rotation patterns under infection: The protective effect of cover crops

Using the effective crop ratio δ(t) we compute the values of total yield Y for each sequence. We model the scenario in which the pathogen infects at the beginning of the first season, at t = 0, and include pathogen evolution. As previously, we focus on the ten rotation patterns which yield the best (Fig 4a). Interestingly, results show that 8 out of 10 rotation patterns which have a greater Y in the presence of the pathogen coincide with the set of rotations that maximise yield in pathogen-free conditions.

a) Selection of ten best patterns from 1024 possible sequences when cash yield loss due to infection is computed using the reference values. Each row is a rotation sequence. b) Best rotation sequence in the set of 10 optimal patterns for each of the conditions. The set index corresponds to conditions as indicated in Table 2. c) Intersection array for the sets of optimal sequences under different conditions. Each cell shows the number of sequences found in the intersection between the sets indicated in the vertical and horizontal labels. Highlighted sequences in (a): We allow for the 1024 possible sequences to repeat twice or thrice i.e. two or three generations. Rotation A is the sequence that maximises yield over multiple generations while Rotation B maximises yield only in the first generation but not later on.

Within the set of 10 optimal sequences, the yield range is 7.50 ≤ Y ≤ 7.36 without infection and reduces to 7.49 ≤ Y ≤ 7.31 with infection. In both sets, the best rotation pattern is the one starting with five seasons of cover crop, alternating after that and ending with three cash seasons. The reason behind the coincidence of patterns between the two sets is the double effect that the cover crops provide: on the one hand, they increase soil quality which in turn increases yield on the other hand, they break the epidemic diminishing crop loss and minimising yield loss.

Sensitivity of optimal patterns to different pathogen and soil conditions

Neither all epidemics have the same intensity, nor do all fields respond the same under the same farmer’s practices. Here we explore the conditions under which our set of rotations can maximise yield and compare it with the sets of rotations which have a better outcome in other scenarios. By a set, we refer to the selection of 10 optimal sequences among the 1024 possible rotation patters. We also compare the maximum value of cash yield that we can get for each condition (Table 2).

Sets refer to the selection of 10 sequences which best maximise yield in each condition. Values in bold indicate the change of conditions in the set with respect to the reference set.

Pathogen retainment.

Crop rotations are used to control the disease, but not all pathogens are equally vulnerable to the effects of break crops, here cover crops. The spores of airborne pathogens, such as fungi, can disperse over long distances and are difficult to control with crop rotations because the infection often spreads from the neighbouring fields. Conversely, crop rotations can be beneficial for soil-borne pathogens which cannot reproduce on a non-host plant [4, 35]. The ability of pathogens to survive in the soil or in crop debris, which can also be modified by tillage practices, is represented in our model by the pathogen retainment (ϵ). In the previous simulations, ϵ = 0.5, and here we explore what happens if its value increases to ϵ = 0.8 and decreases to ϵ = 0.2.

When we increase the retainment (set 1), the maximum yield decreases to Y = 6.93, and the optimal sequences have a ratio of two cash crops for every three cover crops in all cases. The number of cover seasons increases because there is a need for a more extended period of non-host crops to compensate that more pathogen stays in the soil. When we decrease the retainment (set 2), the maximum yield is approximately maintained, being Y = 7.50., and also the ratio of cash and cover crops.

Initial pathogen inoculum.

For the pathogens, the characteristics of the initial inoculum can determine the severity of the epidemic [36]. Here we explore it in two ways: the quantity of pathogen in the initial inoculum (p1(0)) and the initial virulence of the pathogen, controlled by the values in the fitness matrix (Wij) at time t = 0. The default initial pathogen in our model is p1(0) = 1 here, we observe how a ten-fold increase p1(0) = 10 and decrease p1(0) = 0.1 affect the optimal rotation patterns and yield. For the pathogen fitness, we conserve the ability of the pathogen to mutate into five fitter strains, but we set values of w11 = 1.5 and w11 = 0.5 as initial fitness, in comparison to the reference w11 = 1 (with wj2 = 0, as before, for the cover crops c2).

Starting with an initial pathogen of p1(0) = 10 (set 3) decreases the maximum yield to Y = 7.38 and decreases the ratio of cash to cover crops to 2:3 in all the sequences. The decrease is not drastic since starting with five consecutive cover crops decreases the pathogen load. This feature is present also in the reference set, to increase soil quality, showing the double effect of the cover crops. The decrease of inoculum (set 4) maintains the yield to Y = 7.50 and keeps the reference crop ratio. The increase of pathogen fitness (set 5) reduces the yield to Y = 6.91 and decreases the cash to cover ratio to 2:3. The results of decreasing pathogen fitness (set 6) are similar to the decrease of initial inoculum, being the yield Y = 7.50 and the ratio maintained to 1:1 or 2:3.

Initial soil quality.

When farmers aim to maximise cash yield, disregarding soil quality can lead to a sterile field which needs more cover crops than a priori expected. Since the rotation plan may start in a field with poor quality, we check the effect of initial soil quality on the patterns. The values chosen are q(0) = 1.9, close to the carrying capacity K = 2, and q(0) = 0.1.

High initial soil quality (set 7) leads to the highest maximum yield increase, being Y = 9.29 and the ratio of crops 1:1. This yield increase is because we can get the highest yield in the first seasons, and we can maintain soil quality by the alternation of crops (Fig 4b). The number of cash crops cannot increase more because this would promote the infection, decreasing the yield. Low initial soil quality (set 8) has the most substantial reduction of maximum yield, decreasing to Y = 5.30. Dedicating more seasons in improving soil quality at the beginning, the ratio of cash to cover crops decays to 3:7 or 2:3 (Fig 4b).

Intersection of optimal sets.

Results show that the set of 10 best sequences shown in the previous section—and chosen as reference set—intersects with the optimal sets obtained in all conditions except for increased initial soil quality, despite changes in the maximum yield. We check for the number of common rotation sequences via a pairwise comparison of the sets for each of the exposed conditions (Fig 4c). The cases for which the sets intersect the most with the reference set relate to the initial pathogen: increase and decrease of pathogen retainment ϵ (8/10), increase (8/10) or decrease (9/10) of initial pathogen p1(0) and changes of initial pathogen fitness w11(0) (8/10). When pathogen retainment and pathogen fitness is high, there is full intersection due to a common need for more non-host crops that break the epidemic (Set 1 and Set 5, 10/10) and also vice-versa (Set 2 and Set 6, 10/10). Other conditions also have high intersection values between them (from 6/10 to 9/10) due to similar needs for both increasing soil quality and controlling the infection. Variations in soil quality lead to the most different optimal patterns, with low (1/10) or no intersection with the rest of the sets.

Longer-term rotations: Soil quality and virulence control for the next generation of crops

Ten seasons, or a decade in yearly crops, can be regarded as long term planning, but farmers cultivate fields for even longer. To investigate if our rotation patterns are sustainable over decades, we study the variation in the yield and the pathogen load in consecutively repeated patterns.

For the analysis, we focus on the repetition of the population of sequences of length L = 10 i.e. all of the 1024 possible patterns and then repeating them twice or thrice. We term these repetitions as generations. We do not explore, however, the complete combinatorial space presented by the inclusion of second and third generation (i.e. 2 20 or 2 30 combinations), which is beyond the scope and focus of this manuscript. Of the 1024 patterns we limit our attention to the sets of 10 sequences that best maximise the yield in the infection scenario of reference (p1(0) = 1, ϵ = 0.5, w11(0) = 1) and median initial soil quality (q(0) = 1).

The results show that the rotations that best maximise yield after the second and third generation coincide with the optimal rotations for the first generation (intersection of 8/10 for both sets). To further investigate their sustainability, we analyse the changes of the agronomic variables—soil quality and cash yield—and the host-pathogen eco-evolutionary dynamics. We focus on two rotation patterns: the common optimal rotation for all generations Fig 4 (rotation A) and a rotation from the 10-optimal set of the first generation which is excluded in the set for the second and third generations Fig 4 (rotation B). Rotation A starts with five cover crops, alternates for two seasons and finishes with three cash crops rotation B has five cover crops followed by five cash crops.

The analysis (Table 3) shows that rotation A maintains the initial soil quality after the 10th season (q(10) = 1), while rotation B depletes it (q(10) = 0.15). In the previous section, we have shown that initial soil quality is key in determining the optimal rotation. Because of this feature, rotation A is able to maintain its optimal performance in the following generations, but B would need more investment in soil quality to aim for the same cash yield. Importantly, pathogen evolution during the first generation is also determinant in the yield outcome in the future. For rotation A, the increased frequency of virulent pathogen strains (f(p5) = 0.28) provokes more yield loss during the infection time. Consequently, the cash yield within the second (Y = 7.38) and third (Y = 6.32) generation is lower than within the first instance (Y = 7.49), even if soil quality is maintained. This effect is more drastic for rotation B, which initiates the second generation with high frequency of virulent strains (f(p5) = 0.46) and shows severe infection when a cash crop is cultivated. The frequency of p5 is chosen to be an indicator for virulence. If the strain p5 exists then the existence of all other strains is guaranteed.

A and B are the highlighted sequences in Fig 4. Rotation A is the sequence that maximises yield over multiple generations. Rotation B maximises yield in the first generation but not in the subsequent. For each rotation and generation there are shown values for total yield, final soil quality and final frequency of the most virulent strain (p5).

Remarkably, the pathogen strain with more fitness does not outcompete the rest of strains (Fig 5). Since pathogens can mutate in both forward and reverse directions with the same rate (Eq (5)), the system reaches a mutation-selection balance in which the rate of generating strains with less fitness equals the rate at which the fitter strains are generated. The faster growth of the fitter strains is reflected in their higher eventual frequency in equilibrium.

A) Soil quality (blue circles) and cash yield (red squares) variations, in discrete time-steps which correspond to the harvesting seasons. B) Eco-evolutionary dynamics of crop (yellow = cash, purple = cover) and pathogen (grey) within and between seasons. C) Relative abundances of pathogen strains during the rotation.

These results show the properties of the rotation patterns that maintain soil quality and slow down pathogen evolution in the long term—requirements for sustainable farming.

Scientists monitor crop photosynthesis, performance using invisible light

Twelve-foot metal poles with long outstretched arms dot a Midwestern soybean field to monitor an invisible array of light emitted by crops. This light can reveal the plants’ photosynthetic performance throughout the growing season, according to newly published research by the University of Illinois.

“Photosynthetic performance is a key trait to monitor as it directly translates to yield potential,” said Kaiyu Guan, an assistant professor in the College of Agriculture, Consumer, and Environmental Sciences (ACES) and the principal investigator of this research. “This method enables us to rapidly and nondestructively monitor how well plants perform in various conditions like never before.”

Scientists evaluate the photosynthetic performance of soybeans using these towers, which use hyperspectral cameras to capture light invisible to the human eye that may one day help us predict yield on a grand scale.

Published in the Journal of Geophysical Research – Biogeosciences, the Illinois team led by Guofang Miao, a postdoctoral researcher in ACES and the lead author of the paper, report the first continuous field season to use sun-induced fluorescence (SIF) data to determine how soybeans respond to fluctuating light levels and environmental stresses.

“Since the recent discovery of using satellite SIF signals to measure photosynthesis, scientists have been exploring the potential to apply SIF technology to better agricultural ecosystems,” said study collaborator Carl Bernacchi, an associate professor of plant science at the Carl R. Woese Institute for Genomic Biology (IGB). “This research advances our understanding of crop physiology and SIF at a local scale, which will pave the way for satellite observations to monitor plant health and yields over vast areas of cropland.”

Photosynthesis is the process where plants convert light energy into sugars and other carbohydrates that eventually become our food or biofuel. However, one to two percent of the plant’s absorbed light energy is emitted as fluorescent light that is proportional to the rate of photosynthesis.

Researchers capture this process using hyperspectral sensors to detect fluctuations in photosynthesis over the growing season. They designed this continuous study to better understand the relationship between absorbed light, emitted fluorescent light, and the rate of photosynthesis. “We want to find out whether this proportional relationship is consistent across various ecosystems, especially between crops and wild ecosystems such as forests and savannas,” said Miao.

“We are also testing the applicability of this technology for crop phenotyping to link key traits with their underlying genes,” said co-author Katherine Meacham, a postdoctoral researcher at the IGB.

“SIF technology can help us transform phenotyping from a manual endeavor requiring large teams of researchers and expensive equipment to an efficient, automated process,” said co-author Caitlin Moore, also a postdoctoral researcher at the IGB.

A network of SIF sensors has been deployed across the U.S. to evaluate croplands and other natural ecosystems. Guan’s lab has launched two other long-term SIF systems in Nebraska to compare rainfed and irrigated fields in corn-soybean rotations. “By applying this technology to different regions, we can ensure the efficacy of this tool in countless growing conditions for a myriad of plants,” said Xi Yang, an assistant professor at the University of Virginia, who designed this study’s SIF monitoring system.

“Our ability to link SIF data at the leaf, canopy and regional scales will facilitate the improvement of models that forecast crop yields,” Guan said. “Our ultimate goal is to monitor the photosynthetic efficiency of any field across the world to evaluate crop conditions and forecast crop yields on a global scale in real time.”

This work was supported by the NASA New Investigator Award, the Institute for Sustainability, Energy, and Environment (iSEE), a NASA Interdisciplinary Science Award and the TERRA-MEPP (Mobile Energy-Crop Phenotyping Platform) research project that is funded by the Advanced Research Projects Agency-Energy (ARPA-E).

The paper “Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and Light Use Efficiency of a Soybean Field from Seasonally Continuous Measurements” is available online (DOI: 10.1002/2017JG004180) or by request. Co-authors also include Joseph A. Berry, Evan H. DeLucia, Jin Wu, Yaping Cai, Bin Peng, Hyungsuk Kimm, and Michael D. Masters.

TERRA-MEPP (Mobile Energy-Crop Phenotyping Platform) is a research project that is developing a low-cost phenotyping robot to identify top-performing crops. TERRA-MEPP is led by the University of Illinois in partnership with Cornell University and Signetron Inc. and is supported by the Advanced Research Projects Agency-Energy (ARPA-E).
The Carl R. Woese Institute for Genomic Biology research facility at the University of Illinois is dedicated to transformative research and technology in life sciences using team-based strategies to tackle grand societal challenges.

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The Census of Agriculture is a complete count of U.S. farms and ranches and the people who operate them. Even small plots of land - whether rural or urban - growing fruit, vegetables or some food animals count if $1,000 or more of such products were raised and sold, or normally would have been sold, during the Census year. The Census of Agriculture, taken only once every five years, looks at land use and ownership, operator characteristics, production practices, income and expenditures. For America’s farmers and ranchers, the Census of Agriculture is their voice, their future, and their opportunity.

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FSA Crop Acreage Data Reported to FSA

2020 Crop Year

    (ZIP, 21 MB, January 12, 2021) (ZIP, 21 MB, December 10, 2020) ( ZIP, 22 MB, November 10, 2020 ) (ZIP, 21 MB, October 9, 2020) (ZIP, 21 MB, September 11, 2020) (ZIP, 21 MB, August 12, 2020)

2019 Crop Year

Note: Beginning with the 2019 crop, producers may report the same acre of either wheat, barley, oats, rye, and triticale for grain and grazing. That situation can occur when a producer intends to graze cattle in the winter, remove the cattle, and harvest the grain when mature later that spring. Thus, for these crops the acre would be counted twice when a producer intends to use the same acre for both grazing and grain.

    (ZIP, 21 MB, January 10, 2020) (ZIP, 21 MB, December 10, 2019) (ZIP, 21 MB, November 8, 2019) (ZIP, 21 MB, October 10, 2019) (ZIP, 21 MB, September 12, 2019) (ZIP, 21 MB, August 27, 2019) (ZIP, 21 MB, August 12, 2019)

Due to the large amount of questions surrounding the difference between NASS estimated planted acres and certified acres reported to FSA, USDA is publishing this update of the August 1, 2019 data.

A description of the differences between the August 2019 NASS crop acre estimates and the FSA certified acres reported to FSA can be found on the Office of the Chief Economist website, click this link for more information.

Where to find data on different crops - Biology

Plants have shaped our human life form from the outset. With the emerging recognition of world population feeding, global climate change and limited energy resources with fossil fuels, the relevance of plant biology and biotechnology is becoming dramatically important. One key issue is to improve plant productivity and abiotic/biotic stress resistance in agriculture due to restricted land area and increasing environmental pressures. Another aspect is the development of CO2-neutral plant resources for fiber/biomass and biofuels: a transition from first generation plants like sugar cane, maize and other important nutritional crops to second and third generation energy crops such as Miscanthus and trees for lignocellulose and algae for biomass and feed, hydrogen and lipid production. At the same time we have to conserve and protect natural diversity and species richness as a foundation of our life on earth. Here, biodiversity banks are discussed as a foundation of current and future plant breeding research. Consequently, it can be anticipated that plant biology and ecology will have more indispensable future roles in all socio-economic aspects of our life than ever before. We therefore need an in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex natural as well as anthropogenic ecosystems. Latest developments in biological and bioanalytical research will lead into a paradigm shift towards trying to understand organisms at a systems level and in their ecosystemic context: (i) shotgun and next-generation genome sequencing, gene reconstruction and annotation, (ii) genome-scale molecular analysis using OMICS technologies and (iii) computer-assisted analysis, modeling and interpretation of biological data. Systems biology combines these molecular data, genetic evolution, environmental cues and species interaction with the understanding, modeling and prediction of active biochemical networks up to whole species populations. This process relies on the development of new technologies for the analysis of molecular data, especially genomics, metabolomics and proteomics data. The ambitious aim of these non-targeted ‘omic’ technologies is to extend our understanding beyond the analysis of separated parts of the system, in contrast to traditional reductionistic hypothesis-driven approaches. The consequent integration of genotyping, pheno/morphotyping and the analysis of the molecular phenotype using metabolomics, proteomics and transcriptomics will reveal a novel understanding of plant metabolism and its interaction with the environment. The analysis of single model systems – plants, fungi, animals and bacteria – will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches. In this review the foundations of green systems biology are described and applications in ecosystems research are presented. Knowledge exchange of ecosystems research and green biotechnology merging into green systems biology is anticipated based on the principles of natural variation, biodiversity and the genotype–phenotype environment relationship as the fundamental drivers of ecology and evolution.