How do we find genes related to traits? A review of Bulked Sample Analysis

Here at Legume Laboratory we have written a number posts about research that has overexpressed a particular gene which has been linked with a particular trait such as resistance to drought or resistance to pest damage. The idea behind such research is usually to see whether an increase in the amount that gene is transcribed results in a linear change in the trait, evidencing  the control of that trait by the gene being overexpressed.

But how are those candidate genes initially identified?

The Plant Biotechnology Journal recently published a review piece on the process and current state of technology used in designing and sampling populations of plants to isolate particular differences in phenotype and identifying the genetic differences that cause the phenotypes of related plants to diverge. In particular, the review focuses on bulked sample analysis, a short cut to the more time and budget costly method of gene mapping all samples of a population. The end result can be the identification of one or a couple of genes controlling the trait of interest, but is usually the identification of a number of regions of the genome that are differentially expressed between the phenotypes, such regions called  Quantitative Trait Loci (or QTL).


How we find genes related to traits

Bulked segregants and variants

To discover which genes are involved in a particular trait within a certain species of plant we first need to pull together a population of the plant that shows variation in the trait of interest. There are two methods of creating this population, one of which uses a controlled population created from a specific breeding strategy (segregating population), the other creates a population from plants with phenotypic variation in the trait of interest which are derived from any population of that species ie the population isn’t raised through controlled breeding (a variant population). The idea behind both strategies is to obtain a population of plants which, in the next step, can by phenotyped for the trait of interest with particular attention paid to the most extreme variation ie plants showing significant drought tolerance versus plants most adversely affected by water deficit.

Sampling and phenotyping

After a method of developing a population has been chosen the plants are grown and a method established of scoring or classifying the different phenotypes being examined. An example may be the number of lesions formed on plant leaves as a result of a fungus or grain yield under varying water supplies. In the case of segregating populations the phenotyping may be carried through a number of generations of plants with individuals at the phenotypic extremes being selected for crossing to create the following generation, segregating the trait and, theoretically, the genetic underpinnings of the trait.

In establishing these populations care must be taken to ensure that only the trait of interest is being selected for.  The authors of the review emphasise the importance of reducing the signal-to-noise ratio and mention the development and implementation of precision phenotyping techniques and technology.

Where a particular type of stress is being selected for, the contrasting environments (one of high stress, one of lesser or absent that stress) need to be established and tested for concurrently.

Once the population for phenotyping has been developed under the required testing conditions, the plants are sampled. In most cases the sampling takes place by applying the phenotyping criteria to each plant, the end result being a spectrum of phenotypes that will usually distribute normally with the extreme phenotypes being at the tail ends of the distribution curve.

Obtaining results that are statistically significant rely on the population size and the number of plants at either end of the distribution curve. Variations in the sample sizes required depend heavily on factors such as the distance between genes related to the trait (and therefore the frequency of recombination), the number of genes related to the trait and effect size of a particular gene or genes on the trait.


Figure 2 from article. Four types of bulked sample analysis (BSA). (a) BSA for qualitative traits such as disease resistance with two distinct phenotypes (R, resistance; S, susceptible). (b) BSA for quantitative traits with normal distribution, among which samples from two tails (L: lower; U, upper) are selected and bulked. (c) BSA for multiple parallel bulks with individuals selected independently from the two tails of a normal distribution. (d) BSA with only one bulk available for the target trait, while the other tail was killed by lethal genes or due to severe stresses, when compared with individuals randomly selected from a control population under no stress with normal allele frequencies for the target trait; CK: plants from the control population, R: plants selected from the stressed environment.

For a population consisting of between 200 to 500 plants, the optimum tail size would be 20% to 30% of the population. As the total population being sampled from increased, the size of the tails to be selected will decrease. Large variations in phenotypes can reduce the sample sizes to 10% of a small total population (200 individuals), while QTLs associated with a small phenotype effect will require a much larger population (3000 to 5000 individuals) with each extreme phenotype being a selection of 100 plants from each tail.

Figure 2 above shows different methods of bulking samples for analysis. In the case of a trait that can be classified as a resistance or susceptible to a particular stress, the more resistant and susceptible individuals selected from the tails are used, while populations looking at a quantitative change in phenotype (such as grain weight) can be sampled from the extreme tails in one or multiple bulks from each end of the distribution. Where, for example, one treatment group fails to survive the treatment process leaving only one tail, the tail can be compared to a selection of control crops (figure 2d above).

Molecular analysis

Once the selected samples of the population are bulked they can analysed by various methods to detect differences or changes in genome, gene transcription or protein expression.

DNA analysis is the predominant form of molecular analysis. For many crops a set of DNA markers have been created from analysis of plant genome, based on such genetic landmarks as simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs) and PCR based markers. Using the markers as the basis for PCR amplification, as a most common example, differences between the genotypes of the two phenotype bulks can identified and mapped back to the genome. The result is demonstrated in Figure 2 above with its depiction of DNA bands or DNA expression levels and the connection between variation of plant phenotype and genotype.

DNA microarrays are increasingly being used in a similar manner for a faster and cheaper analysis.

Linkage maps can then be created which show how closely linked the DNA marker is to the gene or genes within the identified QTL.

Analysis of the transcribed DNA via RNA sequencing analysis methods can give a greater insight into the variation in gene transcription between phenotypes, although the effect of any non-transcribed DNA or levels of transcription cannot be assessed.

Protein analysis is a little more difficult to perform and borrows from immunology methods that use labeled antibodies to detect proteins within the bulked samples. However, mass-spectrometry  and Edman degradation are two methods that are being used to understand the primary sequence of proteins present within the samples with greater precision and without the need to have a range of antibodies that will detect the majority of proteins in the samples.


Figure from article – the BSA pipeline, from population selection to application.

Applications of Bulk Sample Analysis and the Future

Bulk sample analysis, particularly bulked segregant analysis, is repeatedly used to detect the genetic underpinnings of particular traits and is widely used in agriculture-related science. When performed under tightly controlled conditions it assists researchers to isolate a particular trait from other variations in phenotype from which  base the identification of QTL can result.

And the depth of interrogation of the genetic basis of important traits is increasing as sequencing technology develops. The ability to effectively barcode segments of DNA before sequencing it in a large pool of DNA, allowing subsequent identification of the starting DNA, will hasten data gathering.

More important is the reducing cost of sequencing DNA. At the point where using markers and PCR or microarrays hardly differs in price to entire genome sequencing, the amount of data generated for analysis (and the number of computer programs developed to assist with the taks) will explode. It may be then that complex traits weakly controlled by a number of QTL will be identified with comparative ease.

The theoretical assistance these methods have for agriculture are the identification of genes that control particular traits which will then be used for screening and selecting crop breeding stocks. As the library of QTL increases, the ability to select seeds for particular conditions will assist food production levels particularly in the more trying of growing conditions.



How plant diversity benefits soil structure

Soil health and, particularly, the concern about increasing soil degradation and its effect on ecosystems, is a significant area of research. We’ve previously written about crop diversity and how it can have positive effects on soil organic matter and how crop residue return increases microbial biomass and soil health. However, a new article in Ecology Letters noted that many studies have focused on the biological effects of plant diversity on soil. Therefore, these researchers designed a study to look at the direct physical effects of species diversity on soil properties, leaving aside the biological effects.


The experiment was conducted in two parts: a mesocosm trial and a field trial.

The mesocosm trial used 64 mesocosms with 24 plants in each. The type of plant added depended on the treatment. Six plants were used; two grasses (Lolium perenne and Anthoxanthum odoratum), two forbs (herbs) (Plantago lanceolata and Achillea millefolium) and two legumes (Trifolium repens and Lotus corniculatus). Therefore, some mesocosms were bare soil (control), 6 were monocultures of one of the six plants, and the remainder contained each available combination of two or more of the six plants. The 64 mesocosms were replicated again as some of the subsequent soil testing procedures were destructive. The 128 mesocosms were randomly arranged across 4 glasshouses and grown for 18 months after which the aboveground biomass was measured and soil cores were taken for root trait analysis. The soil cores were used to test for stability through three methods:

  1. slaking (rapid immersion in water);
  2. microcracking (slowly wetting); and
  3. mechanical breakdown (agitated in conical flask).

The replicated samples were tested for water conductivity (water flow through the soil) and soil strength, being its ability to withstand being displaced when a mechanical ram applied force to the soil column.

The field experiment used the grassland experiment set up at Jena in Germany in 2002. 82 plots with different treatments consisting of the same species used in the mesocosm experiment save for the L. perenne were combined with a number of other species to provide differing levels of diversity. Plots contained either 1, 2, 4, 8, 16 or 60 different species.

Topsoil was extracted and analysed for aggregate stability, root trait analysis, organic matter content and glomalin-related protein content in similar methods to those used in the mesocosm analysis.

Results and discussion

The data collected from the two experiments indicated that although species diversity increased aggregate stability, certain species of plants, particularly the grasses with their particular root traits, played the most significant role in this improved stability. With improved stability came improved productivity indicated by an increase in above-ground plant biomass significantly correlated with the improved aggregate stability.

Plant diversity Figure 1

Figure 1 from article. Figures (a) – (c) contain aggregate stability data from the mesocosm experiments while (d) – (f) contain the same data from the field experiment. Across the experiment, increasing plant diversity resulted in increased soil aggregate stability when assessed as a function of the remaining mean weight soil diameter.

Rooting strategies of the grasses resulted in a significant increase in resistance to slaking even when planted as a monoculture. The researchers hypothesise that this may be due to the grasses having finer root systems for greater resource uptake, their root length being the greatest and narrowest out of the 3 types of plants in the experiment. Across the experiment it was shown that root length density and root density were positively correlated with increased aggregate stability. Further, finer roots are decomposed easier than thicker roots which is likely to lead to higher organic matter which has independently been shown to increase aggregate stability.

L. perenne, one of the grasses, had the greatest root length and impacted on aggregate stability the most. When it was planted in a mesocosm with a diversity of other plants, the root length density of the mesocosm was not correlated with the soil aggregate stability but, in mesocosms with a diversity of plants but without L. perenne, the correlation between stability and root length density was again apparent (see Figure 2). The data points in the figure (at least for slaking aggregate stability) appear to generally show greater stability overall when L. perenne was included, indicating the significant effect the root system of this grass has on aggregates.

Plant Diversity Figure 2

Figure 2 from article. Without L. perenne (circles), increasing root length density, correlated with species diversity, resulted in increased resistance to being broken down. But the presence of L. perenne skewed this correlation (dots), seeming to have single-handed effect on increasing aggregate stability.

Adding to this finding that the effects were more complicated than just the effect of species diversity alone, the two legume plants resulted in reduced aggregate stability with associated reduced root length but thicker roots and greater root mass. Even in field plots with legumes included in the diversity treatments, a reduction in the aggregate stability of the treatment was observed compared to when the legumes were not included.

These abundant short, thick roots had other advantages though – water permeability and resistance to soil displacement from the force of the mechanical ram increased in the legume treatments.


What does this mean? As almost always, its complicated. However, we can be confident that increasing crop diversity is likely to result in increase soil aggregate stability. Aggregate stability increases the nutrient holding capacity of the soil, assisting plant growth. But some plants in the combination will have a greater affect than others on traits such as stability or resistance to displacement. If you have  sandy soil struggling to form or maintain aggregates and therefore losing nutrients, planting grasses are likely to help to a bigger extent than many other plant types. But if you have a clay soil that struggles to drain water and in which plants struggle to form a solid root system, a legume crop is more likely to increase water permeability and assist in soil aeration.

A point that is not obvious from the paper but could be of interest is whether, or how, legume crops adversely affect root length in a diverse planting. Grasses are considered to have their particular root strategy to scavenge as many nutrients as possible. But if planted next a legume, does the easy accessibility to nitrogen fixed by the root nodules of the legume plant result in the grass roots not growing to the depth they would have otherwise grown to? Perhaps this has been answered before but, if not, it could explain this particular piece of data.

A great experiment with some helpful data for farmers, agronomists and gardeners.

The Effects of Residue Return on Soil Microbial Biomass – A 30 Year Study

The role of microbial biomass (bacteria and fungi) in the soil is significant. Microbes help to decompose organic matter in the soil, recycle nutrients and to help the formation of soil aggregates. These activities increase the mineralisation and retention of elements in the soil required for plant growth. What effect returning crop residues to the soil in combination with different fertiliser use have on microbial biomass is therefore important for both crop health and to avoiding overuse of fertilisers and related financial and environmental consequences.

A study in the Journal of Agricultural Science looked at the effects of 30 years of returning maize to an experimental plot at the Jilin Agricultural University in combination with a variety of fertiliser treatments on the amount and composition of the microorganisms in the soil.

Using a split-plot design, the researchers had 3 residue return treatments of 0, 2.5 and 5 thousand kilograms of residue per hectare per year. Each plot treatment was repeated in triplicate and was subdivided into 4 square metre subplots. Each subplot was randomly treated with either no fertiliser, nitrogen only, potassium only, phosphorus only or with the various combinations of two or more of those fertilisers.

The soil was a clay loam soil and the subplots were divided by concrete barriers that were buried to a depth of 2 metres. On each plot a target of 60,000 maize plants were grown per hectare and after harvest the residues were dried, cut and incorporated back into the soil to a depth of 20cm at the various rates. On 8 May 2014, after 30 years of the soil be subjected to the various treatments, 4 soil sample cores were taken from each plot for analysis.

The hypothesis of the study were that:

  1. fertilisation would lead to significant changes in soil microbial communities to differing degrees dependent on the amount of maize residues also returned to the soil; and
  2. the soil microbial communities would be due to treatment-induced changes in the properties of the soil.


Given the 24 different soil treatments there are a large number of statistically significant results reported on. Some of the more interesting or useful results were:

  • All fertiliser treatments resulted in a lowering of the soil pH. Plots with residue returned at 5000kg/ha had a significantly lower pH than the other residue treatments;
  • Fertilisation had no effect on organic carbon content of soils under control and 5,000kg/ha residue returns but a significant effect was seen when fertiliser was combined with 2,500kg/ha residue returns. Plots with residue return had higher organic carbon carbon than plots with none;
  • Fertilisers had differing effects on total nitrogen content depending on residue return but the more crop residues that were returned the higher the total N observed;
  • The carbon-to-nitrogen ratios were highest in plots without residue return and decreased as residue return increased;
  • Residue return resulted in a significant increase in available nitrogen and potassium, but not phosphorus.
  • A 5,000 kg/ha residue return resulted in a higher total and ratio of soil microbial groups than both the other residue treatments but the ratio of fungi to bacteria was reduced;
  • Fertiliser effect on soil microbes depended on the crop residue amount for a significant effect to be observed;
  • Residue return significantly effected fungi and bacteria and had an indirect effect on bacteria due to an increase on the soil fertility while fertiliser effected bacteria in the soil as a result of the decrease in pH;
  • Microbial community composition of plots with 5,000kg/ha returned were significantly different from the other residue treatments.

residue return figure 1

Figure from article. Effect of residue return amount and mineral fertiliser application on microbial biomass.


The main point raised by the researchers in the discussion was that there seems to be a threshold over which the rate of residue return will have a significant effect on microbial biomass, as can be seen in figure 1 (a) above. The biomass amounts between the control and 2,500kg/ha residue return treatment show no significant effect but a visible effect can be seen when the 5,000kg/ha of residue was returned. Whilst it has been previously explained that an increase in residue causes an increase in organic carbon, in turn increasing biomass, in this experiment the increase in residue amount and increase in biomass did not correlate with the changes in organic carbon amounts in the soil.

If such a threshold is confirmed, knowing this threshold under different soil types, different crop types and environmental conditions could be an important piece of knowledge to farmers in adjusting their fertiliser rates for a given rate of residue return.


Soil nutrient composition is effected by many factors. Tillage, crop rotation, soil type and texture are just a few of these. Understanding how two agronomic treatments such as residue retention and fertiliser will interact and adjusting application rates of the fertiliser will certainly assist food production. Whilst there is further research required to confirm the threshold observed in this study, whether different crops have different effects and whether crop rotations alter the impact are just a few. But this study can be used to guide farmers and agronomists in broadly understanding the effect of residue retention.


The quest for the ultimate on-farm stress sensor

Very recently, Dr Lee Hickey of the University of Queensland told a short story about a farmer in the future being alerted to a disease outbreak on his farm via his phone. He swiftly commands one of his drones to spray an RNAi specific to the disease on the infected field and averts the crises. Instead of worrying about the problem and the potential effect on his crop’s yield for the year, he goes to the footy with mates without a worry.

The idea of remotely sensing and specifically addressing a crop under stress is a current area of technology under development. There are some commercial sensors available to detect a small array of stressors and the use of drones in agriculture is increasing, but the ability to monitor and address a number of potential in-crop problems at the same time is a technology still confined to a distant world in an Isaac Asimov novel.

But that doesn’t mean scientists aren’t researching how the technology might work.

A recent study in the journal Agriculture took three commercial sensors, two being spectrometers and one a fluorometer. The spectrometers can use a variety of indices to give an output which relates the amount of light hitting a plant and the amount of light being reflected to, for example, how green the plant is. Fluorescence sensors measure the fluorescence of the cells of the plant and can adjust to measure such spectra as red fluorescence, blue green fluorescence and infra-red fluorescence. From these measurements certain conclusions can be made about the health of the crop being monitored.

The Experiment

Although the sensors mentioned above are normally used to monitor an individual stress such a water deficiency, nitrogen deficiency, fungal infection or weed competition, this paper used a combination of stress of factors to determine whether a single band or index could be used to detect and identify a single stress whilst one or more other stressors were present.

The experiment was set up so that every combination of the four types of stress and a control was tested using each index available on each device. 12 days after planting, the water stress testing commenced with water reduced to 30% of the pot water holding capacity while non-stressed plants were watered at 70% of the water holding capacity. For nitrogen stressed plants no nitrogen fertiliser was used throughout the experiment while the non-stressed wheat received fertiliser at predetermined amounts on predetermined days. Weed plants and fungal infections were added at the same stage of the experiment.


Table from article. Combinations of stress treatments.

Each treatment combination was measured every third day after two leaves had appeared on the spring wheat that was being tested. For each index to be measured, five readings were taken on each reading day, and the five readings averaged.


Each stress resulted in a significant effect on the growth of the spring wheat either by the amount of biomass or the ratio of root to shoot growth. Many indices provided significantly different spectral ratios and fluorescence levels between control plants and plants tested with one type of stress. The tables of average values for each index tested for each device and whether the difference between stressed and non-stressed values were statistically significant are provided below.


For a given index, combining the average values of more than one stress made it more difficult to ascertain whether a plant was stressed and, if so, which stress was being applied.

Without pulling the entirety of the article here, an example of this difficulty is given. Where two different types of stress resulted in an increase of the index value for one stress and a decrease in the index value for the other stress, determining whether one or the other stress is present is easy – if the value goes down compared to the control, then the individual stressor that causes the index to be lower is present, and if the value is higher than in the control then obviously the stressor that increases the value is present. However, if both stressors are present then the value given will be somewhere in between the two extremes and can be difficult to distinguish from the control. A graph of the values of the FLAV index from the Multiplex sensor reading nitrogen deficiency and water shortage is given as an example of this difficulty.


From Figure 7 of article (“-” = not stressed, “+” = stressed). Using the FLAV index for water and nitrogen monitoring, the unstressed plant gives the lowest reading, the water stress only treatment gives the highest reading, the nitrogen stress only treatment gives a reading only slightly higher than the unstressed treatment, and the treatment with both types of stress gives a reading roughly half way in between the unstressed and the water stress only treatment.

The difficulty caused by this is that, pretending you were the farmer in Dr Hickey’s story, it may be difficult for the app on his phone to differentiate between a nitrogen stressed crop, an unstressed crop and a crop with both nitrogen and water stress. The lack of precise diagnosis results in the lack of precise management.

What was also clear from the data obtained was that an index value for an unstressed plant at one particular stage of development can be identical with the value for a stressed plant at a different stage of development. The result is an added complexity in differentiating between a stressed and unstressed crop as a function of growing time compared to a stress that can be differentiated from a control plant at the same stage of growth.


The experiment shows that there are a number of current indexing methods of differentiating between a plant faced with a particular stress to an unstressed plant. However, for some indices the stressed and unstressed plants need to be at the same growth stage in order for the results to be of use. Further, while some indices are able to show the presence of one type of stress when other types of stress are also present, the interplay between the effect of different stressors can make detecting a stress or identifying whether one ore more particular stressors are present difficult.

For the time being, developing a set of standard readings over a range of indices for unstressed and differently stressed species at different growth stages is required. The standards can be incorporated into sensors that may use either complex algorithms to determine whether a plant is stressed and, based on the deviation of the reading to known standards for a range of types of stress, evaluate the likely type or types of stress present. Alternatively, a sensor using a variety of indicies which can each individually distinguish a different type of stress and deviation from a set standard may result in more accurate and dynamic sensing system.

We are still quite a way from Dr Hickey’s vision, but the technology and the methods to distinguish different types of crop stress already exists. Its not hard to envision a breakthrough piece of technology becoming available soon, and harder not to imagine that this type of technology will soon be incorporated into automated precision agriculture such as the Farmbot and indoor agricultural systems.

Growing Potato under different mulch materials

Here is some science for the home gardener looking to maximise their potato yield!

A short article published in the 2011 proceedings a conference entitled “New findings in organic farming research and their possible use of Central and Eastern Europe” compared soil water availability, soil temperature and tuber yield of potatoes grown with either a chopped grass mulch, a black textile mat and a control group with no mulch.

The article has been hanging around here for a while with thoughts of replicating it ourselves. There are some issues with the paper; being a conference paper it isn’t peer reviewed, it contains very little raw data, the methods aren’t described particularly well and it is possible that the experiment could have been designed in a way to control for some unaccounted for variables.

What the researchers did was plant potatoes at two sites in the Czech Republic to study for two years. At each site 4 replicates of the experimental and control groups were planted. One group was treated with a 25mm thick layer of chopped grass 14 days after planting, one group was planted in pre-cut holes in a black textile mat that had been placed over ridges dug into the plot, and the control group had no mulch added.

Over the course of two years the researchers measured soil temperature and soil water potential in each plot. At the end of the experiment they cultivated the potatoes and measured their size and total yield.

They found that soil temperature was higher under the black mulch than in the control. Strangely, they do not mention the soil temperature under chopped grass. Soil water potential was highest under chopped grass, with the control variant having slightly less water potential than black mulch.

Finally, the researchers found that under the chopped grass mulch the number of larger tubers was greater than in the other two groups, the total weight of smaller tubers were reduced and the total yield was significantly more.

Potato study figure

Figure from article: increase in number of larger tubers and total yield in potatoes grown under grass mulch (GM) compared to black textile mulch and control conditions.

The discussion section of the paper points to an article which found that potato doesn’t grow as well under high soil temperature and low soil moisture, and the results of the study correspond to this earlier research.

As indicated above, there are some issues with the paper (which is part of the reason the thought of replicating the study and perhaps bettering it has crossed our minds). This type of study will usually include significantly more detail about the plots where the experiment was carried out. Details such as how the soil was treated over the past number of years, any differences in previous treatment or soil preparation methods are noted, graphical representation of how the plot was divided and how the selection of which treatment is to be applied to which plot was randomised are a common part of the methods and materials. The manufacturer and product details of the type of black textile used would be a detail that would enhance the ability to replicate the study. The lack of raw data on the number of potatoes cultivated at each size and how the statistical analysis was performed also cast some doubt on the reliability of the conclusions.

The result of the study is that, although it has some faults, it does give some direction to home gardeners who may be wondering whether to apply a mulch and, if so, what type of mulch to increase their own potato growing efforts.

Strawberry flowering the possibility of an everbearing cultivar

A study published in the Plant Biotechnology Journal has experimentally confirmed that the expression of a particular gene, called TERMINAL FLOWER1, is intricately involved in the repression of flowering in strawberry cultivars, including commercial varieties.


Different strawberry cultivars will flower and produce fruit at different temperatures and different day lengths. Some cultivars require short day lengths, others require short days and a certain temperature in order to produce fruit, while others require longer days. When the day length and/or the temperature is not right, the plant will repress flowering and will instead sprout runners for reproduction. There are some everbearing varieties but they tend to not be good at reproducing themselves.

The study looked at expression levels of TERMINAL FLOWER1, a gene that has been studied in other plant species and which has been shown to represses flowering when it is expressed. Other plant cultivars with everbearing characteristics have been found to have a lack-of-function mutation in this gene.

Previous studies have found that the TERMINAL FLOWER1 gene is controlled by a photoperiodic pathway; the gene is activated by an upstream gene product, which in turn is activated by a gene product found to be produced in leaf tissues when exposed to long days (assuming the plant produces flowers only in short days). When short days return, the expression of the first gene in the pathway is down-regulated, in turn down-regulating the expression of the two other genes in the pathway, and flowering is once again allowed to occur.

The study

The study performed a number of simultaneous experiments to elucidate the link between day length, temperature and flowering with the expression of this pathway of genes:

  1. Silencing the TERMINAL FLOWER1 gene

The experiment took a short day cultivar and transformed it using an RNA interfernce construct (see our previous article on RNAi) to knockout the gene. The researchers searched a genome database and found 3 homologous genes within the cultivar and tested whether their RNAi construct affected those genes as well, but couldn’t find any difference in their levels of expression between the wild and the transgenic plant lines. Therefore, the sole effect of the RNAi construct should only be to repress this one gene.

What they found was that transgenic lines started flowering 2 weeks before the wild type and produced many more flowers when they were both grown in cool temperatures in short days. The transgenic line continued flowering throughout the experimental period, essentially becoming an everbearing line. Further, the number of runners produced were the same in both the wild and transgenic lines, pointing to a possible solution to the problem with current everbearing lines.

RNAi strawberry

Figure from article – (a) FLOWER TERMINAL1 expression; (b) Gene expression linked to runner production; (c) flowers per plant; (d) number of runners per plant; and (e) wild-type and transgenic strawberry plants.

2. Day length responses in three cultivars with different flowering times

The second experiment took three varieties of strawberry plants with different flowering times, ‘Honeoye’, ‘Alaska Pioneer’ and ‘Polka’, subjected the three to short days and long days and measured the expression of the FLOWER TERMINAL1 (FaTFL1) expression and expression of the other two upstream genes (FaSOC1 and FaFUL1) in the photoperiodic pathway.

Looking at the FLOWER TERMINAL1 gene expression (the middle row of bar graphs in the figure below), the three cultivars had differing responses to the short and long day, the ‘Honeoye’ having a large initial expression under long day lengths that reduced over the period of the trial, the ‘Alaska Pioneer’ doing similar but reducing at a slower rate than the ‘Honeoye’ in long days, and the ‘Polka’ showing a consistent level of expression when exposed to long day light over the trial period.

Strawberry expression levels

Figure from article. Expression levels of the three genes in the photoperiodic pathway in ‘Honeoye’, ‘Alaska Pioneer’ and ‘Polka’ varieties.

3. Interaction of day length and temperature in ‘Elsanta’ and ‘Glima’

‘Elsanta’ and ‘Glima’ are commercial varieties of strawberries with differing flowering habits; ‘Glima’ flowers in long days at temperatures below 21ºC while ‘Elsanta’ requires short days no matter the temperature. What the researchers found was that ‘Glima’ in fact flowered at any day length and only showed any repression when grown in long days at 21ºC. The ‘Elsanta’ plant, as predicted, didn’t grow any flowers in long days at any temperature, while short days resulted in flowers in all test plants with warmer temperatures resulting in earlier and a greater number of flowers.

Gene expression levels in the leaves and shoots of both cultivars were taken and showed that the expression of FLOWER TERMINAL1 was in line with the flowering of the plants but with a higher expression in ‘Elsanta’ than in ‘Glima’. This may explain why day length played a greater role in flowering habits in ‘Elsanta’. Further, the transcript levels in ‘Elsanta’ under long days was quite low at a growing temperature of 9ºC but still didn’t result in flowering.

strawberry Glima and Elsanta

Figure from article. Gene expression levels in ‘Glima’ and ‘Elsanta’ varieties under short and long days grown at three different temperatures.

Looking at the FaFTL1 expression levels in the above figure, the levels are quite similar in long day growing when ‘Glima’ was grown at 21ºC and ‘Elsanta’ was grown at 9ºC. Despite the similar levels, 60% of ‘Glima’ plants flowered while no ‘Elsanta’ plants flowered. This gives an insight into the differing effects of the gene expression in different cultivars.

What can we do with this information?

One take-away point for any gardener looking to grow some strawberries is that to grow and maximise your yield you must match the flowering requirements of the cultivar chosen with the local climate and day lengths. Living in a region with longer winters of shorter day lengths and colder climates can be matched with cultivars that will flower and produce strawberries for a longer period, reproducing over the summer period, while warmer climates with shorter winters and more long days than short will require an appropriate long day cultivar.

The RNAi experiment opens up the commercial possibility of developing a cultivar that has repressed FLOWER TERMINAL1 expression under all conditions, resulting in longer or everbearing plants without affecting vegetative reproduction.

Finally, as the researchers wrote, we don’t completely understand this photoperiodic pathway and how it interacts with temperature-related genes. Further research may identify temperature-sensitive genes which interact with the photoperiodic pathway and this may result everbearing qualities being engineered or bred in such a way as to allow any cultivar to successfully grown in a changing climate.

Nitrogen Use Efficiency – Current Knowledge and Future Development

The significance of nitrogen to agricultural science is a recurring theme for the Legume Laboratory, having recently been the focus of articles about the possibility of artificial nitrogen fixation and estimating nitrogen mineralisation rates from legume crops.

A review piece was recently published in the journal Agronomy about nitrogen use in cereal crops, focusing on our current knowledge of how to increase efficiency in its uptake and identifying areas where developing technology may result in further efficiency improvements.

Wheat crops (and plants generally) require nitrogen to:

  1. establish and maintain photosynthetic capacity and activity;
  2. maximise the number and size of seeds; and
  3. increase the quality of crop products.

Nitrogen is highly mobile in soil and the efficiency of plant uptake depends on many variables including the type of crop and environmental conditions such as soil type and rainfall. For example, rice is known to have the lowest rate of uptake amongst cereal crops while barley has the highest.

The literature review doesn’t delve into the research behind many of its statements but it is a great starting point for some basic knowledge and further research into different sources and uses of nitrogen fertilisers, different application methods and current and developing technologies to diagnose nitrogen status in crops.


Mind-map figure. Left side – inorganic nitrogen fertiliser sources split based on nitrogen content. Right side – current and potential technologies for nitrogen sources and in-crop monitoring. Figure reproduced from article.

Fertiliser sources

The article describes the pros and cons of different inorganic nitrogen sources besides their nitrogen content. For example:

  • Anhydrous ammonia, which contains the highest nitrogen content of inorganic fertilisers, is a gas requiring pressurised storage, specialised equipment for storage, handling and application and can lower soil pH undesirably;
  • Aqua ammonia has high ammonia content but is volatile at temperatures above 10ºC and needs to be injected into soil;
  • Ammonium nitrate combines two different forms of nitrate, reportedly improves baking quality of wheat, but has low nitrogen content compared to other sources.
  • Ammonium sulfate contains both nitrogen (but in a low amount compared to other sources) and sulfate and is useful for acid-requiring crops and high pH soils;
  • Ammonium chloride contains a low concentration of nitrogen but one that is suitable for chloride responsive crops such as cereals and coconut but not for crops that cannot tolerate the chloride. It also lowers soil pH.
  • Urea, the most widely used nitrogen fertiliser source due to ease of manufacture, transport, and low cost, is also volatile, phytotoxic to susceptible crops and can be toxic to germinating seedlings when emerging.

The article discusses the advances in matching as closely as possible in time the release of nitrogen from fertiliser to when the plant requires it. A method such as urease inhibition controls nitrogen release and has been found in studies to increase potato tuber yield and nitrogen use efficiency while also decreasing nitrification. However, as with many fertilisers, the positive effects are affected by the status of the soil prior to treatment, it is difficult to predict the time-frame and release rate of nitrogen, and yield increase doesn’t consistently match the use of nitrogen inhibitors.

The use of microogranisms is a potential nitrogen source which could increase crop yields. Plant growth-promoting bacteria have been shown to promote growth in row or horticultural crops, effecting the production of growth regulators that stimulate the plant uptake of nutrients.

New sources of nitrogen such as nanofertilisers have the potential to deliver more nitrogen from lower dose amounts but require more research on possible adverse effects. Coating seeds in nutrients appears to have some potential to improve nitrogen uptake, and recapturing nitrogen lost from agricultural fields or after food consumption could be the basis of new fertiliser production.

Nitrogen fertiliser application and management

Different fertiliser sources, different crops and different application times require different methods of application. The tables below set out the different nitrogen sources and applications methods for different application times to match nitrogen availability with crop demand (top) and the effects of management practices on nitrogen nutrition.

agronomy figures

 Tables sourced from article.

Diagnosing crop nitrogen needs

Matching nitrogen supply to crop requirements is an area where significant improvements in efficiency can be found. Diagnosing when crops require nitrogen application during growth is limited to a few methods such as sap nitrate tests (high accuracy but labour intensive and destructive to the plant) and optical sensors (non-destructive and reliable but cannot detect over-fertilisation). The development of satellite and drone technology has opened up the possibility of remote sensing nitrogen status. Developing accurate methods of optically determining whether, and how much, nitrogen is needed by a particular crop is both a matter of technology development and increased knowledge of nitrogen uptake pathways and efficiencies.


Crop nitrogen assessment techniques. Figure from article.


The article provides some useful knowledge that can be used now to help match nitrogen source, application method and application time. But more importantly it shows where the limits of our knowledge are and what technological improvements could assist nitrogen supply and crop production and its a pretty safe bet that the authors have written the review as a springboard for further research.

Incorporating suitable fertilisers for particular crops, soil types or environmental conditions at suitable times will improve crop production, particularly throughout the developing world. Research into the matrix of factors that effect nitrogen uptake in crops to elucidate the conditions that enhance efficiency would build on our current knowledge, and the ability to deliver nitrogen with maximum efficiency at times predicated on real-time in-crop knowledge of nitrogen content will significantly increase food production, reduce the costs and side-effects of fertiliser production and mitigate the environmental effects caused by the nitrification of over-supplied fertiliser.

Estimating Legume Nitrogen Mineralisation Rates

The Australian Society of Agronomy held its 17th Australian Agronomy Conference last year, and the result is a smorgasbord of papers freely available here. From pest management to climate change there is a paper to suit every interest.

One paper which is of use to grain growers in South East Australia is “Legume effects on available soil nitrogen and comparisons of estimates of the apparent mineralisation of legume nitrogen“: in short, if a farmer rotates a legume crop through their field, how can they estimate the nitrogen available to subsequent crops grown in the same field.


The introduction states three important factors in the decomposition of organic nitrogen and mineralisation, being:

  1. The amount of rainfall, which stimulates microbial activity;
  2. The amount of legume residues present; and
  3. The nitrogen content and quality of the residues.

Using these factors, the paper reports on a field experiment conducted in southern NSW that compared the soil nitrogen in fields treated as follows:

  1. Lupin grown for grain;
  2. Lupin grown for brown manure (the crop was killed before seed maturation to retain maximum nitrogen in the plant;
  3. Canola grown for grain;
  4. Wheat grown for grain.

By calculating the amount of residues of each treatment left after harvest and measuring the percentage of nitrogen content in those residues, the study used the data obtained along with the rainfall throughout the period to develop a method for grain farmers to estimate soil nitrogen content, in turn assisting growers to estimate fertiliser needs for crops grown following a legume rotation.

The experiment methodology and results

The soil was tested in April 2011 for nitrogen levels prior to growing each of the treatment crops. In April 2012, after each crop was harvested (and the brown manure crop was terminated), the soil was tested again and then all fields were planted with the same cultivar of wheat. In April 2013, the soil was once again tested.

The testing in April 2012 showed that the nitrogen content was 3 to 5 times higher in the lupin crop treatments than in the wheat treatments, and that the brown manure treatment had the highest quality of nitrogen content.

Further testing in April 2013 confirmed the increased nitrogen findings in the lupin treatments, but also showed that the mineralisation of nitrogen by microbial activity continued through the subsequent wheat treatment sown in 2012.  This resulted in a finding that an equivalent of 4 to 5 kg of nitrogen per tonne of the residue dry matter from Lupin treatments in 2011 remained in the soil and had become available for subsequent plant growth.

The conclusion

Using the data obtained and the rainfall measurements, the researchers delineate some useful rules to estimate soil nitrogen content.

The simplest estimate was to assume approximately 10kg of additional nitrogen have been added to the soil per hectare for each tonne of shoot residue. Using a rough estimate of the percentage of dry mass which is harvested as grain, and which farmers will know at the end of a growing season, the equation suggested for farmers to calculate the expected mineral nitrogen available for growing is given as:

Nitrogen (kg/ha) = 20 x tonne grain yield per hectare

The researchers do point out that rainfall between legume harvest and the following growing season will affect microbial activity and therefore mineralisation rate. Given the variation in rainfall, the equation is likely to be applicable only to areas with similar rainfall patterns to those in South East Australia.

Why is it useful?

Being able to adjust fertiliser rates has the obvious economic benefits for farmers. But it also has environmental effects. Over-application of nitrogen fertiliser increases nitrification of unused nitrogen, resulting the production of the greenhouse gas nitrogen dioxide, and when excess nitrogen runs off into adjoining streams, increased bacterial growth results in eutrophication and resultant negative effects on aquatic wildlife.

For the scientifically minded home gardener or market gardener, weighing your legume crops or green manure and calculating the total weight per hectare can give a rough idea of the nitrogen added to your soil and give greater confidence in the amount of fertiliser required in following growing seasons.




Selecting Seed based on Need

A great initiative started in Africa is trialling varieties of soybean crops to determine what cultivar will provide the greatest yield given the conditions peculiar to the particular area. The knowledge, knowledge which should guide productive growing on every patch of land, will increase farm production, helping farmers and communities.

The article is sourced from Science Daily based on information from the University of Illinois College of Agricultural, Consumer and Environmental Sciences.

What is the effect of leaving some of the vegetable crops up over the winter—how does that improve soil conditions?

Great information for improving your soil…by not doing much at all!

Soils Matter, Get the Scoop!

Intentionally or unintentionally, many gardeners have left plants in their gardens over the winter. This is actually a good thing…and something everyone should consider on a yearly basis!

Scientists – specifically agronomists and soil scientists – refer to the plant “litter” that remains after a harvest as “residue”. Leaving the residues in place over the winter, instead of pulling them up or tilling them into the soil surface, provides numerous benefits for the soil and your garden.

Sustaining life through soil protection leads to a bright future Farmers keep crop residues on their fields for the same reason home gardeners should consider them!  Credit: Fabian Fernandez

  • Plant residues reduce erosion and the loss of valuable topsoil. Residues cover soil and protect it during the non-growing season. Crop residues catch rainfall. This reduces the impact that individual rain droplets have with the soil surface. Residues also slow down any flow of melting snow over the soil. Both help protect the soil…

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