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New Hybrid Coffee Variety Might Open Doors in the Industry

New Hybrid Coffee Variety Might Open Doors in the Industry

The new variety is set to be tested in farmers’ fields this year

According to the organization, F1 hybrids have the ability to combine favorable traits including higher yields and disease resistance.

If you’re a coffee drinker, you’ve most likely heard the term “Arabica” thrown around regarding coffee beans. Arabica, indigenous to Ethiopia, is one of the most common types of coffee bean. Recently, the nonprofit organization World Coffee Research has announced that there’s a new coffee variety in town, and it might change the game for coffee producers.

According to the organization, the new coffee variety, Starmaya, is the first of its kind — an F1 hybrid bred by seed rather than biotechnology. Since it’s propagated by seed, it could potentially open doors to “an elite class of varieties.”

Prior to the Starmaya, the possibility for most farmers to gain access to F1 hybrids was very small, as they can currently only be produced by “technically sophisticated nurseries.”

The nonprofit says it plans to incorporate the hybrid into two major research programs that will hopefully allow the variety to become more accessible to farmers in the future.


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."


Nafziger makes sense of statistics at AGMasters Conference

"We can think of crops grown in fields as a 'population' of plants," he said. "Basically when we apply some sort of treatment, we need to know if this forms a new population or not? Statistics, which might be considered the science of describing variability in a population, can help us figure out what is really happening."

Nafziger provided conference participants with a short course in statistics using actual strip-trial data from on-farm trials to show how strip-to-strip variability affects results, and to explore what it means to be "significant."

Using an Excel spreadsheet, Nafziger analyzed data in trials to explain statistical terms and how to use them to interpret results.

"The 'truth' &mdash did a treatment cause a response or not &mdash always exists, it's just our job to find it," he said. "We aren't in the business of doing 'nice' trials rather, applied research is the business of trying to say something when we are done."

Because on-farm trials often have a great amount of variability, he said it's important to do random treatment assignments.

"With 'yes-no' type inputs, for example to use a fungicide or not, assign treatment randomly to one strip of paired strips," he said. "This should be done before planting or right after, in order to prevent bias in keeping or dropping data."

When designing on-farm research trials, Nafziger said to keep it simple.

He recommends using a strip size wide enough to allow borders. He also encourages growers to randomize within each repetition, use 4 to 8 pairs of repetitions per location, keep accurate records of where things are planted, measure yields accordingly, and convert to standard moisture in a standard way.

Nafziger warns growers not to discard data unless they know for sure what happened to cause the data to be untrusted. When the study's completed, stop and get your answer, he said.

A "significant" effect or difference means that the treatment was likely to have caused an effect, but it does not mean that the treatment is useful or that it will pay. He said we can also be fooled, and get "significant" responses due to the "luck of the draw" when we assign treatments to strips. The chances of this diminish quickly as we use more fields to do such comparisons.

"Non-significant results can be obtained from no effect of the treatment or from so much variability among strips that we can't separate a treatment effect from the 'background noise' of variability," he said.

While there is always the choice to not accept such a conclusion as final, Nafziger cautions against thinking that more work will produce an outcome that we like better.

"Don't cherry pick your data to give the answer you want if you need to get a certain answer, why bother to go to all this work?" he said. "Our point is not to find significance, but rather to figure out what happened and where we go from there."

No matter what, getting predictive answers to applied research questions takes a great deal of work, and getting good answers takes honesty and even more work, he said.

"There really aren't any shortcuts," he said. "Statistics do not substitute for the large amount of data and keen observation that good on-farm research always requires."