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msu-ceco
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aec_v1

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Rape is more easily managed for multiple grazings than are the other brassica species.
Rape can generally be grazed at 4-week intervals.
Leave approximately 6 to 10 inches of stubble after the first grazing to promote rapid regrowth; on the final grazing, plants should be grazed close to ground level.
Rape can cause sunburn on lightskinned animals, especially if it is grazed while the plants are immature.
Kale has more variation among varieties than most other brassica species.
Some varieties may provide grazing after about 90 days, followed by a regrowth opportunity; others may require as much as 180 days to mature.
Dry matter yield of kale can be impressive.
Swedes , like turnips, produce large edible roots.
Swedes yield more than turnips, but require 150 to 180 days to reach maximum production.
Swedes is one of the best crops for fattening lambs and flushing ewes.
Yield is maximized with a 180-day growth period for many varieties, but most hybrids produce the greatest yields when allowed to grow 60 days before first grazing and 30 days before the second grazing.
Turnips grow fast and can be grazed as early as 70 days after planting.
They reach nearmaximum production level in 80 to 90 days.
The proportion of top growth to roots for turnips can vary from 90% tops and 10% roots to 15% tops and 85% roots.
Turnips can be seeded any time from when soil temperature reaches 50F until 70 days before a killing frost.
Brassicas require good soil drainage, and soil pH should be in the range of 5.5 to 6.8.
Brassicas can be seeded into wheat stubble or no-tilled into a sod, provided it has been killed with glyphosate.
Clean-till seeding works well, but may have increased insect pressure.
If seeding after crop farming, herbicide carryover residues can be an enormous problem.
As a rule, carryover label recommendations for sugar beets are usually applicable to most brassica species.
Some producers in the Upper Midwest have had success in aerially seeding turnips into standing corn in mid-August.
The corn must be physiologically mature for this to be successful.
Fertilizer should be applied at the time of seeding to give brassicas a competitive edge on weeds.
Normally 75 to 80 pounds of nitrogen per acre and any phosphorus and potassium needed should be applied similar to what would be applied for a small grain.
Good soil moisture following seeding is key to successful establishment.
Brassicas should not comprise more than about two-thirds of cattle diets because of their low dry matter content.
Therefore, it is important to provide adjacent pasture, corn stalks, or a palatable, dry hay fed free choice to cattle when grazing these crops.
It is also desirable to introduce them to brassicas slowly by limit grazing for a couple of hours per day until their digestive systems are accustomed to them.
As with stockpiled forage, brassicas should be strip grazed.
If regrowth is desired, at least 2 inches of leaf should be left intact.
Generally animals will consume the leafy portion of the plant before progressing to the root portion.
To encourage consumption of roots, it may be necessary to disk after the tops have been grazed.
Cereal crops such as wheat, rye, oats, barley, or triticale can provide autumn or early winter grazing opportunities.
However, certain management practices need to be modified from what is normally done for grain production.
When small grains are used for grazing, they should be planted 3 to 4 weeks earlier than for grain production.
Also, between 60 and 100 pounds of nitrogen per acre is normally applied at planting time.
Recommended seeding rates vary depending on establishment method and seeding combinations.
Rye is more productive than wheat or triticale for both fall and spring production.
However, forage quality is better with triticale than with rye.
Oats seeded in the fall can be excellent quality and very productive, but will be killed by cold weather during winter.
Depending on geographical location, with adequate fall moisture, rye, triticale, and wheat should be available for grazing from October through much of December and then again in early spring.
The intended use of small grain determines what the stocking rate and grazing dates should be.
If a silage or grain harvest is planned, grazing should only be moderate, as heavy grazing can reduce grain yields.
Moderate grazing in the autumn will not result in significant silage or grain losses provided moisture and soil fertility are adequate.
In fact, fall pasturing can be beneficial where the small grain was seeded early and has made excessive growth and soil conditions are dry.
Spring grazing may be started when growth resumes.
If a grain or silage crop is to be harvested, grazing should be discontinued when the plants start to grow erect, just before jointing ; otherwise grain yield will be reduced.
Seeding date has a major impact on how early small grains can be grazed.
If the goal is to graze in late fall, seeding should be completed by late August in the Midwest and by late September in the Deep South.
With adequate moisture, growth will continue until air temperatures drop to around 40F.
Remove livestock when 3 inches of growth remain to maintain sufficient leaf area for continued growth and recovery.
Annual ryegrass can be used as a companion species with, or as an alternative to, the small grain cereal crops to provide grazing in late autumn, early winter, and spring.
Compared to small grains, ryegrass is easier to manage, has a higher feed quality, and fewer management problems in spring, and can make rapid regrowth after initial grazing.
Annual ryegrass can be easily established into standing corn or soybeans or in these or other summer row crop fields after harvest.
It can also be notilled into old alfalfa fields.
There are differences in winterhardiness among annual ryegrass varieties, so if spring grazing is desired, it is important to plant varieties that are known to be adapted.
Seeding rates vary according to planting method and combination of species.
Wait to graze winter annual grasses until at least 8 inches of growth have accumulated.
In climates and management situations in which plants are likely to persist, it is generally advantageous to grow perennial rather than annual legumes.
However, in the Deep South, where perennial legumes such as white clover usually act like annuals, any of several winter annual legumes are a usually a better choice, depending on soils, rainfall, and producer objectives.
Various species may be grown alone, with another annual legume, or in combination with winter annual grasses.
Winter annual legumes make almost all of their growth in late winter and spring, but the distribution of growth of various species within this time period varies greatly.
Some row crop producers plant winter annuals as cover crops to provide nitrogen for a summer row crop, improve soil tilth, and protect the soil during winter.
Of course this forage can also be grazed in late winter or spring.
Hairy vetch is hardy enough to be grown as far north as the Lower Midwest, but it produces most of its growth during a few weeks in mid-spring.
Overseed winter annuals on summer grass sods
Winter annuals, including annual ryegrass, small grains, and various annual legumes such as clovers and vetches can be seeded as a single species or in various mixtures into warm-season perennial grass sods such as bermudagrass, bahiagrass, or dallisgrass to extend the grazing season by 30 to 60 or more days.
Winter annuals should normally be overseeded about 2 or 3 weeks before
Extend the grazing season by 30 to 60 days or more by overseeding winter annuals on summer grass sods.
the expected date of a killing frost.
Unless some tillage is provided to ensure good seed-soil contact, the existing grass should be clipped or grazed to 1 to 2 inches tall.
Producers who have pastures of both tall fescue and summer perennial grasses may be able to graze their summer grass closely to facilitate overseeding of winter annuals at the same time they are stockpiling tall fescue.
Overseeded pastures should be kept grazed closely in spring to prevent shading of summer species.
Provide supplemental feed during warm weather
Despite the best management plans, shortages of forage commonly occur during July and August in the coolseason grass region due to drought or overstocking.
When this happens, supplemental feeding of hay or grain byproducts in July and August might be used to avoid overgrazing.
Also, a pasture or paddock of summer annual grass might be planted in anticipation of reduced pasture availability.
In areas where cool-season perennial forages dominate pastures, if pastures are short or pasture forage is of poor quality in July and August, feeding animals in a dry lot might be an option.
This may be more cost effective than overgrazing or trying to supplement animals on overgrazed pastures.
There is less hay loss when feeding hay in summer months as compared to winter.
Also, this approach allows pastures to begin recovering from overgrazing or drought and provides an opportunity to stockpile for late fall and winter grazing.
Using the same logic, some producers might also consider feeding hay in late summer or autumn to allow stockpiling of tall fescue forage.
Once livestock are removed from pastures, it may be worthwhile to apply 30 to 60 pounds per acre of nitrogen to stimulate plant recovery.
During hot weather, use of ammonium nitrate may be advisable as surface-applied urea can lose significant amounts of nitrogen through volatilization.
If using urea, the application should be made just before a rain to minimize the exposure time of the fertilizer material on a dry soil surface.
This publication emphasizes the value of grazing, but most livestock producers will need to provide hay or some other stored feed at certain times during the year.
Losses during the harvesting, storing, and feeding of hay vary considerably.
Ranges in losses are included in table 4.
Given the worstcase scenario, animals may consume only about 29% of the forage present in a hay field at harvest.
Further, the more hay wasted, the more that must be produced or purchased to feed animals at times when adequate pasture forage is not available.
The value of hay storage and feeding losses alone in the United States are estimated to exceed 3 billion dollars annually.
On some farms, hay storage and feeding losses account for over 10% of the cost of livestock production.
This is particularly objectionable because these losses occur after all the time, energy, and effort required to produce and harvest the hay have been incurred.
Also, these losses can be greatly reduced or eliminated without a great deal of expense or effort.
Table 4.
Percent loss of hay from curing through feeding.
Lax management- Good management-
Incremental Additiveb Incremental Additiveb
Field curing 25 25 12 12
Harvesting 15 36 8 19

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Dataset Card for AEC

Dataset Description

The Agricultural Extension Corpus 1.1 is a compilation of 1655 official agricultural Extension documents (e.g., fact sheets, digital books) concerning water-related and sustainable agricultural practices research.

Homepage: https://huggingface.co/datasets/msu-ceco/aec_v1
Paper: AgXQA: A benchmark for advanced Agricultural Extension question answering
GitHub: agxqa_benchmark_v1
Curated by: DSI Lab
Point of Contact: pouyan@msu.edu
Language(s): English (en)

Uses

Direct Use

It is intended to train ML models in general and for NLP tasks in particular (e.g., Masked Language Modeling).

Out-of-Scope Use

This dataset should not be used as a reference/answer to address or respond to time-sensitive and location-sensitive questions.

Dataset Structure

This dataset does not contain any specific fields. It is a corpus of paragraphs and sentences that has been split into training, validation, and test sets using an 80-18-2 ratio

Dataset Creation

Curation Rationale

The motivation for the creation of this dataset is to provide practitioners of applied AI in agricultural fields with a reliable base corpus that can be used for several NLP downstream tasks (MLM, NER, QA, etc.).

Source Data

Two categories of sources:

The Journal Of Extension
Land-grant institutions’ official Extension websites (e.g., MSU Extension, UNL Extension, etc.)
- We covered at least one Extension website in each of the 40 (out of 48) states in the continental USA. Due to the limitations of appropriate software, we only provided author references categorized per all documents found in each state. The list of states and the references can be found here.

Data Collection and Processing

The dataset was compiled from publicly available documents: (1) 212 scholarly full-text articles from the Journal Of Extension and (2) 1443 materials extracted from 40 land-grant institutions that provide Extension services throughout the USA. These materials include factsheets, e-books, and articles related to agricultural research, and they are in HTML, text, and PDF formats.

The PDFs were processed and converted into text using Amazon's Textract.

Furthermore, we removed non-UTF8 characters, bibliographic references, and URLs (when possible).

Who are the source data producers?

The original documents were authored by Extension staff (educators, faculty, specialists, etc.) from diverse land-grant institutions. The references to these authors can be found here.

Personal and Sensitive Information

To the best of our ability, we tried removing any personal information (e.g., emails) during the automatic processing stage. However, considering that this dataset was programmatically compiled from other existing texts, there could be residues of personal information, (especially from bibliographical sections) that could have carried over into our dataset as well. If you are the original author of a document compiled inside AEC and found your personal details in the dataset, please refer to our list of references here. If you have not been cited, please contact us to request removal.

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

BibTeX:

@article{KPODO2024109349,
    title = {AgXQA: A benchmark for advanced Agricultural Extension question answering},
    journal = {Computers and Electronics in Agriculture},
    volume = {225},
    pages = {109349},
    year = {2024},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2024.109349},
    url = {https://www.sciencedirect.com/science/article/pii/S0168169924007403},
    author = {Josué Kpodo and Parisa Kordjamshidi and A. Pouyan Nejadhashemi},
    keywords = {Agricultural Extension, Question-Answering, Annotated Dataset, Large Language Models, Zero-Shot Learning},
    abstract = {Large language models (LLMs) have revolutionized various scientific fields in the past few years, thanks to their generative and extractive abilities. However, their applications in the Agricultural Extension (AE) domain remain sparse and limited due to the unique challenges of unstructured agricultural data. Furthermore, mainstream LLMs excel at general and open-ended tasks but struggle with domain-specific tasks. We proposed a novel QA benchmark dataset, AgXQA, for the AE domain to address these issues. We trained and evaluated our domain-specific LM, AgRoBERTa, which outperformed other mainstream encoder- and decoder- LMs, on the extractive QA downstream task by achieving an EM score of 55.15% and an F1 score of 78.89%. Besides automated metrics, we also introduced a custom human evaluation metric, AgEES, which confirmed AgRoBERTa’s performance, as demonstrated by a 94.37% agreement rate with expert assessments, compared to 92.62% for GPT 3.5. Notably, we conducted a comprehensive qualitative analysis, whose results provide further insights into the weaknesses and strengths of both domain-specific and general LMs when evaluated on in-domain NLP tasks. Thanks to this novel dataset and specialized LM, our research enhanced further development of specialized LMs for the agriculture domain as a whole and AE in particular, thus fostering sustainable agricultural practices through improved extractive question answering.}
}

APA:

Kpodo, J., Kordjamshidi, P., & Nejadhashemi, A. P. (2024). AgXQA: A benchmark for advanced Agricultural Extension question answering. Computers and Electronics in Agriculture, 225, 109349. https://doi.org/10.1016/J.COMPAG.2024.109349

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