Agriculture Efficiency Breakout Session

Tuesday, August 21, 2006

Major comments and recommendations are in *italics.*

Questions

1.  What are 2-3 elements of agricultural systems, eg. soil moisture,
    where improved information can benefit decision support tools
    between now and 2011?

2.  What are the actual and potential data sources from NASA that can
    contribute information to the elements defined above?

3.  The applied Sciences Program element in agriculture has been focused
    almost exclusively on global agricultural monitoring. What
    additional topics should the program element consider through 2011?

Purpose: What should this program element be in the near future?

Program Plan what the program element will be doing in next year and 5
years:

What should be included in program plan

Will help applied sciences draft solicitations in next couple of years

-   Projects supported by NASA Slide

> Most projects are contributing to the PECAD CADRE Decision Support
> Tool of the FAS, global agriculture productivity and yield. Suite of
> info that FAS uses to make estimates that are fed to the World
> agricultural resource board.

-   Road map available through website. Ultimate goal is: a global
    agricultural monitoring capability.

-   At this point are there other issues? DSS? Directions that the
    program should explore and consider over the next 3-5 years.

Where we do want to be, what do we want to do within the context of the
applied science program? Partnerships with users, exploiting NASA
capabilities, and new missions. If successful how to make transition
from experimental to operational observations?

Many ag applications rely on field level observations, current NASA
systems are primarily coarse resolution, are we to include farm level,
and field level observations? - Yes. (can include LDCM and Landsat data)

-Is there a need for field level observations? What are these needs?

(Chair) We are not confined to existing measurements -- include the
observations that you think should be made in order to support ag
decisions.

Start of Discussion of the questions:

(Chair) interested in areas that NASA can participate. A lot that is
done within precision ag. These measurements are avail through non NASA
sources. If there is a way that NASA data can be used in association
with these data systems, this is of interest.

Q. Emphasis on decision support systems, is this going to continue,
broaden?

A. There will be a continued focus on partnership of operational users-
not only federal agencies, ngo's, private. Solutions network -- effort
to look at what is coming out of the research that can have impact on ag
efficiency, don't need a specific user.

Rapid prototyping- generate a product, (simulated data) will this
product deliver the info needed, doesn't necessarily require user.

-For social benefit, need a partner that will use the data for decision
making process.

Q. is this diff. from how DSS used to be defined (as a black box)?

A. Yes. Now is seen as more of a decision support process, information
is part of the decisions.

Gauging how much residue, crop vegetation remains from the previous year
on the soil, local county agents makes decision. Difficult remote
sensing problem. Dead vegetation is same color as the soil. Can we come
up with some way to estimate how much residue is there. Now the standard
is to make a transect, counting how many points are veg, and how much is
soil. Is there an alternative?

A.  a lot depends on where you are, terrain, crop.

B.  Crop residue, what type of sensing do you need for this, what
    sensors do you need for this, should this be on a wish list?

C.  Probably precision ag users would need this information.

*What are the core elements in agriculture that can benefit potentially
from NASA capabilities::*

a.  *Timeliness*

    i.  *Of data delivery*

    ii. *Of repeat coverage*

b.  *Landcover*

    i.  *Crop residue*

    ii. *Carbon sequestration*

    iii. *Field boundaries (for wildlife and fisheries)*

    iv. *Crop type and invasive species*

Defining core elements for ag support, physical elements and
characteristics of system, ie. Timeliness.

-   Pushing the forecasting issues, risks of extreme events.

-   To what extent should this focus on the carbon sequestration? This
    is all part of the system? According to USDA forest are included in
    ag.

-   Carbon issues are in the carbon management area.

> Precision ag- encourage ppl to plant certain things on their field
> borders. Understating relationships btw. Crops and neighboring
> wildlife. \*\*\*\*
>
> Identify specific species that farmers plant, scale this up to look at
> certain crops next to certain forest types, and what are their
> relationships. Farmers were originally against this practice since
> they thought it would hurt crop yield, but it helped.
>
> *Forecasting and modeling is Key. Want to know what will happen in the
> next year, season, 10 years (ers is doing 10 year forecasting).
> Anything better and more accurate is helpful, help plan better.*
>
> *Need data to monitor compliance, risk management. -- need to provide
> information for farm subsidies. A lot of responsibility on USDA, on
> monitoring and on if farm programs are running as they should. Need
> data on the farm level for this.*
>
> *Q. (Chair) Air temperature, soil temperature- both important for ag.*
>
> *Are there other parameters that would be of value:*

-   *landcover issues*

-   *solar radiation*

-   *timeliness:*

    -   *in terms of delivery of data products*

    -   *frequency of delivery, impt. For growing season.*

-   *Data quality issue*

    -   *Cloud cover*

    -   *Data fusion being able to leverage different data sources*

    -   *Agriculture needs to be included when planning missions*

    -   *Calibrate systems specifically to crops (based on crops)*

*Crop stage, emergence, senescence. Critical element. Requires time
series finer than are now available. Suggests more frequent
observations, or data from different systems, if data were to be
characterized well enough, or a series of micro-satellites. Observations
frequent enough to characterize crop stage, and crop phenology.*

*We now have decent temp for national assessments, but terrible crop
stage information, mutes value of temp data. Crop stage issue- is a key
element for FAS for global analysis users, and is not being addressed
well. Emergence senescence are 2 key elements.*

*(Different crops have different stages.)*

*Improvement of remote sensing data and model interface. Crop phenology
models are good. Improving interface could be useful. ARS is working on
this.*

*Meaning: crop models do a good job of predicting crop phenology. Using
remote sensing in these models is less developed. This is a NEED. To
capture the full use of this.*

-   *the parameterization of crop models for large area studies is
    difficult and time consuming. Initializing and updating the model --
    difficult. Technically difficult. Delineating the field units, soil,
    weather data, etc. many models are good with the validation part.*

*- Timeliness, need for short term decisions.*

-   *Providing longer term context in which short decisions are made.*

-   *Temp trends, but farmers have a short term perspective. Presenting
    the short term in the longer term context.*

-   *Have the shorter term embedded within the longer term context.*

-   *Timing of planting may need to change in the future. Changing
    assumptions of when water will be available in the future.*

*Interfacing with models, models of pests. There is a need for
improvement of this interface. This helps on both the short term scale
and the long term scales. Pests are a major problem, in short and long
term scales.*

Most of the NASA missions are science missions not operational. No
guarantee of data continuity, data gaps (Landsat). Important element to
understand within mission continuity.

*Data redundancy- multiple data and produce one index. Very important to
have redundancy.*

*Relationship between the EDR and what we are talking about here.
Compare those with what we need . if products we need are not on that
list, it is an issue. Also an issue if they are on the list due to data
continuity issues. Links all applications groups to research community.*

Q. Is it feasible that an agricultural mission would be part of NASA's
charter?

> (Chair): this is an important question -ideally there would be a
> mission that had a research and applications mission. Need to know
> what the application requirement are before design. Maybe possible?
> The structural problem now getting applications requirement addressed
> in the long term. No agency in USG with this responsibility.

*Is animal husbandry/production included? What are the animal needs?*

-   *Hydrology, and water quality, are important issues farm
    environments, buffers. Asses this.*

-   *Air quality, feedbacks btw airquality and ag, methane, and pasture,
    livestocks., burning of crops.*

-   *Hydorlogy issues, moisture*

> Linking flux towers and remote sensing. Is there a similar interest in
> ag world? Yes. Carbon sequestaion in ag environments.

Forecasting -- NASA has a role in the modeling of soil moisture. Good
soil moisture has values for seasonal and temp. forecasting. Soil
moisture info is avail from remote sensing sources.

soil moisture- what are the parameters for the products that you are
looking for resolution, timeliness.

Limitations"

-   spatial resolution passive is 10km and up\*\*\*sufficient for
    regional, want to get to a field level. 3km came from the antenna
    limitations, not from the users. 3km best could do credibly.

-   vertical penetration depth 5cm(addressed through data assimilation
    system)

-   difficulties heavy veg and closed crop canopies.

-   Optimal sensor looks like that minimizes these difficulties- looks
    like HYDROS. (3km)

-   Has anybody looked at using geostationaly data for soil moisture?
    (sam)

-   Thermal bands at goes satellites and backing up soil moisture-
    active research. If flown on geostationary would improve soil
    moisture measurements. Geostationary is 5-10 m Down scale using
    thermal tm.

-   European system operating visible and near ir bands. Look at this
    area. Looking at the diurnal cycle. Know when rain is occurred,
    approximates the amount of moisture, and the drying demand. After a
    while you know enough of the regional soil. Requires sophisticated
    logic, but with good geostationary. For technology with a passive
    radar on a geostationary data.

-   Start brining up geostationary data? MSG- Meteosat 2^nd^ generation.

    -   Lots of science and applications that geostationary satellites
        can solve. Especially in ag. Limited to lower
        latitudes.\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* structural changes
        with monitoring shadows.

-   heat capacity mission 1980 why didn't wasn't that a success? Looked
    at middle of day/ night. If there was rain less change, and if there
    is no soil moisture there would be a lot of change.

-   Only 1 visible band. Concept of heat capacity mapping, veg ruins the
    conductivity curves. Clouds and angles were problematic. Want to
    look at the hourly changes of what is going on.

> Need of data continuity, mechanism for users to have an impact on NASA
> missions, links between ag efficiency ant other applications in the
> NASA program water quality, air quality etc.

Question2.

-   *What are the similarities and shared interests with this program
    element with other research NASA does. How can this element draw
    from the research side? Are there other ideas that it can draw on?*

    -   *Validation of LAI and FPAR data sets. W/ ground data. This
        relationship is less studied than in AG. More need for ag
        validation. More work done on examining how well those data
        products are doing in an ag setting. Most is done small time
        periods in forests. More attention to other crop cover. Need
        this for error bars in models. What is the impact of error on
        parameter estimation. Expand validation*

Decisions being made, and understanding the decision process. But is it
clear that decisions are being made, who is making them, do they have
the data they need?

Looking to 2011, what would we like to build toward?

Look at different scales. Need data of different types at diff scales.
Does it make sense to look at agriculture at different scales. Global to
within field. 5 years from now what you build toward is a system that
has reliable timely data that meets needs.

Soil moisture, temp, precipitation, but still need flexibility.

*Define specific needs at variable scales. Is this a way of organizing?*

-   *different scales of questions that you are dealing with. Individual
    grower level. Want soil moisture in field. change over time . at
    continental level, need soil moisture at much coarser scale.
    Decisions and tools are different.*

-   *Follow a similar idea to google earth.*

-   *Global, regional, local. NASA seems to be focused on the regional
    to global scale.*

-   *Can use NASA capabilities down to the local scale.*

-   *Limit to how local you can get, due to data limitations.*

-   *2011- modis/ikonos/landsat model is obsolete. Need to get over this
    by then.*

-   *Need to beat back our temporal spatial limitation by 2011. We are
    stuck in the traditional view. Need to make the leap to what the
    need. We don't have the feedback loop back to the system designers.*

-   *We don't know what decision makers could use, that we can provide
    them.*

*2011 vision- "Google Vegetaton" -- earth vegetation zoom into it to
present, and models going into the future.*