Land Use/Cover Data -- Answers to Activities

Activity 2.1 What's the Problem Anyway???

As you go from group to group, help students frame the issue; ask them what they want to know about it, and why that would be interesting to know. Once they clarify their own interest, specific problem formulation is much easier.

Depending on your own research interests, choose a small and clear example of narrowing a broad area of interest down into one or two research questions. Alternatively, use Supporting Material 2.1 over the course of this activity, walking students step by step through the overhead. Students will understand the task more easily after you demonstrate the process.

Give students some time to formulate problem statements and specific research questions in small groups or pairs. Then display students' problem formulations and research questions on an overhead transparency. In order to help them see which problem formulations are better than others, you may use your own approach or point out and discuss at a minimum the following issues:

• Are the statements clear/confusing?
• What are the important concepts in this problem? What data do we need to get?
• Is the place, time (span), geographic scale, the researched population etc. specified?
• If you were to undertake the research on this problem, would you know what to do? (And so on...)

Also discuss how, depending on one's perspective (paradigm, underlying theory, etc. -- if this language is appropriate for your students), a problem may be researched in more than one way, partially because the problem is stated in different ways, but also because what is acceptable evidence and methodology may differ.

B The operationalization is, again, demonstrated on Supporting Material 2.1. Be flexible in allowing answers but ask students why they chose a particular measure for a given variable.

C Discuss with students exactly what kind of data they would have to collect in order to answer the research question as stated. The example on Supporting Material 2.1 hints at some of the problems that can be expected.

• Are these data available? Where, from whom?
• Is there more than one way (there is!) to operationalize the chosen variables?
• How would the data search, the data analysis, and the likely results differ if other variables, other measures were chosen?
• Which measures seem more appropriate than others?
• What do these measures cover, what do they leave out?
• Thus, how limited or applicable are the likely answers we will find?

Here is an additional example besides the one provided to students on the Student Worksheet:

Problem: Has population growth driven (i.e., caused) deforestation in the Brazilian Amazon over the past 50 years?

Note: This question implies nothing about the strength or importance of this driving force. In the interpretation of results, this should be assessed.

Remote sensing data for Brazil / NOAA or similar sources
Birth and death rates for Brazil / UN Demographic Yearbook

You might have to assist students in thinking of data sources, or else let them do some research of their own in the library or on the Internet (or see Supporting Materials). Note the limitations of these data with the students (remote sensing in tropical areas (clouds); regrowth after clearing is very similar in reflectance values to older growth forest; boundary recognition on remote sensing imagery; demographic units are not necessarily the same as ecological region, etc.).

D Reinforce the distinction between data and information provided on the Student Worksheet 2.1. The specific results of this activity depend on the hand-out you will put together for students. Point out to students that data and information are sometimes hard to distinguish. All data are information, but not all information is data.

Information is -- broadly defined -- any sensory detail that contains meaning. It may or may not be relevant to the issue we would like to research. And it may or may not be direct input into our analysis. When we undertake qualitative research the distinction between data and information becomes rather blurry. Often times, information gives us clues to the background or context of the researched problem.

Data is that specific portion of information that directly enters into our analysis. It may be quantitative or qualitative, but it is always specific to what we need for the analysis.

E You might have to jump start the brainstorming process by giving students an example of your own research or by referring to Supporting Material 2.1. Unless this module is taught in a research methods class, it's not so important for students to have this technical/statistical/ research know-how, but to think logically and creatively through the research process.

F You might demonstrate this again with an example from your own research; otherwise no specific instructions here.

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Activity 2.2 Getting Wired for Global Change Research

Activity 2.3 Naming It -- Counting It: How Terminology Matters

FAO Production Yearbook 1970

Definitions:

Arable land: Land under temporary crops (double cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens (including cultivation under glass), and land temporarily fallow or lying idle. Within the scope of this definition there may be wide variations among reporting countries; the dividing line between temporary and permanent meadows, for instance, is rather indefinite; the period of time during which the unplanted land is considered fallow varies widely.

Land under permanent crops: Land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee and rubber; it includes land under shrubs, fruit trees, nut trees and vines, but excludes land under trees grown for wood or timber. A problem arises here as to whether bamboo, wattle, or cork oak plantations should be included under this heading or under forest land. Data changes are due to actual changes in land use categories (esp. in Europe and North America) and improvement in statistics (esp. from other continents).

Permanent meadows and pastures: Land used permanently (five years or more) for herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land). Permanent meadows and pastures on which scattered trees and shrubs are grown should also be included in this category, although some reporting countries include them under forests.

Forest land: Land under natural or planted stands of trees, whether productive or not. It includes land from which forests have been cleared but that will be reforested in the foreseeable future. The question of savanna raises the same problem as mentioned (under permanent meadows and pastures).

Notes:

• Crop areas generally refer to harvested areas, with exceptions: tea, sugarcane, cereals -- sown area; grapes, abaca, agaves, hard fibers -- planted area.
• Continental, regional and national totals shown for most agricultural items, but world not covered evenly or completely because of paucity of available data.
• Production of crops is reported by countries by calender year, agricultural years, marketing years. These are then allocated according to the calender year in which the entire or the bulk of the harvest occurred. Most figures refer to the calender year, with few indicated exceptions. Livestock numbers per 12 month period ending Sept. 30.
• Some countries are not shown in the tables, but included in the totals. Explanation: very rough estimates, only reliable to the order of magnitude, thus good enough to adjust the total but too rough as a country estimate. In some cases, estimated adjustments to make up for deficient geographical coverage of the country.
• Changes in country boundaries, or FAO classifications of regions are indicated separately in country notes.
• Any other additional information in footnotes beneath tables, or at the end of the volume.

Definitions:

Arable land: First sentence is the same as in the 1970 yearbook. Large shifts in African countries due to the exclusion of what is considered fallow land resulting from shifting cultivation.

Land under permanent crops: First sentence same as in the 1970 yearbook. No mention of second or third sentence anymore.

Permanent meadows and pastures: First sentence same as in the 1970 yearbook. No mention of second sentence anymore.

Forest land: Includes forest and woodland, i.e. land under natural or planted stands of trees, whether productive or not, and includes land from which forests have been cleared but that will be reforested in the foreseeable future. No mention of second sentence from 1970 yearbook anymore.

• Specific deviations from the definitions, measurements, aggregation etc. in selected countries are listed by type of crop and country if necessary.

• Crop areas mostly refer to harvested areas, but some exceptions refer to planted areas.
• Data on yields of permanent crops are not as reliable as yields of temporary crops.
• When considering the section on land use it should be borne in mind that definitions used by reporting countries vary considerably and items classified under the same category often relate to greatly differing kinds of land.

Definitions (changes since 1980):

Arable land: Just the wording is different: Aabandoned land resulting from shifting cultivation is not included in this category.@

Land under permanent crops: Same as before.

Permanent meadows and pastures: Same as before.

Forest land: Same as before.

Notes (changes since 1980):
• Figures from individual countries may not add up to the totals given for the entire table due to independent rounding of country totals or of overall totals.
• New note with cereals: difference between sown and harvested land is negligible in normal years (but not so in drought or other bad years)
• For specific crops there are minor changes that reflect changes in agriculture in general, e.g., mixed cereals or buckwheat are not individually listed anymore, but they still figure into the totals of cereals.
• Livestock numbers refer to calendar years with few exceptions.

* At the time of the publication of this module, the 1990 volume of the FAO Production Yearbook was not available. Since the point of the exercise is to pay attention to small details and finding differences between publication years, rather than what exactly the 1990 Yearbook said, the "Notes on Tables" of the 1991 FAO Production Yearbook have been included in the Appendix (in the Print version) - go online here to get more current information from theFood and Agriculture Organization (FAO). 1946-present. Production yearbook. (annual publication). Rome: FAO. See online at FAOSTAT or David Lubin Memorial Library or WAICENT Portal .

Activity 2.4 Reading Between the Points ...

Source: based on Skole, D. 1995. "Data on land use change: Acquisition, assessment, and analysis." In: Changes in land use/land cover: A global perspective, eds. Meyer, W.B. and B.L. Turner, 444. Cambridge University Press.

The kinds of answers to look for in students' discussions on the implications of sparse sampling would include:

• most importantly: increase the sampling density (get more data!);
• cross-check with data from other sources; if they are compatible, fill in the holes.

Activity 2.5 Checking for Water-Tightness

Activity 2.6 Looking at the Blue Planet Through Rose-Colored Glasses

Then explain to them the term "error" and let them name a few sources of errors (e.g., measurement instrument broke down, data were lost, etc.); you may want to include the notion of "random error," which represents the natural variability in the occurrence of a phenomenon. Note the difference between a measurement instrument that is occasionally out of order (>> error) and a measurement instrument that is badly calibrated (>> systematic error).

Use the collection of newspaper articles that students gathered to manifest the distinction between the two. If students can't decide whether it's bias or error that they are confronted with, let them discuss the arguments for either case with each other. Help them ask the kind of questions that will allow them to make the distinction. Find consensus. Also make sure to point out, that sometimes we just can't tell. We certainly do not always know whether data contain error or bias or not!