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Нажимая кнопку "Продолжить", я принимаю политику конфиденциальности

  1) What is FAO? 

  2) Is land suitability assessed and classified without respect to specified kinds of uses?

  3) Should evaluations take into account the physical, economic, social and political context of the area concerned?

  4) What is LUT?

  5) An   economic land  evaluation  is  based  on  some   economic

measure of net benefits‚ isn’t it?

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Find the English equivalent s of the following Russian

Phrases in the text .

  1)  Продовольственная и сельскохозяйственная организация ООН;  

  2) экономический критерий; 

  3) многосторонний подход;   

    4) классификация земель по системе «Службы мелиорации и сохранения плодородия»;   

  5) земельная площадь.

Fill in the gaps with the words from the box.

Each word can be used only once.

determine economic implemented areas manager classified evaluation political suitability inappropriate

 

 

   1) Land __________ is assessed and __________ with respect to specified kinds of uses.

  2) Evaluations should take into account the physical, __________, social and __________context of the area concerned.

 3) There are no bad land __________, only __________ land uses. 

4) A physical land evaluation is based only on physical factors that __________ whether LUT can be __________ on a land area, etc.

5) The physical __________ reveals the nature of limitations and hazards, which is useful information to the land __________.

Circle the Odd Word Out.

1) FAO‚ Guidelines, LUT;

2) by, is, out;

3) assessor, evaluation, evaluator;

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 4) are, classified‚ take;  

5) economic‚ financial, hazard.

 

Translate the sentences into English

using the Gerund.

  1) Мы знаем, что разработчики проектируют новую модель классификации земель.

  2) Идея использования пустующих территорий под застройку в этом района не нова.

  3) То, что господин  Блок  принял участие в проведении этого совещания, очень помогло нам.

  4) Общеизвестно, что бережное отношение к земле приводит к повышению её плодородия.

    5) Измерение земельной площади данного участка представляет большие трудности.

 

Text

Evaluating Land Qualities from Diagnostic

Land Characteristics

       Since land qualities, by definition, can’t be directly measured in routine survey, their severity levels or single-factor ratings for each evaluation unit must be inferred from one or more diagnostic land characteristics. Diagnostic land characteristics (LC) are the land characteristics that will be used to evaluate the land qualities (LQ). They must be measurable at the appropriate scale, and well-related to the land quality (which is why they are called ‘diagnostic’). There may be a choice of land characteristics, in which case the simplest or cheapest to determine should be used.

       For example, to evaluate the land qualities ‘erosion hazard’, we may choose as diagnostic land characteristics ‘slope’, ‘rainfall intensity’, ‘topsoil particle-size distribution’, and ‘topsoil mineralogy’.

      Land indices (formerly and confusingly called parametric indices) are point systems with each diagnostic LC contributing points to an overall value,  which  then  is  classified  into  a  severity level. It

differs from empirical statistical methods in that classified LCs can be used,  and   that  there is  rarely  an  empirical   statistical  basis  to  the

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combination.

       The indices may be additive (i.e., add up the individual point values) or multiplicative (i.e., multiply the individual point values, and then normalize) or a combination of arbitrary arithmetic operations, resulting in a ‘continuous’ value (which will in general be an integer only for additive indices), which is then classified into severity levels by arbitrary cut-off points. For example, on a scale of 0-100, 80-100 could be classified as ‘slight limitation’, 60-80 as ‘moderate limitation’, etc. Note that there is no objective basis for this classification nor for the original point system.

      Land indices can in some degree compensate for problems with matching tables. Typically, the same table is used, but each row is assigned a point value, and each cell is worth a certain number of points. Each diagnostic LC is rated separately, and the points are added, multiplied, or combined according to some other rule. This allows the evaluator tremendous flexibility (and subjectivity). Interactions can still not be accounted for in a purely additive or multiplicative index, since each row is evaluated separately, but it is possible to use cross-products of point values for some LCs along with sums for others to get some approximation of interaction effects.

      Land indices are not much used to estimate LQs, more to go directly from LCs to suitability as in earlier ‘parametric’ methods of land evaluation.

       Advantage: Provides a more-or-less continuous scale of the land quality, allows a large number of LCs to participate in the rating, each more-or-less weighted according to its importance.

      Disadvantage: Highly subjective, appears more precise to the casual observer than it is in fact.

      Another kind of land index (also formerly and confusingly called parametric indices) is point systems with each diagnostic LC contributing points to an overall value, which then is classified into a severity level, with the difference that the LC is given points according to its value on a continuous scale, not according to its class. It differs from empirical statistical methods in that there is rarely an empirical statistical basis to the combination.

      For example, each cm of soil depth up to 150cm can be assigned 0.2 points,  so that soil depth gives 0 to 30 points towards the

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land index; each % coarse fragments can subtract 0.1 points from a maximum of 10 points, so that coarse fragment content gives 0 to 10 points towards the land index. As in the continuous case, land characteristics are weighted by assigning them different maximum points.

     One way to determine the severity level of a land quality is to simulate it over time, using a dynamic simulation model. For example, we could estimate the land quality ‘moisture availability’ from time series of the diagnostic LCs such as rainfall and solar energy. This is especially appropriate if the dynamic or time-dependent nature of the LQ is important, for example, moisture stress at critical times. The results of the simulation are the behavior over time of the Land Quality. This must be classified to severity levels. For example, ‘high

moisture availability’ could be defined as less than 10% frequency of three or more consecutive days with a moisture deficit in the growing season.

      Advantages: (1) the model provides a more-or-less mechanistic view of the land quality, i.e., its causes as well as its severity level; (2) dynamic simulation provides a time-series of results.

      Disadvantages in a land evaluation context: (1) high data requirements, (2) difficult calibration, and (3) the considerable expertise and judgment needed for their correct application.

Exercises