Land Resources of Russia

Climate, relief and soils are considered to be the main natural factors affecting yield. Flooding, slopes and dissections are factors limiting land cultivation. The rate of biomass production and plant development is in proportion to the increasing of the sum of temperature above 10oC (Shashko, 1967). The analysis of temperature conditions over the country recognizes only a few places where the sum of the temperatures above 10oC exceeds 3,000o.

Precipitation is another important climate factor. The coefficient of biological productivity (CBP) is functionally linked with the coefficient of climate humidity (CH)

CBP=1.00, if CH=1.00;
CBP=0.97, if CH=0.90;
CBP=0.94, if CH=0.94;
CBP=0.86, if CH=0.77, etc.

The increase of the climate humidity above CH=1.0 does not support yield. Geographically, the optimal region for water provision is allocated within the south-taiga and the forest-steppe zones in Russia.

Among other climate factors, seasonal distribution of temperatures and precipitation, droughts and frosts, and winter severity all must be mentioned. Numerous investigations found that all these yield-affecting climate details are very well correlated with the coefficient of climate continentality (CC) (Ivanov, 1949). The more continental the climate, and the higher the value of the CC, the more frequent and severe are the negative weather events affecting yield.

Slopes less than 50 do not limit cultivation of tilled crops and vegetables. Cereals do not have limitations up to 80. As a rule, the slopes less than 120 can be cultivated under special technology. Slopes more than 120 should be terraced, which is economically unprofitable in Russia.

Crops "select" various soil conditions. Cereals (Karmanov, 1980) show a preference for the following:
1. Water-stable granular structure, low bulk density (about 1.1 g/cm2), very high porosity (about 60%), high water storage capacity and plant water availability, sufficient hydraulic conductivity without any water stagnation;
2. Sufficient essential plant nutrients (N, P, K);
3. Sufficient other plant nutrients, microelements, ferments, and high biological activity;
4. Sufficient humus content;
5. Considerable thickness of humus horizon with gradual decline of humus content with depth;
6. Texture - light or medium loamy for sod-podzolic soil, medium and heavy loamy for grey forest and chestnut soils, and heavy loamy for chernozems.

Soils close to the agronomic optimum should not have characteristics limiting fertility (compaction, cementation, gleyization, stoniness, salinization, etc.).Obviously, such optimal composition of soil parameters could be observed only for some types of chernozems. In order to simplify the experimental part of the data collection and analysis, the following four soil groups with similar soil characteristics have been distinguished:
1. Sod-podzolic soils prevail with inclusion of light grey forest soils;
2. Grey and dark grey forest soils prevail with inclusion of leached and podzolized chernozems;
3. Leached chernozems typical of the forest-steppe zone prevail with possible inclusion of ordinary chernozems;
4. Chernozems of the steppe zone (ordinary and southern) and chestnut soils prevail.
The first group is exploited humus content, stocks of mobile phosphates and potassium and pH value in the arable layer.
The second group in addition to the characteristics used by the first group applies the thickness of humus horizon.
The third group exploited thickness of humus layer, humus content in arable horizon and humus stock in 0-100 cm layer.

Soil bonitet (SB) refers to relative quantitative assessment of soil productivity obtained at the compatible input level (Karmanov, 1980). The range of the SB ratings has 100 points, from which the highest yield has maximum points. There are a few examples, where some very rarely extended soils with the highest yield performance have more than 100 points. The SB point has a yield equivalent. For the 90's, 25-30 kg/point has been considered as very low yield; 45-50 kg/point, medium; and 65-75 kg/point, very high. Once average productivity is increased, the SB must be changed accordingly.

Calculation for Local Scale. The SB is calculated by administrative unit (oblast, districts, farms), correlating land characteristics with long-term statistical yield for a given crop/group of crops having common ecological requirements. At the local scale, the climate factor has been considered homogeneous and is thus ignored.

The basis for bonitet calculation is large-scale soil maps and lists of soil types, land uses, and their area. For distinguishing different and common soil features, the results of chemical analyses and field morphological descriptions are widely used. The data on available nutrients content are derived from agrochemical observation. Soil fertility is calculated and verified using yield data for crops, gardens, haylands and pastures. The following data have been handled for this purpose:
· Statistical data on the extent of cultivated land at farms;
· Yield obtained at agricultural experimental stations; and
· Yield measured at farms.

For yield data comparability, climatic factors (mostly water and heat provision) and input level parameters (fertilizer application, technical supply, and human resources) are used. It is assumed that: (1) SB can not be based only on the actual yield in a certain area, because this does not include economic and human factors that can influence particular yield; (2) the SB rating should not depend on the present land use.

The methods of the local bonitet calculation differ throughout the country and can be combined into two main classes according to the criteria used and into five groups regarding the calculation procedure. The first class (A) elaborates SB ratings on the basis of numeric values of soil properties that have been correlated with the yield; the second class (B) creates SB ratings on the basis of yield that has been related to soil groups. In class A, there are three methods of calculation: In group A-1, SB ratings are calculated in proportion with numeric values of soil properties that closely correlate with the yield of the main agricultural crops (usually wheat). The soil property scale is considered to be the basis, and is used with adjustment coefficients for detailed evaluation of soil cover. The main feature of the approach is that only those soil characteristics are chosen which SB ratings correlate (with 5-10% error) with the yield range scale.

There are two steps done in the calculation under Scheme A-2. The first one usually is the same as in A-1, but the requirements as to the accuracy of the relationship between the yield and soil properties are not so strict. The second step is to correct the SB ratings acquired. This can be done by different methods:
· Averaging soil characteristics with the yield range;
· Incorporating separate yield ranges according to the properties of the main soils by using yield coefficients or total productivity parameters; and
· Indirectly correcting, for instance, including the influence of a certain soil characteristic on the yield.
The following steps are distinguished in the A-3 approach:
· Compiling preliminary SB ratings for soil properties, which are closely correlated with crop yield;
· Developing a mathematical crop-specific productivity model; and
· Calculating SB ratings based on comparable productivity.

There are two ways of choosing the soil properties used in this subgroup: (1) empirical, which is based on general knowledge of the significance of certain soil parameters for crop productivity and (2) statistical (correlation) analysis. Usually these approaches are combined. Correlation is calculated using direct yield measurements on a certain soil, State strain test station's data, or yield data in typical farms within a specific soil unit. Bulk data on economic productivity for a certain period (usually for 5 or more years) is also used.

Regression equations are produced if the influence of all other factors is negligible. Elimination of this influence decreases the uncertainty in the evaluation procedure. For this purpose, economic factors and, in some cases, climatic factors are considered. Leveling of climatic factors can be achieved by climatic rationalization of the territory.

In the class of local bonitet calculation known as B-1, the land type and/or agro-economic soil groups being assessed use numerous statistical data of long-term yields. Depending on the goals stated, either general rating scales or scales for particular crops are produced. There are two ways of binding yield data and soils: (1) only the quality composition of soil cover is taken into account (% of soils on the farms) or (2) soil properties are ranked and related to the yield.

Class B-2 (direct yield measurement) uses data that are collected for definite crops on the particular soil units where given soil properties sequentially change to give sufficient information on the relation between yield and soil characteristics. Correlation coefficients, regression equations, graphical bond analysis, and trend calculation assess the relation SB ratings are based mostly on soil properties that are characterized by numeric value. Such factors as degree of erosion, podzolization, gleyization, alcalinization, salinization, etc. have certain gradations and are taken into account by using adjustment coefficients. These coefficients are also used to correct the bonitet ratings by texture, stone content, and the size of fields. They are calculated by comparative analysis of the yield for a given kind of soil, whether or not it has the features listed above.

Calculation for National Scale. The overall goal of the national SB is to make relative analysis of the soil productivity across the country. At the national scale, climate diversity plays a very important role and must be included into the analysis. The concept of zonal soil has been applied to achieve this task. The basic assumption is that the zonal soils include favorable characteristics, which do not negatively affect the yield. It is proposed that they allow the bioclimatic potential to be completely utilized. The advantage is that the approach is based on the actual yield performed by the best soil. The following equation has been proposed to calculate SB ratings for zonal soils:

The value CC+70 is taken because the yield is not in direct proportion with increasing of the continentality;
The value 8.2 represents an empirical coefficient obtained for cereal to get 100 points for the best yield performing by the weakly leached, very deep chernozems of Krasnodar Krai.

A similar approach is applied to create soil bonitet ratings for other crops. However, it should be mentioned that other crop-specific coefficients must be empirically distinguished. For example, the formula for zonal soils has been transformed for sunflower into following:

where 6.8 is the crop-specific coefficient, and 0.2 and 50 are corrections on CH and CC, respectively.

The list of computed total soil quality indexes (V) for major cultivated soils of Russia is given in the CD-ROM database. The highest V value (1.0) is given to typical and weakly leached chernozems and very deep chernozems of Krasnodar Krai, which provide the best yield performance. None of the cultivated soils of Russia have a value of 1.0 for sugar beet, as the best soils are found in Ukraine. The values of V demonstrate deterioration of the soil quality toward the north (sod-podzolic and grey forest soils) and to the south (chestnut soils), which is in line with general geographic knowledge.

The V completely coincides with the existing agronomic experience in the country. In general, it fits well the soil suitability to various crops. For instance, loamy sod-podzolic soil (eutric podzoluvisols) is considerably better for grasses (V = 0.87) and does not meet sugar beet requirements (V = 0.42). Interpolation of V on nonozonal soils follows a simple procedure based on local SB, which ranks soil productivity in accordance with agronomically sound soil characteristics.

Soil-Ecological Evaluation. The methodology involved with soil-ecological evaluation of productivity allows calculating soil-ecological indexes and SB for agricultural land and various crops, haylands, and pastures. It can be applied at different scales: from fields to oblasts to zones. The method gives comparable results for the whole of Russia. Unlike regional assessments, it is based on the main soil characteristics and climatic parameters.

The procedure for the calculation includes three steps: (1) preparation of the soil, (2) agrochemical and agroclimatic data entry, and (3) calculation.

Crop-Specific Soil Bonitet Calculation. The following data are required:
· Location (administrative and geographical);
· Natural zone indication;
· Type of land use and its extent;
· Information on irrigation;
· Agroclimatic data (mean annual precipitation, the sum of the temperatures above 10oC, and temperature of the warmest and the coldest month); and
· Soil cover data: dominant soil type and subtype, texture, erosion, humus content, stoniness, wetness, salinity, deflation, humus horizon thickness, pH, plant available phosphorus and potassium.
The soil-ecological index (SEi) is calculated as follows:

The advantages of the method are (1) that it is uniform and (2) that it may be applied for land suitability assessments and to make prognoses of land productivity when changing crops, developing irrigation/drainage, etc.

Ivanov N.N. 1949. Landscape-climatic zones of the globe. Notes of the Geographical Society, Vol. 1, (new Series), 228 pp. [In Russian].

Guidelines and Technology of Soil-Ecological Assessment and Soil Bonitet for Agricultural Crops. 1990. All-Union Academy of Agricultural Science, Moscow, 114 pp. [In Russian]

Karmanov I.I. 1980. Soil Fertility in the USSR (Natural Regularities and Quantitative Assessment). Kolos, Moscow, 224 pp. [In Russian]

Shashko D.I. 1967. Agroclimatic Regionalization of the USSR. Kolos, Moscow, 334 pp. [In Russian]

Weather hazards cause considerable or complete loss of productivity and are agrometeorological risks for crop production. Such phenomena as spring frosts, droughts, high winds, hailstones, showers, strong frosts, and ice rims occur in Russia.

The spring frost is a drop in air or soil temperature down to 0^oC and below during the vegetative period. This phenomenon can be advective or radiative, or of a blended, advection–radiation origin. The advection-convective frosts of early and late spring are the most dangerous for crops. The spring frost affects the duration of the vegetative period. Spring frosts are common in regions where the probability is high that the average perennial date of the latter frost in the spring will appear after the average perennial date of emergence of the main agricultural crops.

A drought is a considerable—as compared with a norm—deficit of precipitation during a long span of vegetative period. It results in soil cessation and loss of plant productivity or plant destruction. The intensity of a drought is estimated by interrelating the air temperature and the amount of precipitation for a growing season. For example, drought hazard might be derived from the hydrothermal coefficient:

Drought might affect agricultural plants differently depending on the stage of plant succession and vegetative season.

The lodging of sowings decreases crop productivity by 20-30% in Russia. This phenomenon causes deterioration of grain and straw, hinders the harvesting of grain crops, and fosters the deterioration of grain by fungal diseases. Rains and wind are the main causes of a lodging of sowings. The wet plants become heavy, and the wind enhances the effects of rain. In most cases, lodging occurs at a wind speed of not less than 3-4 m/sec.The negative consequences of a lodging can be reduced by introducing management techniques and special measures. The most effective of these are sowing of varieties that are resistant to lodging and the treatment of plants by retardants.

About 3-5% of sowings are completely wiped out by hailstones each year in Russia. The degree of damage by hailstones depends on plant age and the duration and intensity of the hailstorm. Hail’s effect on flowering and maturing plants is considerable, and injured plants can generally not be recovered. Hailstones also fall out of thunderstorms. Usually, hail falls from one out of 10-20 thunderstorms, depending on the region. Hailstones occur at the end of spring and kick off the summer season. They fall along with shower precipitation, without uniformity. There are about six to eight days with hailstones each year.

Frost killing affects winter crops in Russia when soil temperature drops below a critical level for two to three days. A decrease in plant density is observed when soil temperature at a depth of 3 cm drops to -15^oC. One of the simplest methods of assessing the probability of frost killing is to use the temperature of the coolest month.

The rotting of plants in winter occurs as a result of long-term (more than 50-60 days) coverage of plants by a bull snow (thicker than 30 cm) in a shallow (less than 50 cm) frozen soil. The temperature of the soil under a winter crop is about 0^oC. The rotting of winter crops is a destruction of plants from the stagnation of melting water in the spring. The inundation of winter crops might occur if the amount of precipitation exceeds 200 mm in the autumn. Other important factors are the depth of soil freezing and precipitation in the spring.

Gringof, I.G., V.V. Popova, and V.N. Strashni. 1987. Agrometeorology. Gidrometizdat, Leningrad, 310 pp. [in Russian].

Agriculture is a complex sector of the economy. It includes a great diversity of activities, of which production and processing are the major parts. This CD-ROM introduces a simplified approach that focuses on production aspects, assuming their direct linkage with terrestrial ecosystems and their carbon-related performance.

According to the state land account, Russia has an extended amount of managed (agricultural) land: about 212 million hectares (ha) (Land of Russia…, 1995). This territory includes about 130 million ha of cropland. The rest (about 80 million ha) falls into the category of so-called “other agricultural land” that we assumed to be hayland. Obviously, commercial cropping is tremendously different from place to place; it depends on diversity of natural agro-ecological potential of the region, markets, etc. Despite the huge extent of the country, Russia’s total agricultural area occupies about 12% of the territory. This fact indicates the rather limited natural potential of the country for agriculture that is caused by a cold and humid climate, mountain relief that is unsuitable for cropping, and expansion of poorly drained plains. The geographical variety stipulates production of different crops, in which cereals, grasses, and perennials prevail. These crops occupied more than 75% of the sown area of the country in 1990.

The research is derived from available crop statistics. Agriculture of Russia (1995) reports data on the average yield by administrative oblasts that, however, are not sufficient for phytomass definition, as they do not consider the content of all phytomass fractions like straw, above ground residuals, and roots. We introduce crop and yield specific regression equations (based on Rodin and Krylatov, 1998) to fill this gap. To convert the amount of yield and by-products expressed in metric phytomass units into carbon units we apply the coefficient 0.86 for grain and 0.5 for remaining phytomass fractions. The following assumptions have been made in the calculation: 1) living biomass (LB) is equal to net primary production; 2) phytomass of agricultural land is considered as having a yearly life cycle.

where S_al= total area of agricultural land; S_cl= cropland area from statistics; S_past = pasture and hayland area from statistics and extended on the rest of the territory where other than cereals and grass crops are grown, excluding S_per= perennial crops.

Under the calculations, LB of cropland was considered as a sum of yield Y and residuals R. Values of Y were derived from statistics. The value R is a function of Y, depending on specific crop and Y amount. Three phytomass fractions have been distinguished for cereals: straw, surface residuals and roots. Two fractions, namely surface residuals and roots, have been identified for hayland phytomass. Each fraction is calculated by the general regression equation:

where R_Cr and R_h(residual phytomass of cropland and hayland) are represented by fractions X_s (= straw), X_f (= surface residuals), and, X_r (= roots).

Total phytomass produced by cropland and hayland of Russia in 1990 is estimated at 2,186.9 terragrams (Tg) of dry matter (Table 1), of which cropland accounts for 1,441.0 Tg (or 65.9%), hayland 667.5 Tg (or 30.5%). Phytomass of perennials is only 3.6%. Above-ground phytomass is about 58%, and below ground, 42%. The phytomass density differs by agricultural land, and is 1.106 kg/m², 0.845 kg/m², and 3.062 kg/m² for cropland, hayland, and for perennials, respectively. Net primary production of cropland is the highest compared with other land uses (Table 2).

About two-thirds of total agricultural phytomass is concentrated in the zones of steppe and temperate forests (Table 3). The distribution of the average phytomass density is geographically dependent, i.e. from 0.89 kg/m² in northern tundra to 1.04 kg/m² in southern steppe and about 1.00 kg/m² in semi-desert and desert zones. However, this variability is less than that of natural vegetation. The latter can be explained by a narrower ecological niche for agricultural practices compared with development of natural vegetation and the leveling effect of the human impact via management practices.

Agricultural land	Area, 10⁶ ha	Phytomass, Tg Carbon					Density, kg/m²
Agricultural land	Area, 10⁶ ha	Green part	Woody part	Above ground	Below ground	Total	Density, kg/m²
Cropland	130.34	397.4	0.0	397.4	251.1	648.5	0.498
Hayland	78.96	141.2	0.0	141.2	159.2	302.1	0.380
Perennials	2.56	3.6	24.8	28.3	7.0	37.9	1.378
Total	211.86	542.1	24.8	566.9	417.2	988.5	0.464

Table 3. Distribution of agricultural phytomass (Tg, carbon) by natural zones (1990)

Bio-climate zone	Agricultural land
Bio-climate zone	Crop	Hayland	Total
Polar desert	0	0	0
Tundra	0.1	6.1	6.2
Pre-tundra and northern taiga	1.1	3.3	4.4
Middle taiga	27.0	28.2	55.2
Southern taiga	141.8	34.2	176.0
Temperate forest	107.6	24.8	132.4
Steppe	351.5	137.0	488.5
Semi-desert and desert	19.4	68.5	87.9
Total	648.5	302.1	950.6

References

Agriculture of Russia: Statistical Yearbook. 1995. Official Edition of State Committee of Russia Federation on Statistics, Moscow, 503 pp. [In Russian]

Rodin A.Z. and A.K. Krylatov. 1998. Dynamic of Humus Balance in Cropland of Russia. Agroprogress, Moscow. 60 pp.

Land of Russia – 1995, Problems, Figures, Commentaries. 1996. Russlit, Moscow. 79 pp. [In Russian]