Climatically Optimal Planting Dates

COP Determinator (version 1)

I. Savin, H. Boogaard, C. van Diepen, H. van der Ham

EUR 22233 EN - 2007

The Institute for the Protection and Security of the Citizen provides researchbased, systems- oriented support to EU policies so as to protect the citizen against economic and technological risk. The Institute maintains and develops its expertise and networks in information, communication, space and engineering technologies in support of its mission. The strong crossfertilisation between its nuclear and non-nuclear activities strengthens the expertise it can bring to the benefit of customers in both domains.

European Commission Joint Research Centre Institute for the Protection and Security of the Citizen

Contact information Address: I-21020 Ispra (VA), Italy E-mail: [email protected] Tel.: +39 0332 789173 Fax: +39 0332 789029 http://ipsc.jrc.ec.europa.eu http://www.jrc.ec.europa.eu

Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication.

A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/

PUBSY 7094

EUR 22233 EN ISSN 1018-5593

Luxembourg: Office for Official Publications of the European Communities

Reproduction is authorised provided the source is acknowledged

Printed in Italy

Institute for the Protection and Security of the Citizen (IPSC/JRC) Agriculture Unit & Alterra - Wageningen University and Research Centre

CLIMATICALLY OPTIMAL PLANTING DATES

(COP DETERMINATOR, version 1)

I. Savin, H. Boogaard, C. van Diepen, H. van der Ham

Italy, 2007

CONTENTS

Introduction...... 3

Region of interest ...... 5

Algorithm...... 8 Rules for winter crops...... 9 Rules for autumn sown crops ...... 10 Rules for spring sown crops ...... 12 “In-chain-sown” and other crops...... 15

Validation...... 16 Winter wheat...... 18 Spring wheat...... 22 Potato ...... 25 Grain maize ...... 29

Conclusion ...... 32

References ...... 33

Appendix ...... 34 A. Description of tables storing data needed to estimate year specific sowing dates and initial soil moisture ...... 34 B. Field observed sowing dates for winter wheat ...... 43 C. Field observed sowing dates for spring wheat ...... 48 D. Field observed sowing dates for potato ...... 50 E. Field observed sowing dates for grain maize...... 53

Introduction

The Agriculture (former MARS) Unit within the Institute for the Protection and Security of the Citizen (IPSC) of the Joint Research Centre (JRC) in Italy is responsible for the implementation of the actions MARS-FOOD (Crop Monitoring for Food Security), MARS-STAT (Agricultural Statistics in Europe), MARS-PAC (Support to the Common Agricultural Policy).

Historically, the MARS Crop Yield Forecasting System (MCYFS) has been developed in Europe around the Crop Growth Monitoring System (CGMS) (Supit, van der Goot, 2007). The CGMS is the combination of the WOFOST crop growth model and a statistical yield prediction module embedded in an information system storing data in a relational database. Nowadays the MARS Crop Yield Forecasting System (MCYFS) is fully operational for the European countries (Genovese, Bettio, 2004; Lazar, Genovese, 2004; Micale, Genovese, 2004; Royer, Genovese, 2004). The decisions n°1445/2000/EC and 2066/2003/EC on the application of area frame survey and remote sensing techniques to the agricultural statistics for 1999 to 2007 of the European Parliament moved the MCYFS into the operational phase. The main customers of the system are DG-AGRI and EUROSTAT. Part of the operational service to run the MCYFS is outsourced through the MARSOP project (MARS-OPerational), started in the middle of 2000. The AGRIFISH unit of JRC supervises this project and concentrates on yield forecast analysis and synthesis of all information in bulletins.

In the context of the Global Monitoring for Environment and Security initiative (GMES), JRC formed in 2001 the MARS-FOOD group in the MARS Unit of IPSC-JRC in Ispra (Italy). This action aims at supporting the European Union Food Security and Food Aid Policy through an improved assessment of the crop status in regions/countries stricken by food shortage problems. Accurate and timely information on crop status is needed to properly inform and direct European Food Aid, in order to prevent food shortages and consequent human suffering, and to avoid possible market disruptions due to un-necessary food aid distribution. DG AIDCO and RELEX are the main European Commission customers for this work.

The FOOD action is focused on the development of methods, tools and systems for crop monitoring and yield forecasting in selected parts of the world. It is based on state-of-the-art input data issued from remote sensing sources and global meteorological models. Agro- meteorological and statistical modelling are core activities of the action, building on the existing knowledge and methods developed by the MARS project for Europe and by partner organizations for other parts of the world. The work is carried out in close collaboration with the Food and Agriculture Organization of the United Nations (FAO) and European and international partners.

The geographical dimension of the Action is primarily developing countries where the population is affected by or vulnerable to food shortage problems. Four pilot areas are covered: Russia and Central Asia, the Mediterranean Basin, Eastern Africa and South America. Global coverage is envisaged in a later phase. In addition to the developing world, which represents the

3 primary scope of the Action, major grain producers such as Russia and Argentina are also covered.

MARS-FOOD decided to have its agro-meteorological monitoring in Russia, Central Asia and the Mediterranean Basin based on an adapted version of the European CGMS, given the compatible (even if not similar) agro-phenological conditions. Substantial adaptation was nevertheless necessary to account for specific conditions, and components were added, which are also of use for European monitoring. The result is version 8.0 of CGMS which is now the common CGMS version for MARS-FOOD and MARS-STAT activities (Savin et al., 2004).

The starting conditions are significant for the CGMS. Starting date and soil moisture content greatly affect the final result. A set of expert rules have been defined which estimate climatically optimal sowing date for different crops, and initial soil water content. This is called the Climatically Optimal Planting (COP) Determinator. The underlying report describes the first version of the COP determinator, and present results of its validation based on field observed crop sowing dates in Russia. We hope that this study will lead to the improvement of the COP determinator, and consequently – to improvement of the CGMS simulation of crop growth in general.

Region of interest

The rules for the definition of climatically optimal sowing dates are regional. They were elaborated based on agronomical practice in the region of former Soviet Union, Central Asian, and non European Mediterranean regions. The map of the region where we planned to use the COP is shown in figure 1.

Fig.1. Sub-national administrative mapping units of the region of interest

This region includes near 30 countries, among which are big agricultural producers as Russia, Ukraine, Kazakhstan, and Egypt. Main part of croplands is concentrated mainly in these countries (fig.2).

Fig.2. Cropland patterns of the region of interest

Many crops are under cultivation in the countries of the region of interest. The crop distribution and significance for the countries of the region is summarized in the table 1. Practically each country of the region has its own specific of crop cultivation. However, one can distinguish two main cultivations: one specific for countries of the former Soviet Union republics, and one specific for the Mediterranean and Middle East countries.

5 crop

country wheat barley maize millet soya sorghum rice sunflower sugar beet sugar cane rape seed potato Afghanistan - - - - - Algeria 48 2 5 1 8 9 2 2 Armenia 48 1 1 2 2 9 5 Azerbaijan 55 3 1 7 3 Belarus 19 1 4 7 2 9 Egypt 31 16 1 1 12 2 4 5 1 Georgia 39 1 13 1 2 9 3 Iran 44 4 10 2 5 4 3 Iraq 48 3 4 16 1 2 3 Israel 22 1 6 2 1 11 1 2 Jordan 40 2 2 6 2 11 1 Kazakhstan 48 1 2 4 7 3 Kuwait 19 3 2 13 11 1 Kyrgyzstan 53 1 1 7 7 Lebanon 27 1 4 3 1 5 4 3 Libya 35 3 10 1 5 1 9 2 Moldova 20 4 22 1 3 8 3 Morocco 41 8 8 5 1 1 5 5 2 Nepal 15 16 4 1 38 3 2 3 Pakistan 40 2 1 6 11 1 1 Palestine 40 2 2 6 2 11 1 Russia 31 1 1 6 12 7 Saudi Arabia 27 2 3 13 9 1 Syria 40 1 2 3 1 6 5 1 Tajikistan 58 1 2 2 1 9 5 Tunisia 47 1 8 8 1 2 Turkmenistan 56 2 1 1 6 Ukraine 31 1 2 1 6 13 8 Uzbekistan 48 1 2 3 6 2

Table 1. Crops distribution and its significance in food consumed by the population (based on FAOSTAT database). (green cells – crop is cultivated; yellow cells – crop is cultivated, but areas are small; white cells – crop is not cultivated? figures inside cells – percent of the crop in the food consumed by the population (calculated based on data about calories per capita per day for 2000) (cells without figures – percent less than 1), black figures – production is higher than import, red figures – import is higher, than domestic production)

The crops are cultivated on different soil and relief conditions (fig.3).

Fig.3. Absolute elevation of the region of interest

From all soil parameters the soil moisture content is most important for crop growth monitoring and yield forecasting in many countries of the region. This parameter, that has a strong spatial variation, is very significant for the CGMS crop growth simulations. One example of the soil moisture variability at the moment of crop sowing is presented in figure 4.

Fig 4. Soil water content at the start of crop growth simulation for main crop production region of Russia and for Ukraine (size of the circles reflect relative amount of water in ploughed soil layer, and figures near the circles show percentage of soil moisture content long-term deviation)

7 Algorithm

The aim of the COP determinator is to estimate sowing dates of the crop based on meteorological data available in the CGMS. Thus, all tables mentioned in the text below are tables of the CGMS database. In addition special rules for initial soil moisture were elaborated. The rules are differentiated for crop groups (see figure 5). Local agronomical practice knowledge is used as a basis for the rules elaboration ((Agro-meteorological…, 1958, 1961, 1966; Agro- climatic…, 1968, 1971, 1972; Reference book…, 1986; Narciso et al., 1992)

In many countries of the world optimal conditions for crop sowing depend on soil moisture conditions, and consequently by precipitation. Existing approaches, defining climatically optimal sowing dates (for example FAO approaches for Sahel zone of Africa), consider mainly amount of precipitation, and its periodicity. In the northern regions of the world the temperature plays in many cases a significant role too. In some cases crop sowing does not depend on weather conditions at all. Therefore it has been decided to split all crops of the region into a number of groups, and elaborate expert rules for determination of the sowing date separately for each group. First of all crops were subdivided into two main groups: with sowing depending on precipitation and air temperature, and with meteorologically independent crop sowing. Than each of these groups were subdivided into a number of sub-groups (fig.5). Explanation of the principles of such subdivision is described below. The expert rules were elaborated for winter crops, spring sown crops, and autumn sown crops. For the “in chain” group and “other crops” no rules were elaborated.

Fig.5. Crop groups

8 Rules for winter crops

The following rules cover winter crops (wheat, barley and rapeseed). The purpose is to define a realistic estimate of the sowing date. The sowing date is determined by three controlling factors: 1. The average daily temperature should be below the threshold temperature 2. The subsoil should not be too wet 3. The subsoil should contain sufficient water The user defined parameters of each rule, presented in table CROP_REGIMES in Annex A, are here given in small, italic characters.

The first step consists of determining the sowing date window based on meteo data for the last 15 years. For each year and each grid cell the sum of positive air temperatures, above 3 º Celsius (Tbase), is calculated starting from December 31 (window md end) backwards. If at September 1 (window md start) the sum is less than 500 the sowing date is set on September 1. In other cases the sowing date is equal to the day that the sum of 500 º Celsius (Tsum optimal) is reached. The latest sowing day (LSD) is the latest sowing date over 15 years (years window) and the earliest sowing day (ESD) is one month before the LSD. The results are stored in table COPD_WINDOWS.

The second step consists of the estimation of the sowing date for a concrete year for each grid cell: 1. Check the weather on the ESD

2. Calculate the average daily temperature over the past 10 days (Tav10d) (duration mean temperature). If Tav10d > 17 ºC (Tmean max): go to the next day and apply step 1. If Tav10d <= 17 ºC: check the following rule (workability)

3. Check days with precipitation before current day. If within previous 3 days (duration dry surface) there was one day with more than 3 mm precipitation (R dry surface) – go to the next day, and apply step 1. If each of the three preceding days had less than 3 mm, then check the following rule (sufficient water)

4. Check the precipitation sum of the last 20 days (duration wet subsoil). If the sum was more than 10 mm (R wet subsoil), then sowing takes place. Otherwise go to the next day and apply step 1 5. Move in such way up to the LSD. If no sowing has been found then sowing takes place on LSD. The year specific sowing date is written in the CGMS table CROP_CALENDAR (columns START_MONTHDAY1 and START_MONTH1).

The third step includes the estimation of initial moisture conditions. The initial moisture is linked to the day of sowing (column GIVEN_STARTDATE_WATBAL in table INITIAL_SOIL_ WATER). The initial plant available water (water above wilting point) is equal to the amount of water between field capacity and wilting point taken over the whole soil profile (the column WAV in table INITIAL_SOIL_WATER). Finally, the parameter InitWaterBalance (CGMS ini file or to be entered through the user interface) is set to “2” which means “date available in table INITIAL_SOIL_WATER”.

Rules for autumn sown crops

The following rules cover autumn sown summer crops (wheat, barley, rapeseed and potato-fall) with continued growth in winter in regions without frost (in Northern Africa). These crops have in common that they require cool conditions during sowing (below 17 º Celsius). The sowing date is determined by two controlling factors: 1. The average daily temperature should be below the threshold temperature 2. The subsoil should contain sufficient water. The user defined parameters of each rule, presented in table CROP_REGIMES in Annex A, are here given in small, italic characters.

First, for each climatic grid cell a sowing window is determined. A first preliminary window is defined as follows. The earliest sowing day (ESD_FAO) is two months (months around MSD_FAO) before the long term average sowing date. The long term average sowing date is the national long term average date given by the FAO. This is called MSD_FAO which is given in table CROP_GRID_REGIMES. In any case the latest sowing date will be at most two months after the MSD_FAO. Next, this window is improved by taking into account the weather of the last 15 years (years window). The purpose is to define a realistic estimate of the earliest and latest date of sowing. For each climatic grid cell the sowing date is estimated for each of the last 15 years based on the following rules: 1. Check the weather on the earliest sowing day (ESD_FAO)

2. Calculate the average daily temperature over the past 10 days (Tav10d) (duration mean Temp). Check if Tav10d is equal or below 17 º Celsius (Tmean max). If not go to the next day and apply step 1; if yes check the following criteria (sufficient water).

3. If the sum of the difference between rainfall and ETA over the previous 10 days (duration wet subsoil) >0 mm (R wet subsoil), then sowing takes place, if <=0, then go to the next day and apply step 1 Next, determine for each climatic grid cell the sowing period which is marked by the earliest and latest sowing date found over the past 15 years. These days are defined as Earliest Sowing Day (ESD) and Latest Sowing Day (LSD). The ESD and LSD will always be within the range of ESD_FAO and LSD_FAO. Actually ESD and LSD are a refinement. The results are stored in table COPD_WINDOWS.

After the sowing window is determined, the sowing date for the operational year is determined. The rule is similar to the rule used for determining the window. 1. Check the weather on the ESD 2. Calculate the average daily temperature over the past 10 days (Tav10d). Check if Tav10d is equal or below Tav10d threshold. If not go to the next day and apply step 1; if yes check the following criteria (sufficient water) 3. If the sum of the difference between rainfall and ETA of the previous 10 days >0, then sowing takes place, if <=0, then go to the next day and apply step 1

10 4. If no sowing date is found before LSD the LSD is taken The year specific sowing date is written in the CGMS table CROP_CALENDAR (columns START_MONTHDAY1 and START_MONTH1).

The third step includes the estimation of initial moisture conditions. The date to start the soil water balance is 60 days before sowing (column GIVEN_STARTDATE_WATBAL in table INITIAL_SOIL_WATER). However if this would lead to a date before the first campaign month the start of soil water balance is set on the first day of the first campaign month. The initial plant available water (water above wilting point) is set to zero (column WAV in table INITIAL_SOIL_WATER). Finally, the parameter InitWaterBalance (CGMS ini file or to be entered through the user interface) is set to “2” which means “date available in table INITIAL_SOIL_WATER”.

For calculation of the ETA (=Kc*ET0), the following Kc (Kc) and Threshold (Tmean max) values are used: Kc Threshold Tav10d wheat 0.7 17 barley 0.7 17 rapeseed 0.7 17 potato-fall 0.7 17

The Kc applies to the initial stage of each crop because the rules analyze the favourable conditions for initial stage of crop growth.

11 Rules for spring sown crops

The following rules cover other summer crops (spring sown wheat, spring sown barley, spring sown rapeseed, maize, millet, sorghum, sugar beet, sugar cane, soy bean, sunflower, potato- winter, potato-spring, and potato-summer and rice).

The sowing date is determined by two controlling factors: 1. The average daily temperature should be above the threshold temperature 2. The topsoil should not be too wet for seedbed preparation. Note that the factor “sub soil should contain sufficient water” is not included as it is assumed that the crops are irrigated.

The user defined parameters of each rule, presented in table CROP_REGIMES in Annex A, are here given in small, italic characters.

First, for each climatic grid cell a sowing window is determined. A first preliminary window is defined as follows. The earliest sowing day (ESD_FAO) is two months (months around MSD-FAO) before the long term average sowing date. The long term average sowing date is the national long term average date given by the FAO. The latter is called MSD_FAO which is given in table CROP_GRID_REGIMES. In any case the latest sowing date will be at most two months after the MSD_FAO. Next, this window is improved by taking into account the weather of the last 15 years (years window). The purpose is to define a realistic estimate of the earliest and latest date of sowing. For each climatic grid cell the sowing date is estimated for each of the 15 last years based on the following rules: 1. Check the weather on the earliest sowing day (ESD_FAO)

2. Calculate the average daily temperature over the past 10 days (Tav10d) (duration mean temp). Check if Tav10d is equal or higher Tav10d threshold (Tmean min). If not go to the next day and apply step 1; if yes check the following criteria (workability) (NB for Rice Tav10d is the only rule!)

3. Check if there was more than 3 mm (R dry surface) of daily rainfall within previous 3 days (duration dry surface). If yes go to the next day and apply step 1; if no sowing takes place. Next, determine for each climatic grid cell the sowing period which is marked by the earliest and latest sowing date found over the past 15 years. These days are defined as Earliest Sowing Day (ESD) and Latest Sowing Day (LSD). The ESD and LSD will always be within the range of ESD_FAO and LSD_FAO. Actually ESD and LSD are a refinement. The results are stored in table COPD_WINDOWS.

When the sowing window is determined, the sowing date for the operational year can be determined. The rule is similar to the rule used for determining the window. 1. Check the weather on the earliest sowing day (ESD) 2. Calculate the average daily temperature over the past 10 days (Tav10d). Check if Tav10d is equal or higher Tav10d threshold. If not go to the next day and apply step 1; if yes check the following rule (workability) (NB for Rice Tav10d is the only rule!)

12 3. Check if there was more than 3 mm of daily rainfall within previous 3 days. If yes go to the next day and apply step 1; if no sowing takes place. 4. If no sowing date is found before LSD the LSD is taken The year specific sowing date is written in the CGMS table CROP_CALENDAR (columns START_MONTHDAY1 and START_MONTH1).

Finally, the initial soil moisture conditions are determined. Different regions are defined with different rules for initialization. The definition of regions is as follows: 1. Check weather conditions (presence of snow cover and climatic water balance) during the last 6 months (duration check weather) before ESD 2. If during this period the sum of the climatic water balance (rain minus ETA) of 40 consecutive days (duration dry season) <= 0 mm (minimum effective rainfall) it is REGION WITH DRY SEASON. 3. Else if the grid cell does not belong to the region with dry season, check if during this period there were more than 30 consecutive days (duration snow cover) with snow cover (>= 1cm) (minimum snow cover). If so, it is a REGION WITH SNOW COVER. Next determine the date of snow cover thawing. The date of thawing is the date that over the previous ten consecutive days (duration no snow) the snow depth is less than 1 cm (maximum no snow). 4. If there is no snow cover and no dry season, it is OTHER REGION.

The initial soil moisture is determined as follows: ‚ REGION WITH DRY SEASON: The date to start the soil water balance is 60 days (bias water balance) before sowing. However if this would lead to a date before the first campaign month the start of soil water balance will be set on the first day of the first campaign month. The initial plant available water (water above wilting point) is set to zero ‚ REGION WITH SNOW COVER: The date to start the soil water balance is the date of snow cover thawing. However if this would lead to a date before the first campaign month the start of soil water balance will be set on the first day of the first campaign month. The initial plant available water (water above wilting point) is the amount of water between wilting point and saturation. ‚ OTHER REGIONS: The date to start the soil water balance is the date of sowing. The initial plant available water (water above wilting point) is the amount of water between wilting point and field capacity.

The date to start the water balance is given in the column GIVEN_STARTDATE_WATBAL in table INITIAL_SOIL_WATER. The initial plant available water (water above wilting point) is entered in column WAV in table INITIAL_SOIL_WATER. Finally, the parameter InitWaterBalance (CGMS ini file or to be entered through the user interface) is set to “2” which means “date available in table INITIAL_SOIL_WATER”.

For calculation of the ETA (=Kc*ET0), the following Kc (Kc) and Tav10d threshold values (Tmean min) are used: Kc Threshold Tav10d Spring wheat 0.3 10 Spring barley 0.3 10

13 Spring rapeseed 0.35 12 Maize 0.3 14 Millet 0.3 15 Sorghum 0.3 15 Rice - 16 Sugar beet 0.35 12 Sugar cane 0.4 15 Soya 0.4 15 Sunflower 0.35 10 Potato winter 0.5 11 Potato spring 0.5 11 Potato summer 0.5 11

The Kc applies to the initial stage of each crop because the rules analyze the favorable conditions for initial stage of crop growth.

The rules of the three groups given above have been programmed in an ORACLE package called copdate.

“In-chain-sown” and other crops

In some cases crop sowing depends only on physical availability of lands. The following situations within the region of investigations were distinguished: ‚ In Egypt: rice and maize are planted after wheat harvesting at the same fields.

‚ In Saudi Arabia: sorghum and potato are planted after harvesting of wheat harvesting.

‚ In Algeria: rapeseed and potato (one of the seasons) are planted after wheat harvesting.

‚ In Syria: maize, sunflower, sugar beet, and potato are planted after wheat harvesting.

‚ In Nepal: rice is planted after wheat harvesting.

‚ In Afghanistan: rice, maize, and potato are planted mainly after wheat harvesting.

‚ In Iran: rice, maize, sugar beet, potato are planted after wheat harvesting. From the CROP_YIELD_x table the maturity dekad is taken. The sowing dekad of the "in-chain- sown" crops is the maturity dekad plus 1. From this dekad the year specific sowing date is derived and written into the table CROP_CALENDAR (columns START_MONTHDAY1 and START_MONTH1).

Besides the relative soil moisture of the maturity dekad, given in table CROP_YIELD_x, is multiplied with the amount of water that is available between field capacity and wilting point considering the whole rooting zone. The resulting initial plant available water (water above wilting point), is entered in column WAV in table INITIAL_SOIL_WATER. The initial moisture is linked to the day of sowing (column GIVEN_STARTDATE_WATBAL in table INITIAL_ SOIL_WATER. Finally, the parameter InitWaterBalance (CGMS ini file or to be entered through the user interface) is set to “2” which means “date available in table INITIAL_SOIL_WATER”.

The rules for “in-chain-sown” crops have been implemented in PL-SQL command files. For each combination of country and crop one file exists.

For other crops (out of all mentioned above groups) the long term average sowing date value is recommended to use.

Validation

Validation of the elaborated expert rules was done based on field experimental data and meteorological data received from ECMWF model (ECMWF web site, 2007). The original meteorological daily data were pre-processed by MC Wetter: daily values were aggregated from original 3-hourly data, and all parameters were interpolated from original grid cells to 1 degree grid.

The field experimental sowing dates were available only for Russia. Four crops have been selected to test functionality of main crop groups:

- winter wheat for winter crop group; - spring wheat, grain maize and potato for spring crop group;

All field data were received from agro-meteorological network of stations of Russia. Stations with sowing dates are more or less evenly distributed throughout each crop production zones of Russia (fig.6). The information was received for 4-8 years for each crop, and the set of stations varies from year to year (see the next chapters). In general the database contains 494 field observation results.

Fig.6. Network of agro-meteorological stations in Russia from which we have received field observed information about crop sowing dates (green colour indicates main croplands)

The outputs of numerical ECMWF model were used as a source of meteorological information for the sowing date simulation. We used daily data given for a one degree grid. Thus, the meteorological conditions within 1 degree grid cell were considered as constant.

16 The difference between the field data and the results of the sowing date simulation were analyzed based on a simple statistical approach. The results of the comparison for each crop are presented in the following chapters.

17 Winter wheat

The database of field observed sowing dates of winter wheat contains 239 records. The field observations points cover the main winter wheat production zone of Russia. The sowing dates were observed during 8 years, but the exact number of years between locations differs (fig.7). The number of records per year varies from 6 to 56. All records were preliminary checked. However we cannot exclude remaining errors in these data, which is impossible to find based on available information. All field observed sowing dates of winter wheat, used during the analysis, can be found in Annex B.

Fig.7. Stations and years for which winter wheat sowing dates were recorded

The detection of winter wheat sowing was done based on expert rules described in one of the previous chapters, and using ECMWF meteorological daily data given at 1 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 2, and Figures 8-9.

Analysis of data over all available years shows that field observed results are on average 12 days later than the estimated dates. However, the variance is very large (range is 90 days, and standard deviation is near 15). Descriptive statistics for separate years demonstrate that the mean deviation of estimated dates from observed field dates varies from 6 days for years 2000 and 2001 to 15 days for 1995. For all years the mean deviation (difference between observed and estimated dates) is positive. The variance show similar behaviour as the analysis of all years together. The range varies from year to year from 27 to 88 days, and standard deviation varies from 8 to 20.

Analysis of available field observed sowing dates within one pixel of ECMWF meteorological grid shows that the sowing dates variability is on average 11 days with maximum in 30 days. One of the reasons of such large variance is the management like availability of combines,

18 tractors, fuel and the delay in summer crops harvesting and physical availability of the fields for winter crop sowing. These management factors also explain why observed sowing dates are later than estimated dates: according the climate sowing can take place but because of management reasons sowing is done later. The results confirm the climate rules are basically correct.

The largest deviation between field observed and estimated data is allocated for the points near the mountains. This indicates that the quality of meteorological data is not enough for estimation of sowing dates in these regions. The deviation for other points differs from year to year without clear tendency. Large deviation in non mountainous zone can be explained by the reasons mentioned above, as well as by the errors in the field observed data, or by low quality of the meteorological data.

The cases where the estimated sowing date is later than the observed one are randomly distributed in time and in space. It is likely that the main reason of appearance of such results is mistakes in the field crop sowing or in meteorological data. The ECMWF data are representing the average climate conditions for a 1 by 1 degree grid cell while local sowing events are also driven by local climate conditions. For example it could be that farmers grow their crops in the valley which has more favourable climate conditions compared to the upland or table lands. This could explain why the rule estimates a later sowing date than what happens in reality.

Table 2. Descriptive statistics of the difference between observed and estimated sowing dates of winter wheat. all years 1988 1993 1995 1997 1998 1999 2000 2001 Mean 11.62656 12.74138 10.05 15.35 14.13793 11.43243 13.44444 5.8 5.833333 St. Er. 0.952543 1.699002 1.737323 2.487098 1.801562 2.158001 3.286684 3.404841 6.063094 Median 11 9 9 15.5 14 13 12 3 3.5 Mode 8 8 9 12 9 15 29 10 - St. Dev. 14.78744 12.93921 7.769542 11.12264 9.701709 13.12661 19.7201 20.14331 14.85149 Sample Var. 218.6683 167.4232 60.36579 123.7132 94.12315 172.3078 388.8825 405.7529 220.5667 Kurtosis 0.748171 -0.71408 0.137782 0.081076 -0.47066 -0.62544 0.135532 1.544928 -1.20216 Skewness 0.218661 -0.11748 0.668436 -0.32036 -0.45321 -0.36768 0.175706 1.132594 0.526822 Range 90 55 27 43 37 47 88 86 38 Minimum -27 -18 -1 -7 -8 -13 -27 -23 -10 Maximum 63 37 26 36 29 34 61 63 28 Count 239 56 20 20 29 37 36 35 6

Histogram (all years)

40 35 30 25 20 15 Frequency 10 5 0 5 -5 15 25 35 -25 -15 More days

Fig.8. Histogram of the deviation between observed and estimated winter wheat sowing dates for all available years

Histogram (1988) Histogram (1993)

14 8

12 7

10 6 5 8 4 6 3 Frequency 4 Frequency 2 2 1 0 0 5 5 -5 -5 15 25 35 15 25 35 -25 -15 -25 -15 More More days days

Histogram (1995) Histogram (1997)

6 8 5 7 6 4 5 3 4 2 3 Frequency Frequency 2 1 1 0 0 5 5

-5 -5 15 25 35 15 25 35 -25 -15 -25 -15 More More days days

Histogram (1998) Histogram (1999)

14 5 12 4 10 8 3

6 2 Frequency 4 Frequency 1 2 0 0 5 5 -5 -5 15 25 35 15 25 35 -25 -15 -25 -15 More More days days

Fig.9. Histogram of the deviation between real and simulated winter wheat sowing dates for separate years

Fig.9. continuation

Histogram (2000) Histogram (2001)

7 2 6 5 4 1 3 Frequency 2 Frequency 1 0 0 5 5 -5 -5 15 25 35 15 25 35 -25 -15 -25 -15 More More days days

21 Spring wheat

The database of observed field dates of spring wheat sowing contains 96 records. The field observations points cover the main spring wheat production zone of Russia. The sowing dates were observed during 6 years, but the exact number of years between locations differs (fig.10). The number of records per year varies from 10 to 20. All records were preliminary checked. However we cannot exclude remaining errors in these data, which is impossible to find based on available information. All field observed sowing dates of spring wheat, used during the analysis, can be found in Annex C.

Fig.10. Stations and years for which spring wheat sowing dates were recorded

The detection of spring wheat sowing was done based on expert rules described in one of the previous chapters, and using ECMWF meteorological daily given at 1 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 3, and Figures 11-12.

Analysis of data over all available years shows that field observed results are on average 7 days later than the estimated dates. However, the variance is very large (range is 36 days, and the standard deviation is near 7). Descriptive statistics for separate years demonstrate that the mean deviation of estimated dates from observed field dates varies from 2 day for years 1997 to 11 days for 2000. For all years the mean deviation (difference between observed and estimated dates) is positive. The variance show similar behaviour as the analysis of all years together.

According Agro-meteorological regional reference books the real sowing as a rule takes place with delay in 1-2 weeks from the climatically optimal dates (Agro-meteorological…, 1958, 1961, 1966; Agro-climatic…, 1968, 1971, 1972; Reference book…, 1986). The level of real sowing dates variation within one region with homogenous weather conditions can be explained by many reasons, from which the most frequent are technical ones: availability of machinery, tractors, and fuel, or explained by soil variability. As results, the sowing date of spring wheat at

22 neighbour parcels in the same year can theoretically vary from number of days up to 15-20 days. Analysis of available field observed sowing dates shows that the sowing dates variability within one pixel of ECMWF meteorological grid is in average 7 days with maximum in 11 days. The deviation between observed and estimated dates is close to these values or higher. It is necessary to note that the highest positive deviation (real sowing is later than simulated) is found for the most northern regions. One explanation of this phenomenon can be based on the difference in wheat varieties, and the expert rules must be variety specific. The relatively large delay in sowing in northern regions can also be explained by the fact that the rules do not take soil temperature into consideration. When the air temperature becomes optimal for crop sowing the soils (especially in northern or Siberian regions) can remain cold for sowing. As a result the real sowing in these regions is regulated more by soil temperature than by air temperature.

The highest negative deviation has not a well defined spatial distribution but be aware it only relates to three observations! So this does not give an indication what could be wrong. It is likely that the main reason of appearance of such results is mistakes in the field crop sowing or in meteorological data. As in the previous case it is necessary to note that the ECMWF data are representing the average climate conditions for a 1 by 1 degree grid cell while local sowing events are also driven by local climate conditions. This could explain why the rule estimates a later sowing date than what happens in reality

Table 3. Descriptive statistics of the difference between observed and estimated sowing dates of spring wheat. all years 1985 1997 1998 1999 2000 2001 Mean 7.125 7 2.5 5.411765 7.05 11 7.636364 Standard Error 0.761066 1.30227 3.208496 1.564916 1.976806 1.622214 1.252436 Median 7 7 2 5 8 8 8 Mode 6 3 -7 6 6 6 8 Standard Deviation 7.456894 5.676462 10.14615 6.452314 8.840547 7.071068 4.153859 Sample Variance 55.60526 32.22222 102.9444 41.63235 78.15526 50 17.25455 Kurtosis 0.360023 -0.6426 -0.84859 0.163849 0.752526 -1.11396 0.574835 Skewness -0.21066 0.454208 0.303178 0.453798 -0.8836 0.603786 -0.66717 Range 36 20 31 24 33 21 14 Minimum -13 -2 -12 -5 -13 2 -1 Maximum 23 18 19 19 20 23 13 Count 96 19 10 17 20 19 11

Histogram (all years)

18 16 14 12 10 8 Frequency 6 4 2 0 1 5 9 -7 -3 13 17 21 -11 days

Fig.11. Histogram of the deviation between observed and estimated spring wheat sowing dates for all available years

Histogram (1985) Histogram (1997)

7 3 6 5 2 4

Frequency Frequency 1 2

1 0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 13 17 21 -11 -11 days days

Histogram (1998) Histogram (1999) 5 5

4 4

3 3

2 2 Frequency Frequency

1 1

0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 13 17 21

-11 -11 days days

Histogram (2000) Histogram (2001)

6 5 5 4 4 3 3 2 Frequency Frequency 2 1 1

0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 13 17 21 -11 -11 days days

Fig.12. Histogram of the deviation between observed and estimated spring wheat sowing dates for separate years

Potato

The database of field observed sowing dates of potato contains 133 records. The field observations points cover the main potato production zone of Russia. The sowing dates were observed during 7 years, but the exact number of years between locations differs (fig.13). The number of records per year varies from 9 to 35. All records were preliminary checked. However we cannot exclude remaining errors in these data, which is impossible to find based on available information. All field observed sowing dates of potato used during the analysis, can be found in Annex D.

Fig.13. Stations and years for which potato sowing dates were recorded

The detection of potato sowing was done based on expert rules described in one of the previous chapters, and using ECMWF meteorological daily data given at 1 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 4, and Figure 14.

Analysis of data over all available years shows that field observed dates are on average 16 days later than the estimated dates. However, the variance is very large (range is 63 days with the standard deviation near 12). Descriptive statistics for separate years demonstrate that the mean deviation between observed field dates and estimated dates varies from 1 day for year 1988 to 22 days for 2000. For all years the mean deviation (difference between observed and estimated dates) is positive. The variance for separate years in general is high. The range varies from 14 days with standard deviation near 6 for 1985 to 54 days with standard deviation near 13 for 2000.

25 Agronomical practice demonstrates that real sowing dates of potato in many regions of Russia in best case follow the climatically optimal days in one-two weeks (Agro-meteorological…, 1958, 1961, 1966; Agro-climatic…, 1968, 1971, 1972). But in many cases the delay can be longer due to availability of combines, tractors, and fuel. The potato in many regions is not a crop of first priority, and collective farms start to sow potato only after finishing sowing of more significant crops. Due to such circumstances the real sowing date in many cases has very large delay compared to climatically optimal dates.

The variability of observed sowing dates within one pixel of ECMWF meteorological grid is on average 9 days with a maximum of 16 days. The differences between observed and estimated dates are higher comparing with the variability within one ECMWF grid cell. Likewise spring wheat, the highest positive deviation (observed sowing is later than estimated) for potato is found for the most northern and Siberian regions, and the highest negative – for the most southern regions. This could be explained because of differences in wheat varieties between northern and southern region. The relatively large delay in sowing in northern regions can be explained by the fact, that the rules do not take soil temperature into consideration. When the air temperature becomes optimal for crop sowing the soils (especially in northern or Siberian regions) can remain cold for sowing. As a result the real sowing in these regions are regulated more by soil temperature than by air temperature. This can lead to shifting of sowing dates of the crops which are being sown before potato, and as a result lead to the same or bigger shift of the potato sowing dates.

Note that the highest negative differences in the south are only based on three observations! It is likely that the main reason of appearance of such results is mistakes in the field crop sowing or in meteorological data. Note that the ECMWF data are representing the average climate conditions for a 1 by 1 degree grid cell while local sowing events are also driven by local climate conditions. For example it could be that farmers grow their crops in the valley which has more favourable climate conditions compared to the upland or table lands. This could explain why the rule estimates a later sowing date than what happens in reality.

Table 4. Descriptive statistics of the difference between observed and estimated sowing dates of potato.

all years 1985 1988 1997 1998 1999 2000 2001 Mean 15.82707 17.875 1.111111 9.454545 10.61538 17.20588 22.11429 21.3 Stand. Error 1.059169 2.021646 3.075189 2.640123 1.333678 1.976592 2.276947 3.980648 Median 16 17.5 2 10 9.5 19 25 24 Mode 19 24 2 13 6 19 28 - Stand. Dev. 12.21494 5.718079 9.225568 8.756296 6.800452 11.52541 13.4706 12.58791 Sample Var. 149.2047 32.69643 85.11111 76.67273 46.24615 132.8351 181.4571 158.4556 Kurtosis -0.28027 -1.85359 -0.16086 1.058705 -0.50803 0.170956 -0.58858 -0.91214 Skewness 0.053307 -0.06638 -1.09626 0.486514 0.61336 -0.64569 -0.2866 -0.53789 Range 63 14 24 32 25 46 54 36 Minimum -15 10 -15 -4 1 -11 -6 1 Maximum 48 24 9 28 26 35 48 37 Count 133 8 9 11 26 34 35 10

Histogram (all years) Histogram (1985)

14 4

12 3 10 8 2 6

Frequency Frequency 4 1

0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 25 29 33 37 13 17 21 25 29 33 37 -11 -11 More More days days

Histogram (1988) Histogram (1997)

5 4

4 3 3 2 2 Frequency Frequency 1 1

0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 25 29 33 37 13 17 21 25 29 33 37 -11 -11 More More days days

Histogram (1998) Histogram (1999)

8 6 7 5 6 4 5 4 3 3 Frequency Frequency 2 2 1 1 0 0 1 5 9 1 5 9 -7 -3 -7 -3

13 17 21 25 29 33 37 13 17 21 25 29 33 37 -11 -11 More More days days

Fig.14. Histogram of the deviation between observed and estimated potato sowing dates

27 Fig.14. continuation

Histogram (2000) Histogram (2001)

7 2 6 5 4 1 3

Frequency Frequency 2

1 0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 17 21 25 29 33 37 13 17 21 25 29 33 37 -11 -11 More More days days

Grain maize

The database of field observed sowing dates of maize contains only 23 records. The field observations points are located in the main grain maize production zone of Russia. The sowing dates were observed during 5 years, but the exact number of years between locations differs (fig.15). The number of records per year is very different: only one record was obtained for 1997, two records for 1998, 9 records for 1999, 5 records for 2000 and 6 records for 1985. All records were preliminary checked. However we cannot exclude remaining errors in these data, which is impossible to find based on available information. All field observed data about grain maize sowing used during the analysis, can be found in Annex E.

Fig.15. Stations and years for which grain maize sowing dates were recorded

The detection of maize sowing date was done based on expert rules described in one of the previous chapters, and using ECMWF meteorological daily data given at 1 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 5, and Figure 16.

Analysis of data over all available years shows that the difference between observed and estimated dates is on average only one day. However, the variance is large (range is 53 days with standard deviation near 12). Descriptive statistics for separate years demonstrate that the mean deviation of estimated dates from observed field dates varies from -10 days for 1985 to 5 days for 1999. The mean deviation (difference between observed and estimated dates) is negative only for 1985. The largest negative deviation is -27 days; the maximal positive deviation is 26 days, and it was observed for 1999. The variance shows similar behaviour as the analysis of all years together. The range is on average near 30-40 days.

29 Grain maize is one of the most difficult crops in the region for estimation of sowing dates based on meteorological data. The significance of agro-technological factors for this crop is higher than for other crops. The sowing of grain maize as a rule follows the sowing practically of all other crops. It means that if the delay in sowing of other crops takes place, the sowing of maize automatically will be done with delay too in spite of the optimal weather conditions. Thus, theoretically the difference between the climatically optimal (simulated) sowing date and real sowing date can be very large with a high spatial and temporal variation. This hypothesis is not confirmed by the results. The average difference is around 1 day with a standard deviation of 12 days. Perhaps the rules for maize have to be adapted applying a higher temperature threshold. But be aware that the amount of field observed data is limited for grain maize, and for receiving more reliable results such amount of information is not sufficient.

Table 5. Descriptive statistics of the difference between observed and estimated sowing dates of grain maize. all years 1985 1999 2000 Mean 0.956522 -9.66667 5 4.2 Standard Error 2.528477 3.870113 4.666667 3.512834 Median 1 -8 7 2 Mode -10 -8 -10 Standard Deviation 12.12615 9.479803 14 7.854935 Sample Variance 147.0435 89.86667 196 61.7 Kurtosis 0.136561 3.185514 -1.79945 0.780611 Skewness 0.039561 -1.26475 0.166923 0.724647 Range 53 29 36 21 Minimum -27 -27 -10 -5 Maximum 26 2 26 16 Count 23695

Histogram (all years) Histogram (1985)

6 4

5 3 4 3 2

Frequency 2 Frequency 1 1

0 0 1 5 9 1 5 9

-7 -3 -7 -3 13 13 -15 -11 -15 -11

More More days days

Histogram (1999) Histogram (2000)

3 2

1 Frequency 1 Frequency

0 0 1 5 9 1 5 9 -7 -3 -7 -3 13 13

-15 -11 -15 -11 More More days days

Fig.16. Histogram of the deviation between observed and estimated maize sowing dates for separate years

Conclusion

The Climatically Optimal Planting date (COP) determinator has been elaborated for application in Mediterranean, Central Asian countries and former Soviet Union republics. The COP is a set of expert rules which allows determining crop sowing dates, which are optimal from point of view of the meteorological conditions. Practically the COP determinator is based on local agronomical experience and expresses it in form of expert rules. The COP has been designed for application in Crop Growth Monitoring System (CGMS), which has been elaborated for the region some years ago.

The validation of the sowing dates estimated by the COP determinator has been done by comparing estimated sowing dates with near 500 field observed sowing dates for four main crops in Russia. The ECMWF weather grid with spatial resolution of one degree has been used as a basis of COP estimation.

The results of the validation show that the field observed sowing dates in general follow the simulated dates within one or two weeks. This is in line with the crop sowing practices in the region of analysis. However, the deviation between the observed and estimated dates is sometimes large. Availability in time of tractors, combines, fuel, or land after the harvesting of previous crop in rotation, which are not included in the expert rules, are likely to be the a reason of the large deviation. Additionally, the field observed sowing dates could contain errors. Another possible reason is the quality of the meteorological data used for validation. The spatial resolution is very large. Local temporal meteorological variability is not included in the ECMWF data set and in some cases the differences in scales are too large for a reliable comparison. This is especially evident in near mountainous regions. For early spring crops it seems that the soil temperature plays important role in optimal sowing conditions in cold regions (northern and Siberian regions of Russia).

In spite of the large deviation between the observed and estimated dates we think that the COP determinator can be successfully used for the crop sowing dates simulation in the countries of the region. The usage of the simulated by COP crop sowing dates can significantly improve the CGMS modelling.

The second version of the COP determinator should take into consideration the difference between air and soil temperatures in cold regions. Besides the rules should take into account the management delay that occurs: farmers sow later than the climatically optimal dates. Additionally it seems necessary to investigate the possibilities to tune the COP rules for different crop varieties. The more detail meteorological data can also improve the quality of the COP simulations.

32 References

Agro-meteorological regional reference book for Belorussia, 1958. – Leningrad: Hydromet, 230 pp.

Agro-meteorological regional reference book for Rostov oblast, 1961. – Leningrad: Hydromet, 206 pp.

Agro-meteorological regional reference book for Ryazan oblast, 1966. – Moscow: UGSCO, 134 pp.

Agro-climatic resources of Lipetsk and Orel oblast, 1972. – Leningrad: Hydromet, 120 pp.

Agro-climatic resources of Kuibyshev oblast, 1968. – Leningrad: Hydromet, 208 pp.

Agro-climatic resources of Novosibirsk oblast, 1971. – Leningrad: Hydromet, 156 pp.

Agro-climatic resources of Kursk oblast, 1971. – Leningrad: Hydromet, 104 pp.

ECMWF web site: http://www.ecmwf.int/products/forecasts/)

Genovese G., Bettio M. (Eds), 2004. Methodology of the MCYFS Vol.4 Statistical data collection, processing and analysis EUR 21291 EN/4, Luxembourg: Office for Official Publications of the EC, ISBN 92-894-8183-8

Lazar C., Genovese G. (Eds), 2004. Methodology of the MCYFS Vol.2 Agro-Meteorological data collection, processing and analysis EUR 21291 EN/2, Luxembourg: Office for Official Publications of the EC, ISBN 92-894-8181-1

Micale F., Genovese G. (Eds), 2004. Methodology of the MCYFS Vol.1 Meteorological data collection, processing and analysis EUR 21291 EN/1, Luxembourg: Office for Official Publications of the EC, ISBN 92-894-8180-3

Narciso G., Ragni P., Venturi A., 1992. Agrometeorological aspects of crops in Italy, Spain and Grees. JRC OPOCE, 440 pp.

Reference book for agronomist on agro-meteorology, 1986. – Leningrad: Hydromet, 527 pp.

Royer A., Genovese G. (Eds), 2004. Methodology of the MCYFS Vol.3 Remote Sensing information, data processing and analysis EUR 21291 EN/3, Luxembourg: Office for Official Publications of the EC, ISBN 92-894-8182-X

Savin I., Boogaard H., van Diepen C., Negre T. (Eds), 2004. CGMS version 8.0: User manual and technical description, JRC, OPOCE. 129 pp.

Supit I., and Goot, E. van der, (Eds) http://www.iwan-supit.cistron.nl/~iwan-supit/contents/

Appendix

A. Description of tables storing data needed to estimate year specific sowing dates and initial soil moisture

This section describes the tables needed to determine the year specific sowing dates and initial soil moisture. The long term average sowing dekads, supplied by FAO, and checked by Alterra, are stored in table: CROP_GRID_REGIMES: CROP_NO NOT NULL NUMBER(2) GRID_NO NOT NULL NUMBER(8) SOWING_REGIME VARCHAR2(10) MDECADE_SOWING_FAO NUMBER(2) The column MDECADE_SOWING_FAO stores the long term average sowing dekads supplied by FAO. This is given for each crop, grid cell and for each so called sowing regime. There are three different sowing regimes: winter, autumn and spring referring to the three different types of rules. Actually the column SOWING_REGIME is not really needed because only one long term average sowing dekad of FAO exists for each unique combination of crop and grid cell.

Besides a table has been introduced to store the planting window in which the optimal sowing date is searched: COPD_WINDOWS: CROP_NO NOT NULL NUMBER(2) GRID_NO NOT NULL NUMBER(8) SOWING_REGIME NOT NULL VARCHAR2(10) ESD_MONTH_DAY NOT NULL VARCHAR2(10) ESD_REL_YEAR NOT NULL NUMBER(5) LSD_MONTH_DAY NOT NULL VARCHAR2(10) LSD_REL_YEAR NOT NULL NUMBER(5) CAMP_YEAR_1 NUMBER(4) CAMP_YEAR_2 NUMBER(4) The columns ESD_REL_YEAR and LSD_REL_YEAR indicate if the ESD or LSD is related to the first or second calendar year. This only applies for crops where the campaign year does not match the calendar year. The columns ESD_MONTH_DAY and LSD_MONTH_DAY store respectively the first and last day of the planting window. The columns CAMP_YEAR_1 and CAMP_YEAR_2 store the first and last year of the period used to define the planting window.

And finally there is a table which stores the meta-information on the rules:

CROP_REGIMES: CROP_NO NOT NULL NUMBER(2) SOWING_REGIME NOT NULL VARCHAR2(10) REGIME_DETAIL NOT NULL VARCHAR2(30) DETAIL_VALUE VARCHAR2(30) DETAIL_DIMENSION VARCHAR2(30)

Below the content is presented:

Crop Rule Short description Value Units type 1 winter R dry surface 3 mm H2O rainfall 1 winter R wet subsoil 10 mm H2O rainfall 1 winter T base 3 degree Celsius 1 winter Tmean max 17 degree Celsius 1 winter Tsum optimal 500 degree Celsius 1 winter duration dry surface 3 consecutive days 1 winter duration mean temp 10 consecutive days 1 winter duration wet subsoil 20 consecutive days 1 winter window md end 1231 month day of a year 1 winter window md start 901 month day of a year 1 winter years window 15 year 2 summer Kc 0.3 crop factor 2 summer R dry surface 3 mm H2O rainfall 2 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 2 summer Tmean min 14 degree Celsius 2 summer bias waterbalance 60 number of days before sowing date to start water balance 2 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 2 summer duration dry season 40 nr of cons. days with clim. balance below threshold 2 summer duration dry surface 3 consecutive days 2 summer duration mean temp 10 consecutive days 2 summer duration nosnow 10 nr of cons. days with snow depth below threshold 2 summer duration snowcover 30 nr of cons. days with snow depth above threshold 2 summer duration wet subsoil 20 consecutive days 2 summer maximum no snow 1 snow depth during a day below which day counts thawing 2 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 2 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 2 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 2 summer years window 15 year 3 autumn Kc 0.7 crop factor 3 summer Kc 0.3 crop factor 3 summer R dry surface 3 mm H2O rainfall 3 autumn R wet subsoil 0 mm H2O rainfall - excess above ETA 3 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 3 autumn Tmean max 17 degree Celsius 3 summer Tmean min 10 degree Celsius 3 autumn bias waterbalance 60 number of days before sowing date to start water balance 3 summer bias waterbalance 60 number of days before sowing date to start water balance 3 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 3 summer duration dry season 40 nr of cons. days with clim. balance below threshold 3 summer duration dry surface 3 consecutive days 3 summer duration mean temp 10 consecutive days 3 autumn duration mean temp 10 consecutive days

35 3 summer duration nosnow 10 nr of cons. days with snow depth below threshold 3 summer duration snowcover 30 nr of cons. days with snow depth above threshold 3 autumn duration wet subsoil 10 consecutive days 3 summer duration wet subsoil 20 consecutive days 3 summer maximum no snow 1 snow depth during a day below which day counts thawing 3 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 3 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 3 autumn months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 3 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 3 autumn years window 15 year 3 summer years window 15 year 5 summer Kc 0.3 crop factor 5 summer R dry surface 3 mm H2O rainfall 5 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 5 summer Tmean min 16 degree Celsius 5 summer bias waterbalance 60 number of days before sowing date to start water balance 5 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 5 summer duration dry season 40 nr of cons. days with clim. balance below threshold 5 summer duration dry surface 3 consecutive days 5 summer duration mean temp 10 consecutive days 5 summer duration nosnow 10 nr of cons. days with snow depth below threshold 5 summer duration snowcover 30 nr of cons. days with snow depth above threshold 5 summer duration wet subsoil 20 consecutive days 5 summer maximum no snow 1 snow depth during a day below which day counts thawing 5 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 5 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 5 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 5 summer years window 15 year 6 summer Kc 0.35 crop factor 6 summer R dry surface 3 mm H2O rainfall 6 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 6 summer Tmean min 12 degree Celsius 6 summer bias waterbalance 60 number of days before sowing date to start water balance 6 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 6 summer duration dry season 40 nr of cons. days with clim. balance below threshold 6 summer duration dry surface 3 consecutive days 6 summer duration mean temp 10 consecutive days 6 summer duration nosnow 10 nr of cons. days with snow depth below threshold 6 summer duration snowcover 30 nr of cons. days with snow depth above threshold

36 6 summer duration wet subsoil 20 consecutive days 6 summer maximum no snow 1 snow depth during a day below which day counts thawing 6 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 6 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 6 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 6 summer years window 15 year 9 summer Kc 0.4 crop factor 9 summer R dry surface 3 mm H2O rainfall 9 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 9 summer Tmean min 15 degree Celsius 9 summer bias waterbalance 60 number of days before sowing date to start water balance 9 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 9 summer duration dry season 40 nr of cons. days with clim. balance below threshold 9 summer duration dry surface 3 consecutive days 9 summer duration mean temp 10 consecutive days 9 summer duration nosnow 10 nr of cons. days with snow depth below threshold 9 summer duration snowcover 30 nr of cons. days with snow depth above threshold 9 summer duration wet subsoil 20 consecutive days 9 summer maximum no snow 1 snow depth during a day below which day counts thawing 9 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 9 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 9 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 9 summer years window 15 year 10 winter R dry surface 3 mm H2O rainfall 10 winter R wet subsoil 10 mm H2O rainfall 10 winter T base 3 degree Celsius 10 winter Tmean max 17 degree Celsius 10 winter Tsum optimal 500 degree Celsius 10 winter duration dry surface 3 consecutive days 10 winter duration mean temp 10 consecutive days 10 winter duration wet subsoil 20 consecutive days 10 winter window md end 1231 month day of a year 10 winter window md start 901 month day of a year 10 winter years window 15 year 11 summer Kc 0.35 crop factor 11 summer R dry surface 3 mm H2O rainfall 11 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 11 summer Tmean min 10 degree Celsius 11 summer bias waterbalance 60 number of days before sowing date to start water balance 11 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 11 summer duration dry season 40 nr of cons. days with clim. balance below threshold 11 summer duration dry surface 3 consecutive days 11 summer duration mean temp 10 consecutive days 11 summer duration nosnow 10 nr of cons. days with snow depth below

37 threshold 11 summer duration snowcover 30 nr of cons. days with snow depth above threshold 11 summer duration wet subsoil 20 consecutive days 11 summer maximum no snow 1 snow depth during a day below which day counts thawing 11 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 11 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 11 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 11 summer years window 15 year 13 winter R dry surface 3 mm H2O rainfall 13 winter R wet subsoil 10 mm H2O rainfall 13 winter T base 3 degree Celsius 13 winter Tmean max 17 degree Celsius 13 winter Tsum optimal 500 degree Celsius 13 winter duration dry surface 3 consecutive days 13 winter duration mean temp 10 consecutive days 13 winter duration wet subsoil 20 consecutive days 13 winter window md end 1231 month day of a year 13 winter window md start 901 month day of a year 13 winter years window 15 year 14 autumn Kc 0.7 crop factor 14 summer Kc 0.3 crop factor 14 summer R dry surface 3 mm H2O rainfall 14 autumn R wet subsoil 0 mm H2O rainfall - excess above ETA 14 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 14 autumn Tmean max 17 degree Celsius 14 summer Tmean min 10 degree Celsius 14 autumn bias waterbalance 60 number of days before sowing date to start water balance 14 summer bias waterbalance 60 number of days before sowing date to start water balance 14 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 14 summer duration dry season 40 nr of cons. days with clim. balance below threshold 14 summer duration dry surface 3 consecutive days 14 autumn duration mean temp 10 consecutive days 14 summer duration mean temp 10 consecutive days 14 summer duration nosnow 10 nr of cons. days with snow depth below threshold 14 summer duration snowcover 30 nr of cons. days with snow depth above threshold 14 autumn duration wet subsoil 10 consecutive days 14 summer duration wet subsoil 20 consecutive days 14 summer maximum no snow 1 snow depth during a day below which day counts thawing 14 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 14 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 14 autumn months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 14 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 14 autumn years window 15 year 14 summer years window 15 year

38 15 autumn Kc 0.7 crop factor 15 summer Kc 0.35 crop factor 15 summer R dry surface 3 mm H2O rainfall 15 autumn R wet subsoil 0 mm H2O rainfall - excess above ETA 15 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 15 autumn Tmean max 17 degree Celsius 15 summer Tmean min 12 degree Celsius 15 autumn bias waterbalance 60 number of days before sowing date to start water balance 15 summer bias waterbalance 60 number of days before sowing date to start water balance 15 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 15 summer duration dry season 40 nr of cons. days with clim. balance below threshold 15 summer duration dry surface 3 consecutive days 15 autumn duration mean temp 10 consecutive days 15 summer duration mean temp 10 consecutive days 15 summer duration nosnow 10 nr of cons. days with snow depth below threshold 15 summer duration snowcover 30 nr of cons. days with snow depth above threshold 15 autumn duration wet subsoil 10 consecutive days 15 summer duration wet subsoil 20 consecutive days 15 summer maximum no snow 1 snow depth during a day below which day counts thawing 15 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 15 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 15 autumn months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 15 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 15 autumn years window 15 year 15 summer years window 15 year 30 summer Kc 0.3 crop factor 30 summer R dry surface 3 mm H2O rainfall 30 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 30 summer Tmean min 14 degree Celsius 30 summer bias waterbalance 60 number of days before sowing date to start water balance 30 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 30 summer duration dry season 40 nr of cons. days with clim. balance below threshold 30 summer duration dry surface 3 consecutive days 30 summer duration mean temp 10 consecutive days 30 summer duration nosnow 10 nr of cons. days with snow depth below threshold 30 summer duration snowcover 30 nr of cons. days with snow depth above threshold 30 summer duration wet subsoil 20 consecutive days 30 summer maximum no snow 1 snow depth during a day below which day counts thawing 30 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 30 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 30 summer months around MSD_FAO 2 months around MSD_FAO to define a max.

39 window for osd 30 summer years window 15 year 32 summer Kc 0.3 crop factor 32 summer R dry surface 3 mm H2O rainfall 32 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 32 summer Tmean min 15 degree Celsius 32 summer bias waterbalance 60 number of days before sowing date to start water balance 32 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 32 summer duration dry season 40 nr of cons. days with clim. balance below threshold 32 summer duration dry surface 3 consecutive days 32 summer duration mean temp 10 consecutive days 32 summer duration nosnow 10 nr of cons. days with snow depth below threshold 32 summer duration snowcover 30 nr of cons. days with snow depth above threshold 32 summer duration wet subsoil 20 consecutive days 32 summer maximum no snow 1 snow depth during a day below which day counts thawing 32 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 32 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 32 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 32 summer years window 15 year 34 summer Kc 0.4 crop factor 34 summer R dry surface 3 mm H2O rainfall 34 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 34 summer Tmean min 15 degree Celsius 34 summer bias waterbalance 60 number of days before sowing date to start water balance 34 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 34 summer duration dry season 40 nr of cons. days with clim. balance below threshold 34 summer duration dry surface 3 consecutive days 34 summer duration mean temp 10 consecutive days 34 summer duration nosnow 10 nr of cons. days with snow depth below threshold 34 summer duration snowcover 30 nr of cons. days with snow depth above threshold 34 summer duration wet subsoil 20 consecutive days 34 summer maximum no snow 1 snow depth during a day below which day counts thawing 34 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 34 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 34 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 34 summer years window 15 year 71 summer Kc 0.5 crop factor 71 summer R dry surface 3 mm H2O rainfall 71 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 71 summer Tmean min 11 degree Celsius 71 summer bias waterbalance 60 number of days before sowing date to start water balance

40 71 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 71 summer duration dry season 40 nr of cons. days with clim. balance below threshold 71 summer duration dry surface 3 consecutive days 71 summer duration mean temp 10 consecutive days 71 summer duration nosnow 10 nr of cons. days with snow depth below threshold 71 summer duration snowcover 30 nr of cons. days with snow depth above threshold 71 summer duration wet subsoil 20 consecutive days 71 summer maximum no snow 1 snow depth during a day below which day counts thawing 71 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 71 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 71 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 71 summer years window 15 year 72 summer Kc 0.5 crop factor 72 summer R dry surface 3 mm H2O rainfall 72 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 72 summer Tmean min 11 degree Celsius 72 summer bias waterbalance 60 number of days before sowing date to start water balance 72 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 72 summer duration dry season 40 nr of cons. days with clim. balance below threshold 72 summer duration dry surface 3 consecutive days 72 summer duration mean temp 10 consecutive days 72 summer duration nosnow 10 nr of cons. days with snow depth below threshold 72 summer duration snowcover 30 nr of cons. days with snow depth above threshold 72 summer duration wet subsoil 20 consecutive days 72 summer maximum no snow 1 snow depth during a day below which day counts thawing 72 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 72 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 72 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 72 summer years window 15 year 73 summer Kc 0.5 crop factor 73 summer R dry surface 3 mm H2O rainfall 73 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 73 summer Tmean min 11 degree Celsius 73 summer bias waterbalance 60 number of days before sowing date to start water balance 73 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 73 summer duration dry season 40 nr of cons. days with clim. balance below threshold 73 summer duration dry surface 3 consecutive days 73 summer duration mean temp 10 consecutive days 73 summer duration nosnow 10 nr of cons. days with snow depth below threshold

41 73 summer duration snowcover 30 nr of cons. days with snow depth above threshold 73 summer duration wet subsoil 20 consecutive days 73 summer maximum no snow 1 snow depth during a day below which day counts thawing 73 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 73 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 73 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 73 summer years window 15 year 74 autumn Kc 0.7 crop factor 74 summer Kc 0.5 crop factor 74 summer R dry surface 3 mm H2O rainfall 74 autumn R wet subsoil 0 mm H2O rainfall - excess above ETA 74 summer R wet subsoil -1000 mm H2O rainfall - excess above ETA 74 autumn Tmean max 17 degree Celsius 74 summer Tmean min 10 degree Celsius 74 autumn bias waterbalance 60 number of days before sowing date to start water balance 74 summer bias waterbalance 60 number of days before sowing date to start water balance 74 summer duration check weather 6 nr of months before sowing to check dry season - snowcover) 74 summer duration dry season 40 nr of cons. days with clim. balance below threshold 74 summer duration dry surface 3 consecutive days 74 autumn duration mean temp 10 consecutive days 74 summer duration mean temp 10 consecutive days 74 summer duration nosnow 10 nr of cons. days with snow depth below threshold 74 summer duration snowcover 30 nr of cons. days with snow depth above threshold 74 autumn duration wet subsoil 10 consecutive days 74 summer duration wet subsoil 20 consecutive days 74 summer maximum no snow 1 snow depth during a day below which day counts thawing 74 summer minimum effective rainfall 0 clim. balance during a day below which day is dry season 74 summer minimum snowcover 1 snow depth during a day above which day counts snowcover 74 autumn months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 74 summer months around MSD_FAO 2 months around MSD_FAO to define a max. window for osd 74 autumn years window 15 year 74 summer years window 15 year

B. Field observed sowing dates for winter wheat

oblast name station name latitude longitude year sowing date Krasnodar Otradnaya 44.4 41.5 1988 10/5/1987 Krasnodar Otradnaya 44.4 41.5 1988 10/9/1987 Krasnodar Maikop 44.6 40.1 1988 10/4/1987 Krasnodar Labinsk 44.6 40.7 1988 10/17/1987 Krasnodar Belorechensk 44.7 39.9 1988 10/11/1987 Krasnodar Belorechensk 44.7 39.9 1988 10/20/1987 Krasnodar Abinsk 44.9 38.1 1988 10/5/1987 Krasnodar Krymsk 45 38 1988 10/10/1987 Krasnodar Krasnodar 45.1 39 1988 10/10/1987 Krasnodar Krasnodar 45.1 39 1988 10/10/1987 Krasnodar Kurganinsk 45 40.6 1988 10/14/1987 Krasnodar Armavir 45 41.2 1988 10/10/1987 Krasnodar Anastasievskaya 45.2 37.9 1988 10/15/1987 Krasnodar Ust-labinsk 45.2 39.7 1988 10/4/1987 Krasnodar Ust-labinsk 45.2 39.7 1988 10/8/1987 Krasnodar Demin erik 45.3 38 1988 10/15/1987 Krasnodar Tbilisskaya 45.4 40.2 1988 10/14/1987 Krasnodar Kropotkin 45.4 40.6 1988 10/1/1987 Krasnodar Kropotkin 45.4 40.6 1988 10/1/1987 Krasnodar Kalininskaya 45.5 38.7 1988 10/4/1987 Krasnodar Korenovsk 45.5 39.5 1988 10/4/1987 Krasnodar Korenovsk 45.5 39.5 1988 10/12/1987 Krasnodar Timashevsk 45.6 38.9 1988 10/15/1987 Krasnodar Vyselky 45.6 39.7 1988 9/21/1987 Krasnodar Briukhovetskaya 45.8 39 1988 9/21/1987 Krasnodar Tikhoretsk 45.8 40.1 1988 9/16/1987 Krasnodar Tikhoretsk 45.8 40.1 1988 9/30/1987 Krasnodar Primorsko-akhtarsk 46 38.1 1988 10/2/1987 Krasnodar Kanevskaya 46.1 38.9 1988 9/22/1987 Krasnodar Kanevskaya 46.1 38.9 1988 10/1/1987 Krasnodar Belaya glina 46.1 40.9 1988 9/18/1987 Krasnodar Leningradskaya 46.3 39.4 1988 9/29/1987 Krasnodar Sosyka 46.4 39.2 1988 9/16/1987 Krasnodar Sosyka 46.4 39.2 1988 9/17/1987 Krasnodar Starominskaya 46.5 39 1988 9/19/1987 Krasnodar Starominskaya 46.5 39 1988 9/22/1987 Krasnodar Kuschevskaya 46.5 40 1988 9/16/1987 Krasnodar Kuschevskaya 46.5 40 1988 10/2/1987 Krasnodar Eisk 46.7 38.3 1988 9/21/1987 Krasnodar Eisk 46.7 38.3 1988 9/23/1987 Kursk Belaya 51 35.7 1988 9/3/1987 Kursk Zamostje 51.2 35.3 1988 8/29/1987 Kursk Oboian 51.2 36.5 1988 8/30/1987 Kursk Glushkovo 51.3 34.6 1988 8/26/1987 Kursk Bolshoe-soldatskoe 51.3 35.5 1988 8/28/1987 Kursk Medvenka 51.4 36.1 1988 9/1/1987 Kursk Solntsevo 51.4 36.8 1988 8/23/1987 Kursk Gorshechnoe 51.5 38 1988 9/4/1987

43 Kursk Lgov 51.6 35.3 1988 8/31/1987 Kursk Petrinka 51.6 36.2 1988 8/27/1987 Kursk Tim 51.6 37 1988 8/26/1987 Kursk Kurchatov 51.7 36 1988 8/26/1987 Kursk Novo-kastornoe 51.7 38 1988 8/24/1987 Kursk Konyshevka 51.8 35.7 1988 8/22/1987 Kursk Cheremisinovo 51.8 37.4 1988 8/30/1987 Kursk Zolotukhino 52.1 36.4 1988 9/1/1987 Kursk Zheleznogorsk 52.3 35.5 1988 8/21/1987 Kursk Ponyri 52.3 36.2 1988 9/1/1987 Belgorod Valuiki 50.2 38.1 1993 9/1/1992 Belgorod Gotnia 50.8 35.8 1993 8/28/1992 Briansk Trubchevsk 52.6 33.8 1993 9/16/1992 Kaluga Mosalsk 54.5 35 1993 9/2/1992 Krasnodar Ust-labinsk 45.2 39.7 1993 10/7/1992 Krasnodar Kuschevskaya 46.5 40 1993 9/25/1992 Kursk Lgov 51.6 35.3 1993 9/14/1992 Kursk Petrinka 51.6 36.2 1993 8/31/1992 Lipetsk Lipetsk 52.6 39.6 1993 9/1/1992 Moscow Egorievsk 55.4 39.1 1993 9/2/1992 Moscow Volokolamsk 56.1 36 1993 8/27/1992 Orel Verkhovje 52.8 33.3 1993 8/31/1992 Rostov Gigant 46.5 41.3 1993 9/21/1992 Rostov Millerovo 48.9 40.4 1993 9/1/1992 Ryazan Mikhailov 54.2 39 1993 8/31/1992 Tambov Zherdevka 51.9 41.5 1993 8/26/1992 Tambov Michurinsk 52.9 40.5 1993 8/28/1992 Tula Volovo 53.6 38 1993 8/29/1992 Voronezh Pavlovsk 50.4 40 1993 9/16/1992 Voronezh Anna 51.5 40.4 1993 9/4/1992 Belgorod Valuiki 50.2 38.1 1995 9/21/1994 Belgorod Gotnia 50.8 35.8 1995 8/28/1994 Briansk Trubchevsk 52.6 33.8 1995 9/12/1994 Kaluga Mosalsk 54.5 35 1995 8/26/1994 Krasnodar Ust-labinsk 45.2 39.7 1995 10/11/1994 Krasnodar Kuschevskaya 46.5 40 1995 9/21/1994 Kursk Rylsk 51.6 34.7 1995 9/18/1994 Kursk Petrinka 51.6 36.2 1995 9/9/1994 Lipetsk Lipetsk 52.6 39.6 1995 8/30/1994 Moscow Egorievsk 55.4 39.1 1995 9/4/1994 Moscow Volokolamsk 56.1 36 1995 9/5/1994 Orel Verkhovje 52.8 33.3 1995 9/9/1994 Rostov Gigant 46.5 41.3 1995 10/3/1994 Rostov Chertkovo 48.5 39.9 1995 9/11/1994 Ryazan Mikhailov 54.2 39 1995 8/28/1994 Tambov Michurinsk 52.9 40.5 1995 8/23/1994 Tambov Sh Lenina 53 41 1995 9/2/1994 Tula Volovo 53.6 38 1995 8/27/1994 Voronezh Pavlovsk 50.4 40 1995 9/6/1994 Voronezh Anna 51.5 40.4 1995 9/8/1994 Belgorod Valuiki 50.2 38.1 1997 8/14/1996 Belgorod Gotnia 50.8 35.8 1997 8/26/1996 Briansk Trubchevsk 52.6 33.8 1997 9/11/1996 Chuvashia Poretskoe 55.2 46.3 1997 8/18/1996

44 Ivanovo Shuia 56.9 41.4 1997 8/31/1996 Kabardino-Balkaria Nalchik 43.5 43.6 1997 9/21/1996 Kabardino-Balkaria Baksan 43.7 43.5 1997 10/6/1996 Kaluga Sukhinichi 54.1 35.3 1997 9/4/1996 Kaluga Mosalsk 54.5 35 1997 8/30/1996 Krasnodar Ust-labinsk 45.2 39.7 1997 10/1/1996 Krasnodar Kuschevskaya 46.5 40 1997 9/17/1996 Kursk Rylsk 51.6 34.7 1997 9/10/1996 Kursk Petrinka 51.6 36.2 1997 9/11/1996 Lipetsk Lipetsk 52.6 39.6 1997 8/30/1996 Mary El Kozmodemiansk 56.4 46.6 1997 9/3/1996 Moscow Egorievsk 55.4 39.1 1997 9/1/1996 Moscow Volokolamsk 56.1 36 1997 9/5/1996 Novosibirsk Bagan 54.1 77.7 1997 9/5/1996 Omsk Omsk 55 73.4 1997 8/25/1996 Orel Verkhovje 52.8 33.3 1997 8/26/1996 Rostov Gigant 46.5 41.3 1997 9/20/1996 Rostov Chertkovo 48.5 39.9 1997 8/28/1996 Ryazan Mikhailov 54.2 39 1997 9/13/1996 Tambov Zherdevka 51.9 41.5 1997 8/22/1996 Tambov Michurinsk 52.9 40.5 1997 8/21/1996 Tula Volovo 53.6 38 1997 9/9/1996 Voronezh Pavlovsk 50.4 40 1997 9/5/1996 Voronezh Anna 51.5 40.4 1997 9/8/1996 Yaroslavl Rybinsk 58.1 38.7 1997 9/8/1996 Belgorod Valuiki 50.2 38.1 1998 8/28/1997 Belgorod Gotnia 50.8 35.8 1998 8/29/1997 Briansk Trubchevsk 52.6 33.8 1998 9/18/1997 Briansk Briansk 53.2 34.3 1998 9/10/1997 Ivanovo Shuia 56.9 41.4 1998 8/28/1997 Kalmykia Troitskoe 46.4 44.2 1998 9/11/1997 Kaluga Sukhinichi 54.1 35.3 1998 9/13/1997 Kaluga Mosalsk 54.5 35 1998 9/2/1997 Krasnodar Ust-labinsk 45.2 39.7 1998 10/10/1997 Krasnodar Kanevskaya 46.1 38.9 1998 9/21/1997 Krasnodar Kuschevskaya 46.5 40 1998 9/18/1997 Kursk Rylsk 51.6 34.7 1998 9/13/1997 Kursk Petrinka 51.6 36.2 1998 9/11/1997 Lipetsk Lipetsk 52.6 39.6 1998 9/5/1997 Mary El Kozmodemiansk 56.4 46.6 1998 8/26/1997 Mordovia Temnikov 54.6 43.2 1998 8/26/1997 Moscow Egorievsk 55.4 39.1 1998 8/30/1997 Moscow Volokolamsk 56.1 36 1998 9/20/1997 Omsk Omsk 55 73.4 1998 8/22/1997 Orel Verkhovje 52.8 33.3 1998 9/1/1997 Orenburg Chebenki 51.9 55.7 1998 9/1/1997 Penza Belinsky 52.9 43.4 1998 9/9/1997 Rostov Gigant 46.5 41.3 1998 9/10/1997 Rostov Chertkovo 48.5 39.9 1998 9/5/1997 Rostov Bokovskaya 49.2 41.8 1998 9/3/1997 Ryazan Mikhailov 54.2 39 1998 8/26/1997 Samara Novodevichje 53.6 48.8 1998 8/31/1997 Stavropol Izobilny 45.4 41.7 1998 10/1/1997 Stavropol Arzgir 45.4 44.2 1998 9/25/1997

45 Tambov Zherdevka 51.9 41.5 1998 8/24/1997 Tambov Tambov 52.7 41.5 1998 8/31/1997 Tambov Morshansk 53.5 41.8 1998 8/31/1997 Tula Efremov 53.2 38.1 1998 8/24/1997 Tula Volovo 53.6 38 1998 8/31/1997 Vladimir Murom 55.6 42 1998 8/31/1997 Voronezh Pavlovsk 50.4 40 1998 9/5/1997 Voronezh Anna 51.5 40.4 1998 9/12/1997 Altai Barnaul 53.4 83.8 1999 8/26/1998 Belgorod Gotnia 50.8 35.8 1999 8/28/1998 Chuvashia Poretskoe 55.2 46.3 1999 8/24/1998 Dagestan Levashi 42.4 47.3 1999 9/14/1998 Dagestan Kizliar 43.9 46.7 1999 10/2/1998 Kabardino-Balkaria Baksan 43.7 43.5 1999 10/6/1998 Kalmykia Yashalta 46.3 42.3 1999 9/21/1998 Kaluga Maloyaroslavets 55 36.5 1999 9/22/1998 Krasnodar Otradnaya 44.4 41.5 1999 11/2/1998 Krasnodar Krasnodar 45.1 39 1999 10/6/1998 Krasnodar Kropotkin 45.4 40.6 1999 9/22/1998 Krasnodar Tikhoretsk 45.9 40.1 1999 9/21/1998 Krasnodar Kanevskaya 46.1 38.9 1999 9/28/1998 Kursk Rylsk 51.6 34.7 1999 9/19/1998 Kursk Tim 51.6 37.1 1999 8/27/1998 Lipetsk Elets 52.7 38.5 1999 9/1/1998 Mordovia Temnikov 54.6 43.2 1999 8/28/1998 Nizhny Novgorod Sergach 55.5 45.5 1999 9/10/1998 Orel Bolkhov 53.4 36 1999 9/15/1998 Penza Radischevo 53 46.4 1999 8/27/1998 Penza Penza 53.2 45 1999 8/20/1998 Rostov Gigant 46.5 41.3 1999 9/29/1998 Rostov Semikarakorsk 47.5 40.8 1999 10/9/1998 Ryazan Mikhailov 54.2 39 1999 9/6/1998 Stavropol Blagodarny 45.1 43.4 1999 10/14/1998 Stavropol Izobilny 45.4 41.7 1999 11/7/1998 Stavropol Krasnogvardeiskoe 45.8 41.5 1999 9/30/1998 Stavropol Divnoe 45.9 43.4 1999 9/19/1998 Tambov Tambov 52.7 41.5 1999 8/30/1998 Tula Efremov 53.2 38.1 1999 9/16/1998 Tula Volovo 53.6 38 1999 9/10/1998 Ulianovsk Sengiley 54 48.8 1999 8/2/1998 Vladimir Murom 55.6 42 1999 9/8/1998 Volgograd Seraphimovich 49.6 42.7 1999 8/9/1998 Volgograd Frolovo 49.8 43.7 1999 8/16/1998 Voronezh Anna 51.5 40.4 1999 9/12/1998 Altai Barnaul 53.4 83.8 2000 8/22/1999 Belgorod Gotnia 50.8 35.8 2000 8/25/1999 Dagestan Levashi 42.4 47.3 2000 9/18/1999 Dagestan Kizliar 43.9 46.7 2000 10/11/1999 Kabardino-Balkaria Nalchik 43.5 43.6 2000 11/20/1999 Kalmykia Yashalta 46.3 42.3 2000 11/4/1999 Kalmykia Troitskoe 46.4 44.2 2000 9/1/1999 Kaluga Maloyaroslavets 55 36.5 2000 9/12/1999 Krasnodar Otradnaya 44.4 41.5 2000 11/1/1999 Krasnodar Krasnodar 45.1 39 2000 10/2/1999

46 Krasnodar Kropotkin 45.4 40.6 2000 10/11/1999 Krasnodar Tikhoretsk 45.9 40.1 2000 10/7/1999 Kursk Tim 51.6 37.1 2000 8/24/1999 Lipetsk Lipetsk 52.6 39.6 2000 9/2/1999 Lipetsk Elets 52.7 38.5 2000 9/1/1999 Nizhny Novgorod Sergach 55.5 45.5 2000 9/3/1999 Novosibirsk Bagan 54.1 77.7 2000 9/12/1999 Orel Verkhovje 52.8 33.3 2000 8/22/1999 Orel Bolkhov 53.4 36 2000 9/21/1999 Penza Radischevo 53 46.4 2000 9/1/1999 Penza Penza 53.2 45 2000 9/2/1999 Rostov Gigant 46.5 41.3 2000 9/17/1999 Rostov Semikarakorsk 47.5 40.8 2000 9/7/1999 Ryazan Mikhailov 54.2 39 2000 9/16/1999 Samara Bolshaya glushitsa 52.4 50.5 2000 8/21/1999 Stavropol Blagodarny 45.1 43.4 2000 10/1/1999 Stavropol Krasnogvardeiskoe 45.8 41.5 2000 10/5/1999 Stavropol Divnoe 45.9 43.4 2000 9/17/1999 Tambov Morshansk 53.5 41.8 2000 8/14/1999 Tatarstan Chistopol 55.3 50.6 2000 9/5/1999 Ulianovsk Sengiley 54 48.8 2000 8/28/1999 Volgograd Seraphimovich 49.6 42.7 2000 8/22/1999 Volgograd Frolovo 49.8 43.7 2000 9/1/1999 Voronezh Kamennaia step 51.1 40.7 2000 9/21/1999 Voronezh Anna 51.5 40.4 2000 9/18/1999 Orenburg Chebenki 51.9 55.7 2001 8/24/2000 Samara Bolshaya glushitsa 52.4 50.5 2001 8/24/2000 Saratov Krasny kut 50.9 46.9 2001 9/2/2000 Saratov Atkarsk 51.9 45 2001 9/20/2000 Saratov Pugachev 52 48.8 2001 10/1/2000 Tatarstan Chistopol 55.3 50.6 2001 8/21/2000

C. Field observed sowing dates for spring wheat

oblast name station name latitude longitude year sowing date Bashkiria Yakiar 51.8 58.2 1985 5/4/1985 Bashkiria Zilair 52.2 57.5 1985 5/18/1985 Bashkiria Tselinnoe 52.2 58.6 1985 5/8/1985 Bashkiria Mrakovo 52.7 56.6 1985 5/12/1985 Bashkiria Meleuz 52.9 55.9 1985 5/10/1985 Bashkiria Fedorovka 53.2 55.2 1985 5/15/1985 Bashkiria Sterlibashevo 53.4 55.3 1985 5/4/1985 Bashkiria Sterlitamak 53.6 56 1985 5/20/1985 Bashkiria Uchaly 54.3 59.5 1985 5/10/1985 Bashkiria Arkhangelskoe 54.4 56.5 1985 5/6/1985 Bashkiria Buzdiak 54.5 54.5 1985 5/6/1985 Bashkiria Tuimazy 54.6 53.7 1985 5/15/1985 Bashkiria Chishmy 54.6 55.4 1985 5/12/1985 Bashkiria Gakaly 55.2 53.8 1985 5/6/1985 Bashkiria Kushnarenkovo 55.1 55.3 1985 5/1/1985 Bashkiria Aksarovo 55.3 58.3 1985 5/8/1985 Bashkiria Birsk 55.4 55.5 1985 5/5/1985 Bashkiria Duvan 55.7 57.9 1985 5/4/1985 Bashkiria Askino 56.1 56.6 1985 5/16/1985 Chuvashia Poretskoe 55.2 46.3 1997 5/7/1997 Chuvashia Kanash 55.5 47.5 1997 5/16/1997 Ivanovo Shuia 56.9 41.4 1997 5/2/1997 Kursk Rylsk 51.6 34.7 1997 4/30/1997 Lipetsk Lipetsk 52.6 39.6 1997 4/26/1997 Mary El Kozmodemiansk 56.4 46.6 1997 5/24/1997 Nizhny Novgorod Vetluga 57.9 45.8 1997 5/8/1997 Orenburg Akbulak 51 55.6 1997 5/10/1997 Orenburg Chebenki 51.9 55.7 1997 4/28/1997 Samara Bezenchuk 53 49.4 1997 5/9/1997 Briansk Briansk 53.2 34.3 1998 5/4/1998 Ivanovo Privolzhsk 57.4 41.3 1998 5/22/1998 Kostroma Galich 58.4 42.4 1998 5/16/1998 Mary El Kozmodemiansk 56.4 46.6 1998 5/22/1998 Mordovia Atiashevo 54.6 46.1 1998 5/10/1998 Orel Verkhovje 52.8 33.3 1998 4/24/1998 Orenburg Akbulak 51 55.6 1998 5/17/1998 Orenburg Chebenki 51.9 55.7 1998 5/12/1998 Orenburg Troitskoe 52.3 56.4 1998 5/14/1998 Penza Kondol 52.8 45.1 1998 5/12/1998 Samara Novodevichje 53.6 48.8 1998 5/13/1998 Tambov Morshansk 53.5 41.8 1998 5/6/1998 Tula Efremov 53.2 38.1 1998 5/5/1998 Udmurtia Selty 57.3 52.2 1998 5/22/1998 Viatka Nolinsk 57.6 49.9 1998 5/17/1998 Volgograd Pallasovka 50.1 46.9 1998 4/30/1998 Yaroslavl Rybinsk 58.1 38.7 1998 5/29/1998 Astrakhan Staritsa 48.2 45.9 1999 4/9/1999 Chuvashia Poretskoe 55.2 46.3 1999 5/1/1999

48 Kostroma Galich 58.4 42.4 1999 5/6/1999 Kursk Rylsk 51.6 34.7 1999 4/6/1999 Mordovia Atiashevo 54.6 46.1 1999 4/28/1999 Nizhny Novgorod Lukoianov 55 44.5 1999 5/5/1999 Nizhny Novgorod Sergach 55.5 45.5 1999 5/1/1999 Penza Belinsky 52.9 43.4 1999 4/28/1999 Penza Radischevo 53 46.4 1999 5/7/1999 Ryazan Mikhailov 54.2 39 1999 5/8/1999 Samara Bezenchuk 53 49.4 1999 4/29/1999 Tatarstan Chulpanovo 54.5 50.4 1999 5/6/1999 Tula Efremov 53.2 38.1 1999 4/22/1999 Udmurtia Mozhga 56.4 52.2 1999 5/4/1999 Ulianovsk Sengiley 54 48.8 1999 5/2/1999 Viatka Nolinsk 57.6 49.9 1999 5/15/1999 Viatka Falenki 58.4 51.6 1999 5/20/1999 Viatka Nagorskoe 59.3 50.8 1999 5/14/1999 Volgograd Frolovo 49.8 43.7 1999 4/23/1999 Volgograd Pallasovka 50.1 46.9 1999 4/28/1999 Bashkiria Mrakovo 52.7 56.6 2000 5/14/2000 Bashkiria Meleuz 52.9 55.9 2000 4/28/2000 Bashkiria Duvan 55.7 57.9 2000 5/13/2000 Briansk Briansk 53.2 34.3 2000 4/22/2000 Chuvashia Kanash 55.5 47.5 2000 4/29/2000 Ivanovo Privolzhsk 57.4 41.3 2000 4/29/2000 Lipetsk Lipetsk 52.6 39.6 2000 4/20/2000 Nizhny Novgorod Sergach 55.5 45.5 2000 4/26/2000 Orel Bolkhov 53.4 36 2000 4/29/2000 Orenburg Sorochinsk 52.4 53.1 2000 4/28/2000 Ryazan Mikhailov 54.2 39 2000 4/28/2000 Tambov Morshansk 53.5 41.8 2000 5/2/2000 Tatarstan Chulpanovo 54.5 50.4 2000 5/16/2000 Tatarstan Tetiushi 54.9 48.9 2000 4/28/2000 Udmurtia Mozhga 56.4 52.2 2000 4/30/2000 Ulianovsk Sengiley 54 48.8 2000 4/29/2000 Viatka Falenki 58.4 51.6 2000 5/14/2000 Viatka Nagorskoe 59.3 50.8 2000 4/26/2000 Volgograd Frolovo 49.8 43.7 2000 5/12/2000 Bashkiria Meleuz 52.9 55.9 2001 5/8/2001 Bashkiria Duvan 55.7 57.9 2001 5/7/2001 Orenburg Chebenki 51.9 55.7 2001 5/2/2001 Orenburg Sorochinsk 52.4 53.1 2001 5/2/2001 Samara Novodevichje 53.6 48.8 2001 5/1/2001 Saratov Krasny kut 50.9 46.9 2001 4/21/2001 Saratov Pugachev 52 48.8 2001 5/3/2001 Tatarstan Tetiushi 54.9 48.9 2001 5/3/2001 Tatarstan Chistopol 55.3 50.6 2001 5/5/2001 Ulianovsk Annenkovo 54.1 47.4 2001 4/22/2001 Vologda Semenkovo 59.3 39.7 2001 5/1/2001

D. Field observed sowing dates for potato

oblast name station name latitude longitude year sowing date Bashkiria Fedorovka 53.2 55.2 1985 5/26/1985 Bashkiria Sterlitamak 53.6 56 1985 5/22/1985 Bashkiria Uchaly 54.3 59.5 1985 5/15/1985 Bashkiria Kushnarenkovo 55.1 55.3 1985 5/16/1985 Bashkiria Gakaly 55.2 53.8 1985 5/18/1985 Bashkiria Birsk 55.4 55.5 1985 5/28/1985 Bashkiria Duvan 55.7 57.9 1985 5/29/1985 Bashkiria Yanaul 56.3 55 1985 5/20/1985 Krasnodar Anapa 44.9 37.3 1988 4/8/1988 Krasnodar Armavir 45 41.2 1988 3/24/1988 Krasnodar Kropotkin 45.4 40.6 1988 4/2/1988 Kursk Gorshechnoe 51.5 38 1988 5/8/1988 Kursk Petrinka 51.6 36.2 1988 5/11/1988 Kursk Tim 51.6 37.1 1988 5/17/1988 Kursk Schigry 51.9 36.9 1988 5/18/1988 Kursk Dmitriev 52.1 35.1 1988 5/18/1988 Kursk Fatezh 52.1 35.9 1988 5/17/1988 Chuvashia Poretskoe 55.2 46.3 1997 5/22/1997 Ivanovo Shuia 56.9 41.4 1997 5/13/1997 Kabardino-Balkaria Nalchik 43.5 43.6 1997 5/10/1997 Kemerovo Kemerovo 55.2 86.2 1997 5/8/1997 Khakassia Tashtyp 52.8 89.9 1997 5/20/1997 Krasnojarsk Nyzhny Ingash 56.2 96.6 1997 5/26/1997 Mary El Kozmodemiansk 56.4 46.6 1997 5/24/1997 Nizhny Novgorod Vetluga 57.9 45.8 1997 5/28/1997 Omsk Omsk 55 73.4 1997 5/22/1997 Stavropol Nevinnomyssk 44.6 41.9 1997 5/15/1997 Yaroslavl Rybinsk 58.1 38.7 1997 5/14/1997 Briansk Briansk 53.2 34.3 1998 5/10/1998 Ivanovo Shuia 56.9 41.4 1998 5/13/1998 Ivanovo Privolzhsk 57.4 41.3 1998 5/20/1998 Kaluga Sukhinichi 54.1 35.3 1998 6/4/1998 Kemerovo Kemerovo 55.2 86.2 1998 5/29/1998 Kostroma Kostroma 57.8 40.9 1998 5/22/1998 Kostroma Galich 58.4 42.4 1998 5/27/1998 Krasnojarsk Idrinskoe 54.4 92.1 1998 6/6/1998 Mary El Kozmodemiansk 56.4 46.6 1998 5/27/1998 Mary El Nartas 56.8 49.8 1998 5/29/1998 Mordovia Temnikov 54.6 43.2 1998 5/12/1998 Mordovia Atiashevo 54.6 46.1 1998 5/26/1998 Nizhny Novgorod Vetluga 57.9 45.8 1998 6/4/1998 Omsk Omsk 55 73.4 1998 6/2/1998 Penza Belinsky 52.9 43.4 1998 5/16/1998 Stavropol Nevinnomyssk 44.6 41.9 1998 5/19/1998 Tambov Tambov 52.7 41.5 1998 5/10/1998 Tambov Morshansk 53.5 41.8 1998 5/16/1998 Tiumen Tiumen 57.1 65.4 1998 5/31/1998 Tula Volovo 53.6 38 1998 5/8/1998

50 Udmurtia Selty 57.3 52.2 1998 5/30/1998 Ulianovsk Sengiley 54 48.8 1998 5/11/1998 Viatka Yaransk 57.4 47.9 1998 6/6/1998 Vladimir Murom 55.6 42 1998 5/16/1998 Vladimir Suzdal 56.4 40.4 1998 5/22/1998 Yaroslavl Rybinsk 58.1 38.7 1998 5/19/1998 Altai Talmenka 53.8 83.6 1999 5/26/1999 Chuvashia Poretskoe 55.2 46.3 1999 5/22/1999 Dagestan Levashi 42.4 47.3 1999 5/10/1999 Kaluga Maloyaroslavets 55 36.5 1999 5/24/1999 Komi Ust-kulom 61.7 53.7 1999 6/1/1999 Kostroma Kostroma 57.8 40.9 1999 5/26/1999 Kostroma Galich 58.4 42.4 1999 5/28/1999 Krasnodar Otradnaya 44.4 41.5 1999 4/7/1999 Kursk Tim 51.6 37.1 1999 5/6/1999 Lipetsk Elets 52.7 38.5 1999 5/4/1999 Mary El Nartas 56.8 49.8 1999 5/30/1999 Mordovia Temnikov 54.6 43.2 1999 5/18/1999 Mordovia Atiashevo 54.6 46.1 1999 5/16/1999 Nizhny Novgorod Lukoianov 55 44.5 1999 5/23/1999 Nizhny Novgorod Sergach 55.5 45.5 1999 5/28/1999 Novosibirsk Ogurtsovo 54.9 83 1999 5/18/1999 Omsk Russkaya poliana 53.8 73.9 1999 5/20/1999 Orel Bolkhov 53.4 36 1999 5/16/1999 Penza Belinsky 52.9 43.4 1999 5/29/1999 Penza Radischevo 53 46.4 1999 5/3/1999 Ryazan Tuma 55.1 40.6 1999 5/11/1999 Tambov Tambov 52.7 41.5 1999 4/29/1999 Tiumen Berdiuzhje 55.8 68.3 1999 5/22/1999 Tiumen Tiumen 57.1 65.4 1999 5/16/1999 Tomsk Kozhevnikovo 56.2 84 1999 5/26/1999 Tomsk Parabel 58.7 81.5 1999 6/1/1999 Tula Volovo 53.6 38 1999 5/14/1999 Udmurtia Mozhga 56.4 52.2 1999 5/22/1999 Udmurtia Selty 57.3 52.2 1999 6/4/1999 Ulianovsk Sengiley 54 48.8 1999 4/29/1999 Viatka Falenki 58.4 51.6 1999 5/24/1999 Vladimir Murom 55.6 42 1999 5/14/1999 Vladimir Suzdal 56.4 40.4 1999 4/30/1999 Voronezh Anna 51.5 40.4 1999 4/28/1999 Altai Talmenka 53.8 83.6 2000 5/28/2000 Bashkiria Duvan 55.7 57.9 2000 6/1/2000 Briansk Briansk 53.2 34.3 2000 5/16/2000 Dagestan Levashi 42.4 47.3 2000 5/11/2000 Ekaterinburg Butka 56.7 63.8 2000 5/27/2000 Ekaterinburg Krasnopolianskoe 57.2 63.7 2000 6/12/2000 Ivanovo Privolzhsk 57.4 41.3 2000 5/11/2000 Kabardino-Balkaria Nalchik 43.5 43.6 2000 4/14/2000 Kaluga Maloyaroslavets 55 36.5 2000 5/20/2000 Komi Ust-kulom 61.7 53.7 2000 5/31/2000 Krasnodar Otradnaya 44.4 41.5 2000 4/17/2000 Krasnojarsk Tiukhtet 56.5 89.3 2000 5/31/2000 Kurgan Makushino 55.3 67.3 2000 5/16/2000 Kurgan Pamiatnaya 56 65.7 2000 6/5/2000

51 Kursk Tim 51.6 37.1 2000 5/4/2000 Lipetsk Elets 52.7 38.5 2000 5/6/2000 Nizhny Novgorod Lukoianov 55 44.5 2000 5/30/2000 Nizhny Novgorod Sergach 55.5 45.5 2000 5/26/2000 Novosibirsk Ogurtsovo 54.9 83 2000 5/20/2000 Omsk Russkaya poliana 53.8 73.9 2000 6/6/2000 Orel Bolkhov 53.4 36 2000 5/15/2000 Orenburg Burannoe 51 54.5 2000 4/24/2000 Penza Belinsky 52.9 43.4 2000 5/22/2000 Penza Radischevo 53 46.4 2000 5/22/2000 Ryazan Tuma 55.1 40.6 2000 5/20/2000 Tambov Morshansk 53.5 41.8 2000 5/22/2000 Tatarstan Tetiushi 54.9 48.9 2000 5/21/2000 Tatarstan Chistopol 55.3 50.6 2000 5/26/2000 Tomsk Kozhevnikovo 56.2 84 2000 5/27/2000 Tomsk Parabel 58.7 81.5 2000 5/27/2000 Udmurtia Mozhga 56.4 52.2 2000 5/26/2000 Ulianovsk Sengiley 54 48.8 2000 5/11/2000 Viatka Falenki 58.4 51.6 2000 5/24/2000 Vologda Totma 60 42.8 2000 5/25/2000 Voronezh Anna 51.5 40.4 2000 5/4/2000 Bashkiria Duvan 55.7 57.9 2001 5/30/2001 Khakassia Tashtyp 52.8 89.9 2001 5/16/2001 Krasnojarsk Tiukhtet 56.5 89.3 2001 6/3/2001 Orenburg Burannoe 51 54.5 2001 5/22/2001 Saratov Ozinki 51.2 49.8 2001 5/18/2001 Saratov Ershov 51.4 48.3 2001 5/12/2001 Tatarstan Tetiushi 54.9 48.9 2001 5/8/2001 Tyva Saryg-sep 51.5 95.6 2001 5/19/2001 Vologda Semenkovo 59.3 39.7 2001 6/1/2001 Vologda Totma 60 42.8 2001 6/5/2001

E. Field observed sowing dates for grain maize

oblast name station name latitude longitude year sowing date Bashkiria Yakiar 51.8 58.2 1985 6/2/1985 Bashkiria Tselinnoe 52.2 58.6 1985 5/24/1985 Bashkiria Mrakovo 52.7 56.6 1985 5/26/1985 Bashkiria Chishmy 54.6 55.4 1985 6/10/1985 Bashkiria Kushnarenkovo 55.1 55.3 1985 6/12/1985 Bashkiria Birsk 55.4 55.5 1985 5/24/1985 Kabardino-Balkaria Baksan 43.7 43.5 1997 5/10/1997 Krasnodar Kanevskaya 46.1 38.9 1998 4/21/1998 Rostov Bokovskaya 49.2 41.8 1998 5/16/1998 Dagestan Kizliar 43.9 46.7 1999 5/15/1999 Kabardino-Balkaria Baksan 43.7 43.5 1999 5/1/1999 Krasnodar Otradnaya 44.4 41.5 1999 5/11/1999 Krasnodar Kanevskaya 46.1 38.9 1999 4/12/1999 Rostov Semikarakorsk 47.5 40.8 1999 4/26/1999 Rostov Bokovskaya 49.2 41.8 1999 5/23/1999 Stavropol Krasnogvardeiskoe 45.8 41.5 1999 5/4/1999 Tula Efremov 53.2 38.1 1999 5/24/1999 Voronezh Kamennaia step 51.1 40.7 1999 5/26/1999 Belgorod Gotnia 50.8 35.8 2000 4/28/2000 Dagestan Kizliar 43.9 46.7 2000 5/12/2000 Rostov Semikarakorsk 47.5 40.8 2000 5/20/2000 Stavropol Krasnogvardeiskoe 45.8 41.5 2000 4/27/2000 Voronezh Kamennaia step 51.1 40.7 2000 5/26/2000

54 European Commission

EUR 22233 EN – DG Joint Research Centre, Institute for the Protection and Security of the Citizen Title: Climatically optimal planting dates (COP determinator, version 1) Authors: I. Savin, H. Boogaard, C. van Diepen, H. van der Ham

Reviewers: G. Genovese, O. Leo

Luxembourg: Office for Official Publications of the European Communities 2007 – 55 pp. – 29 x 21 cm EUR – Scientific and Technical Research series; ISSN 1018-5593

Abstract The Climatically Optimal Planting date (COP) determinator has been elaborated for application in Mediterranean, Central Asian countries and former Soviet Union republics. The COP is a set of expert rules which allows determining crop sowing dates, which are optimal from point of view of the meteorological conditions. The COP has been designed for application in Crop Growth Monitoring System (CGMS). The monograph contains description of the expert rules, and the results of the COP validation, which has been done by comparing estimated sowing dates with near 500 field observed sowing dates for winter and spring wheat, maize and potatoes in Russia.

The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national.