Climatically Optimal Planting Dates

Transcription

1 Climatically Optimal Planting Dates COP Determinator (version ) I. Savin, H. Boogaard, C. van Diepen, H. van der Ham EUR EN

2 The Institute for the Protection and Security of the Citizen provides researchbased, systemsoriented 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-2020 Ispra (VA), Italy Tel.: Fax: 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 PUBSY 7094 EUR EN ISSN Luxembourg: Office for Official Publications of the European Communities European Communities, 2007 Reproduction is authorised provided the source is acknowledged Printed in Italy

3 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 ) by I. Savin, H. Boogaard, C. van Diepen, H. van der Ham Italy, 2007

4 CONTENTS Introduction... 3 Region of interest... Algorithm... 8 Rules for winter crops... 9 Rules for autumn sown crops... 0 Rules for spring sown crops... 2 In-chain-sown and other crops... Validation... 6 Winter wheat... 8 Spring wheat Potato... 2 Grain maize Conclusion References Appendix A. Description of tables storing data needed to estimate year specific sowing dates and initial soil moisture B. Field observed sowing dates for winter wheat C. Field observed sowing dates for spring wheat D. Field observed sowing dates for potato... 0 E. Field observed sowing dates for grain maize... 3

5 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 44/2000/EC and 2066/2003/EC on the application of area frame survey and remote sensing techniques to the agricultural statistics for 999 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 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 200 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. Agrometeorological 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

6 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. 4

7 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. Fig.. 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. 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.

8 crop country wheat barley maize millet Afghanistan Algeria Armenia Azerbaijan Belarus Egypt Georgia Iran Iraq Israel Jordan Kazakhstan Kuwait Kyrgyzstan Lebanon Libya Moldova Morocco Nepal Pakistan Palestine Russia Saudi Arabia Syria Tajikistan Tunisia Turkmenistan Ukraine Uzbekistan soya Table. 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 ), 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). sorghum rice sunflower sugar beet sugar cane rape seed potato 6

9 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

10 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 ). Local agronomical practice knowledge is used as a basis for the rules elaboration ((Agro-meteorological, 98, 96, 966; Agroclimatic, 968, 97, 972; Reference book, 986; Narciso et al., 992) 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.). 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.. Crop groups 8

11 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:. The average daily temperature should be below the 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 years. For each year and each grid cell the sum of positive air temperatures, above 3 º Celsius (Tbase), is calculated starting from December 3 (window md end) backwards. If at September (window md start) the sum is less than 00 the sowing date is set on September. In other cases the sowing date is equal to the day that the sum of 00 º Celsius (Tsum optimal) is reached. The latest sowing day (LSD) is the latest sowing date over 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:. Check the weather on the ESD 2. Calculate the average daily temperature over the past 0 days (Tav0d) (duration mean temperature). If Tav0d > 7 ºC (Tmean max): go to the next day and apply step. If Tav0d <= 7 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. 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 0 mm (R wet subsoil), then sowing takes place. Otherwise go to the next day and apply step. 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_MONTHDAY and START_MONTH). 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. 9

12 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 7 º Celsius). The sowing date is determined by two controlling factors:. The average daily temperature should be below the 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 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 years based on the following rules:. Check the weather on the earliest sowing day (ESD_FAO) 2. Calculate the average daily temperature over the past 0 days (Tav0d) (duration mean Temp). Check if Tav0d is equal or below 7 º Celsius (Tmean max). If not go to the next day and apply step ; if yes check the following criteria (sufficient water). 3. If the sum of the difference between rainfall and ETA over the previous 0 days (duration wet subsoil) >0 mm (R wet subsoil), then sowing takes place, if <=0, then go to the next day and apply step Next, determine for each climatic grid cell the sowing period which is marked by the earliest and latest sowing date found over the past 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.. Check the weather on the ESD 2. Calculate the average daily temperature over the past 0 days (Tav0d). Check if Tav0d is equal or below Tav0d. If not go to the next day and apply step ; if yes check the following criteria (sufficient water) 3. If the sum of the difference between rainfall and ETA of the previous 0 days >0, then sowing takes place, if <=0, then go to the next day and apply step 0

13 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_MONTHDAY and START_MONTH). 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 Tav0d wheat barley rapeseed potato-fall The Kc applies to the initial stage of each crop because the rules analyze the favourable conditions for initial stage of crop growth.

14 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, potatowinter, potato-spring, and potato-summer and rice). The sowing date is determined by two controlling factors:. The average daily temperature should be above the 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 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 years based on the following rules:. Check the weather on the earliest sowing day (ESD_FAO) 2. Calculate the average daily temperature over the past 0 days (Tav0d) (duration mean temp). Check if Tav0d is equal or higher Tav0d (Tmean min). If not go to the next day and apply step ; if yes check the following criteria (workability) (NB for Rice Tav0d 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 ; 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 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.. Check the weather on the earliest sowing day (ESD) 2. Calculate the average daily temperature over the past 0 days (Tav0d). Check if Tav0d is equal or higher Tav0d. If not go to the next day and apply step ; if yes check the following rule (workability) (NB for Rice Tav0d is the only rule!) 2

15 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 ; 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_MONTHDAY and START_MONTH). Finally, the initial soil moisture conditions are determined. Different regions are defined with different rules for initialization. The definition of regions is as follows:. 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 (>= cm) (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 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 Tav0d values (Tmean min) are used: Kc Threshold Tav0d Spring wheat Spring barley

16 Spring rapeseed Maize Millet 0.3 Sorghum 0.3 Rice - 6 Sugar beet Sugar cane 0.4 Soya 0.4 Sunflower Potato winter 0. Potato spring 0. Potato summer 0. 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. 4

17 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-chainsown" crops is the maturity dekad plus. From this dekad the year specific sowing date is derived and written into the table CROP_CALENDAR (columns START_MONTHDAY and START_MONTH). 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.

18 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 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 degree grid cell were considered as constant. 6

19 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. 7

20 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 6. 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 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 2 days later than the estimated dates. However, the variance is very large (range is 90 days, and standard deviation is near ). 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 200 to days for 99. 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 days with maximum in 30 days. One of the reasons of such large variance is the management like availability of combines, 8

21 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 by 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 Mean St. Er Median Mode St. Dev Sample Var Kurtosis Skewness Range Minimum Maximum Count Frequency Histogram (all years) More days Fig.8. Histogram of the deviation between observed and estimated winter wheat sowing dates for all available years 9

22 Histogram (988) Histogram (993) Frequency Frequency More More days days Histogram (99) Histogram (997) Frequency Frequency More More days days Histogram (998) Histogram (999) Frequency More Frequency 3 More days days Fig.9. Histogram of the deviation between real and simulated winter wheat sowing dates for separate years 20

23 Fig.9. continuation Histogram (2000) Histogram (200) Frequency More Frequency 2 3 More days days 2

24 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.0). The number of records per year varies from 0 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.0. 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 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 3, and Figures -2. 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 997 to days for 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 -2 weeks from the climatically optimal dates (Agro-meteorological, 98, 96, 966; Agro-climatic, 968, 97, 972; Reference book, 986). 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

25 neighbour parcels in the same year can theoretically vary from number of days up to -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 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 by 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 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Histogram (all years) Frequency days Fig.. Histogram of the deviation between observed and estimated spring wheat sowing dates for all available years 23

26 Histogram (98) Histogram (997) 7 3 Frequency Frequency days days Histogram (998) Histogram (999) 4 4 Frequency 3 2 Frequency days days Histogram (2000) Histogram (200) 6 4 Frequency Frequency days days Fig.2. Histogram of the deviation between observed and estimated spring wheat sowing dates for separate years 24

27 Potato The database of field observed sowing dates of potato contains 33 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.3). The number of records per year varies from 9 to 3. 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.3. 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 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table 4, and Figure 4. Analysis of data over all available years shows that field observed dates are on average 6 days later than the estimated dates. However, the variance is very large (range is 63 days with the standard deviation near 2). Descriptive statistics for separate years demonstrate that the mean deviation between observed field dates and estimated dates varies from day for year 988 to 22 days for 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 4 days with standard deviation near 6 for 98 to 4 days with standard deviation near 3 for

28 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, 98, 96, 966; Agro-climatic, 968, 97, 972). 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 6 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 by 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 Mean Stand. Error Median Mode Stand. Dev Sample Var Kurtosis Skewness Range Minimum Maximum Count

29 Histogram (all years) Histogram (98) 4 4 Frequency Frequency More More days days Histogram (988) Histogram (997) Frequency 3 2 Frequency More More days days Histogram (998) Histogram (999) 8 6 Frequency More Frequency More days days Fig.4. Histogram of the deviation between observed and estimated potato sowing dates 27

30 28 Fig.4. continuation Histogram (2000) More days Frequency Histogram (200) More days Frequency

31 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 years, but the exact number of years between locations differs (fig.). The number of records per year is very different: only one record was obtained for 997, two records for 998, 9 records for 999, records for 2000 and 6 records for 98. 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.. 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 degree spatial resolution grid. The difference between observed field and estimated dates is summarized in Table, and Figure 6. 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 3 days with standard deviation near 2). Descriptive statistics for separate years demonstrate that the mean deviation of estimated dates from observed field dates varies from -0 days for 98 to days for 999. The mean deviation (difference between observed and estimated dates) is negative only for 98. The largest negative deviation is -27 days; the maximal positive deviation is 26 days, and it was observed for 999. The variance shows similar behaviour as the analysis of all years together. The range is on average near days. 29

32 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 day with a standard deviation of 2 days. Perhaps the rules for maize have to be adapted applying a higher temperature. 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. Descriptive statistics of the difference between observed and estimated sowing dates of grain maize. all years Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count

33 Histogram (all years) Histogram (98) 6 4 Frequency More Frequency 9 3 More days days Histogram (999) Histogram (2000) 3 2 Frequency More Frequency 9 3 More days days Fig.6. Histogram of the deviation between observed and estimated maize sowing dates for separate years 3

34 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 00 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

35 References Agro-meteorological regional reference book for Belorussia, 98. Leningrad: Hydromet, 230 pp. Agro-meteorological regional reference book for Rostov oblast, 96. Leningrad: Hydromet, 206 pp. Agro-meteorological regional reference book for Ryazan oblast, 966. Moscow: UGSCO, 34 pp. Agro-climatic resources of Lipetsk and Orel oblast, 972. Leningrad: Hydromet, 20 pp. Agro-climatic resources of Kuibyshev oblast, 968. Leningrad: Hydromet, 208 pp. Agro-climatic resources of Novosibirsk oblast, 97. Leningrad: Hydromet, 6 pp. Agro-climatic resources of Kursk oblast, 97. Leningrad: Hydromet, 04 pp. ECMWF web site: Genovese G., Bettio M. (Eds), Methodology of the MCYFS Vol.4 Statistical data collection, processing and analysis EUR 229 EN/4, Luxembourg: Office for Official Publications of the EC, ISBN Lazar C., Genovese G. (Eds), Methodology of the MCYFS Vol.2 Agro-Meteorological data collection, processing and analysis EUR 229 EN/2, Luxembourg: Office for Official Publications of the EC, ISBN Micale F., Genovese G. (Eds), Methodology of the MCYFS Vol. Meteorological data collection, processing and analysis EUR 229 EN/, Luxembourg: Office for Official Publications of the EC, ISBN Narciso G., Ragni P., Venturi A., 992. Agrometeorological aspects of crops in Italy, Spain and Grees. JRC OPOCE, 440 pp. Reference book for agronomist on agro-meteorology, 986. Leningrad: Hydromet, 27 pp. Royer A., Genovese G. (Eds), Methodology of the MCYFS Vol.3 Remote Sensing information, data processing and analysis EUR 229 EN/3, Luxembourg: Office for Official Publications of the EC, ISBN X Savin I., Boogaard H., van Diepen C., Negre T. (Eds), CGMS version 8.0: User manual and technical description, JRC, OPOCE. 29 pp. Supit I., and Goot, E. van der, (Eds) 33