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Authors: 超级管理员 | Edit: mazh
Annex A. Agroclimatic indicators and BIOMSS
We also stress that the reference period, referred to as "average" in this bulletin covers the 15- year period from 2007 to 2021. Although departures from the 2007-2021 are not anomalies (which, strictly, refer to a "normal period" of 30 years), we nevertheless use that terminology. The specific reason why CropWatch refers to the most recent 15 years is our focus on agriculture, as already mentioned in the previous paragraph. 15 years is deemed an acceptable compromise between climatological significance and agricultural significance: agriculture responds much faster to persistent climate variability than 30 years, which is a full generation. For "biological" (agronomic) indicators used in subsequent chapters we adopt an even shorter reference period of 5 years (i.e. 2017-2021) but the BIOMSS indicator is nevertheless compared against the longer 15YA (fifteen- year average). This makes provision for the fast response of markets to changes in supply but also to the fact that in spite of the long warming trend, some recent years (e.g. 2008 or 2010-13) were below the trend.
Correlations between variables (RAIN, TEMP, RADPAR and BIOMSS) at MRU scale derive directly from climatology. For instance, the positive correlation between rainfall and temperature results from high rainfall in equatorial, i.e. in warm areas.
Considering the size of the areas covered in this section, even small departures may have dramatic effects on vegetation and agriculture due to the within-zone spatial variability of weather. It is important to note that we have adopted an improved calculation procedure of the biomass production potential in the bulletin based on previous evaluation. The improved approach includes sunshine (RADPAR), TEMP and RAIN.
Table A.1 January 2022 – April 2022 agroclimatic indicators and biomass by global Monitoring and Reporting Unit (MRU)
65 Global MRUs | RAIN Current (mm) | RAIN 15YA dep. (%) | TEMP Current (°C) | TEMP 15YA dep. (°C) | RADPAR Current(MJ/m2) | RADPAR 15YA dep. (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA dep. (%) | |
C01 | Equatorial central Africa | 661 | -14 | 23.3 | -0.1 | 1225 | 2 | 1148 | -4 |
C02 | East African highlands | 139 | -49 | 20.2 | 0.4 | 1382 | 3 | 543 | -18 |
C03 | Gulf of Guinea | 122 | -13 | 27.1 | -0.1 | 1326 | 1 | 617 | -5 |
C04 | Horn of Africa | 315 | -35 | 22.1 | 0.5 | 1340 | 5 | 799 | -13 |
C05 | Madagascar (main) | 1317 | 11 | 22.4 | 0.1 | 1147 | -2 | 1430 | 2 |
C06 | Southwest Madagascar | 384 | -30 | 25.6 | 0.5 | 1256 | 1 | 1008 | -7 |
C07 | North Africa-Mediterranean | 172 | -19 | 10.3 | -0.4 | 956 | 0 | 452 | -7 |
C08 | Sahel | 29 | 30 | 27.1 | -0.5 | 1369 | 0 | 360 | -9 |
C09 | Southern Africa | 557 | -7 | 22.1 | 0.2 | 1222 | 0 | 969 | -6 |
C10 | Western Cape (South Africa) | 86 | -29 | 19.3 | 0.2 | 1293 | 2 | 542 | -8 |
C11 | British Columbia to Colorado | 324 | -11 | -2.8 | -0.3 | 715 | 1 | 289 | -6 |
C12 | Northern Great Plains | 231 | 0 | -0.6 | -0.9 | 729 | 0 | 331 | -12 |
C13 | Corn Belt | 456 | 8 | -0.3 | -0.6 | 649 | -1 | 373 | -6 |
C14 | Cotton Belt to Mexican Nordeste | 372 | -6 | 11.4 | 0.0 | 909 | 5 | 661 | -4 |
C15 | Sub-boreal America | 271 | 26 | -8.6 | -0.7 | 489 | -8 | 181 | -12 |
C16 | West Coast (North America) | 326 | -34 | 7.3 | 0.0 | 821 | 6 | 406 | -24 |
C17 | Sierra Madre | 50 | -41 | 16.6 | 0.0 | 1326 | 3 | 359 | -15 |
C18 | SW U.S. and N. Mexican highlands | 75 | -39 | 8.9 | -0.3 | 1098 | 3 | 316 | -17 |
C19 | Northern South and Central America | 435 | 3 | 23.2 | -0.1 | 1176 | 1 | 816 | 3 |
C20 | Caribbean | 253 | 26 | 23.7 | 0.3 | 1189 | 3 | 835 | 12 |
C21 | Central-northern Andes | 871 | -12 | 15.3 | 0.0 | 1063 | 2 | 822 | -2 |
C22 | Nordeste (Brazil) | 202 | -51 | 26.6 | 1.1 | 1309 | 4 | 791 | -21 |
C23 | Central eastern Brazil | 467 | -50 | 24.9 | 1.4 | 1234 | 4 | 1003 | -26 |
C24 | Amazon | 1034 | -19 | 24.5 | 0.3 | 1143 | 7 | 1404 | -3 |
C25 | Central-north Argentina | 662 | 25 | 22.7 | -0.6 | 1143 | 0 | 1067 | 3 |
C26 | Pampas | 513 | 5 | 22.4 | 0.1 | 1174 | -1 | 1027 | 3 |
C27 | Western Patagonia | 367 | 44 | 12.9 | -0.6 | 1200 | 0 | 604 | 4 |
C28 | Semi-arid Southern Cone | 271 | 42 | 17.6 | -0.7 | 1283 | -1 | 628 | 6 |
C29 | Caucasus | 303 | -10 | 3.0 | -0.1 | 810 | 1 | 437 | -5 |
C30 | Pamir area | 289 | -31 | 4.9 | 1.8 | 946 | 5 | 415 | -5 |
C31 | Western Asia | 155 | -20 | 8.6 | 1.6 | 899 | 0 | 399 | -8 |
C32 | Gansu-Xinjiang (China) | 113 | 10 | -2.1 | 0.2 | 870 | -2 | 210 | 1 |
C33 | Hainan (China) | 396 | 42 | 21.0 | -0.3 | 925 | -3 | 912 | 19 |
C34 | Huanghuaihai (China) | 92 | -6 | 6.4 | 0.5 | 895 | -2 | 274 | -11 |
C35 | Inner Mongolia (China) | 61 | 10 | -4.7 | 0.0 | 883 | -2 | 193 | 6 |
C36 | Loess region (China) | 99 | 10 | 2.5 | 0.5 | 940 | -4 | 289 | 3 |
C37 | Lower Yangtze (China) | 594 | 19 | 10.7 | 0.3 | 700 | -2 | 797 | 6 |
C38 | Northeast China | 115 | 16 | -5.8 | 0.7 | 763 | -3 | 244 | 15 |
C39 | Qinghai-Tibet (China) | 362 | -3 | 0.4 | 0.0 | 1045 | 0 | 315 | -1 |
C40 | Southern China | 426 | 13 | 15.2 | 0.0 | 865 | 4 | 789 | 7 |
C41 | Southwest China | 401 | 31 | 8.6 | 0.1 | 746 | -6 | 645 | 12 |
C42 | Taiwan (China) | 362 | 25 | 19.3 | 0.0 | 960 | -2 | 773 | 8 |
C43 | East Asia | 287 | -1 | -1.2 | 0.8 | 764 | -2 | 331 | 4 |
C44 | Southern Himalayas | 149 | -12 | 18.7 | 0.1 | 1132 | 1 | 502 | -2 |
C45 | Southern Asia | 64 | -25 | 25.6 | -0.1 | 1306 | 2 | 537 | -2 |
C46 | Southern Japan and the southern fringe of the Korea peninsula | 467 | -5 | 7.3 | 0.9 | 815 | 1 | 627 | -1 |
C47 | Southern Mongolia | 59 | -3 | -11.8 | 0.5 | 802 | -3 | 113 | -8 |
C48 | Punjab to Gujarat | 56 | -12 | 23.5 | 0.9 | 1208 | 1 | 473 | 6 |
C49 | Maritime Southeast Asia | 1292 | -2 | 24.3 | 0.2 | 1158 | 5 | 1444 | 3 |
C50 | Mainland Southeast Asia | 302 | 27 | 24.7 | 0.0 | 1185 | -1 | 824 | 12 |
C51 | Eastern Siberia | 206 | -2 | -8.4 | 1.5 | 558 | -1 | 190 | 3 |
C52 | Eastern Central Asia | 94 | 7 | -11.7 | 0.8 | 693 | -2 | 156 | 2 |
C53 | Northern Australia | 954 | -7 | 26.3 | 0.6 | 1302 | 5 | 1322 | -3 |
C54 | Queensland to Victoria | 325 | 39 | 21.0 | 0.1 | 1143 | -4 | 814 | 14 |
C55 | Nullarbor to Darling | 113 | 6 | 21.3 | 0.2 | 1261 | 2 | 616 | 6 |
C56 | New Zealand | 336 | 13 | 14.8 | 0.2 | 1025 | 1 | 720 | -2 |
C57 | Boreal Eurasia | 322 | 5 | -3.4 | 1.0 | 385 | -1 | 258 | 2 |
C58 | Ukraine to Ural mountains | 293 | 13 | -0.7 | 1.1 | 386 | -12 | 345 | 2 |
C59 | Mediterranean Europe and Turkey | 270 | -26 | 6.6 | -0.5 | 820 | 5 | 517 | -11 |
C60 | W. Europe (non Mediterranean) | 257 | -22 | 4.5 | 0.2 | 600 | 5 | 481 | -6 |
C61 | Boreal America | 388 | 25 | -6.8 | 1.1 | 401 | -9 | 200 | 7 |
C62 | Ural to Altai mountains | 181 | -3 | -4.3 | 2.3 | 529 | -4 | 277 | 12 |
C63 | Australian desert | 146 | 32 | 22.4 | 0.0 | 1269 | -1 | 624 | 4 |
C64 | Sahara to Afghan deserts | 55 | -28 | 17.4 | 0.5 | 1162 | 1 | 358 | -9 |
C65 | Sub-arctic America | 66 | -17 | -22.1 | 0.7 | 306 | -4 | 37 | 4 |
Table A.2 January 2022 – April 2022 agroclimatic indicators and biomass by country
Country code | Country name | RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure(°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) |
ARG | Argentina | 532 | 34 | 21.6 | -0.5 | 1173 | 0 | 962 | 8 |
AUS | Australia | 325 | 18 | 21.7 | 0.2 | 1173 | -2 | 794 | 10 |
BGD | Bangladesh | 83 | -40 | 23.2 | -0.1 | 1188 | 0 | 524 | -10 |
BRA | Brazil | 628 | -38 | 24.7 | 1.1 | 1214 | 4 | 1097 | -19 |
KHM | Cambodia | 410 | 27 | 26.6 | -0.1 | 1192 | 1 | 969 | 14 |
CAN | Canada | 346 | 15 | -6.3 | -0.5 | 523 | -7 | 211 | -8 |
CHN | China | 352 | 19 | 6.7 | 0.3 | 800 | -2 | 460 | 7 |
EGY | Egypt | 49 | -3 | 14.6 | -1.0 | 993 | -2 | 271 | -8 |
ETH | Ethiopia | 94 | -48 | 20.8 | 0.3 | 1389 | 2 | 494 | -17 |
FRA | France | 259 | -29 | 6.2 | 0.4 | 647 | 6 | 528 | -7 |
DEU | Germany | 293 | -2 | 3.8 | 0.2 | 528 | 1 | 499 | 1 |
IND | India | 64 | -26 | 23.4 | 0.0 | 1256 | 1 | 487 | -1 |
IDN | Indonesia | 1323 | -6 | 24.4 | 0.2 | 1169 | 6 | 1479 | 1 |
IRN | Iran | 159 | -25 | 8.7 | 1.0 | 1019 | 2 | 396 | -13 |
KAZ | Kazakhstan | 189 | 6 | -2.5 | 2.3 | 606 | -6 | 323 | 14 |
MEX | Mexico | 123 | 0 | 18.9 | -0.1 | 1257 | 2 | 446 | -8 |
MMR | Myanmar | 122 | -5 | 21.5 | 0.3 | 1210 | -3 | 548 | 1 |
NGA | Nigeria | 101 | -20 | 26.4 | -0.6 | 1358 | 2 | 534 | -2 |
PAK | Pakistan | 185 | -40 | 14.8 | 1.9 | 1088 | 5 | 452 | -5 |
PHL | Philippines | 956 | 48 | 24.6 | 0.0 | 1144 | -1 | 1264 | 12 |
POL | Poland | 262 | 1 | 2.6 | 0.2 | 482 | 1 | 455 | -1 |
ROU | Romania | 179 | -31 | 3.0 | -0.3 | 677 | 5 | 401 | -16 |
RUS | Russia | 246 | 10 | -3.5 | 1.6 | 443 | -9 | 277 | 6 |
ZAF | South Africa | 137 | -40 | 19.6 | 0.1 | 1282 | 2 | 605 | -16 |
THA | Thailand | 364 | 34 | 25.2 | -0.2 | 1180 | 0 | 904 | 17 |
TUR | Turkey | 352 | -5 | 3.2 | -1.0 | 831 | 2 | 491 | -5 |
GBR | United Kingdom | 306 | -21 | 6.1 | 0.8 | 463 | 7 | 535 | -2 |
UKR | Ukraine | 225 | -7 | 1.8 | 0.2 | 502 | -5 | 408 | -6 |
USA | United States | 341 | -5 | 4.8 | -0.3 | 803 | 3 | 436 | -8 |
UZB | Uzbekistan | 228 | -9 | 8.0 | 1.7 | 836 | 0 | 418 | -4 |
VNM | Vietnam | 381 | 24 | 20.7 | -0.1 | 975 | 0 | 871 | 11 |
AFG | Afghanistan | 183 | -41 | 7.5 | 2.2 | 1017 | 5 | 421 | -11 |
AGO | Angola | 698 | -14 | 21.9 | -0.1 | 1198 | 2 | 1213 | -2 |
BLR | Belarus | 300 | 15 | 0.4 | 0.4 | 368 | -11 | 367 | -4 |
HUN | Hungary | 141 | -38 | 4.2 | -0.4 | 671 | 6 | 393 | -21 |
ITA | Italy | 191 | -51 | 6.4 | -0.2 | 790 | 8 | 448 | -22 |
KEN | Kenya | 204 | -55 | 21.6 | 0.6 | 1398 | 5 | 700 | -21 |
LKA | Sri_Lanka | 683 | 22 | 25.3 | 0.0 | 1249 | 0 | 1277 | 14 |
MAR | Morocco | 184 | -15 | 10.7 | -0.2 | 1013 | 0 | 466 | -5 |
MNG | Mongolia | 70 | 1 | -11.7 | 0.5 | 777 | -2 | 154 | 1 |
MOZ | Mozambique | 847 | 9 | 23.6 | 0.2 | 1179 | -2 | 1269 | 0 |
ZMB | Zambia | 919 | -6 | 20.9 | 0.0 | 1166 | 0 | 1242 | 0 |
KGZ | Kyrgyzstan | 351 | 12 | -3.1 | 0.4 | 829 | -1 | 301 | 5 |
Note: Departures are expressed in relative terms (percentage) forall variables, except for temperature, for which absolute departure in degrees Celsius is given. Zero means no change from the average value; relative departures are calculated as (C-R)/R*100, with C=current value and R=reference value, which is the fifteen-year average (15YA) for the same period between Oct- Jan.
Table A.3 Argentina, January 2022 – April 2022 agroclimatic indicators and biomass (by province)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure(°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Buenos Aires | 377 | 55 | 19.8 | -1.0 | 1205 | -1 | 887 | 18 |
Chaco | 473 | -6 | 25.4 | 0.6 | 1115 | -2 | 1018 | -6 |
Cordoba | 443 | 58 | 21.3 | -0.9 | 1227 | 1 | 924 | 10 |
Corrientes | 667 | 29 | 24.8 | 0.9 | 1167 | 0 | 1117 | 4 |
Entre Rios | 744 | 94 | 22.2 | -0.7 | 1165 | -2 | 1090 | 17 |
La Pampa | 211 | 19 | 20.9 | -0.9 | 1278 | 2 | 784 | 12 |
Misiones | 528 | -18 | 23.6 | 0.7 | 1207 | 0 | 1168 | -4 |
Santiago Del Estero | 796 | 66 | 22.9 | -1.1 | 1102 | -1 | 1147 | 11 |
San Luis | 181 | -13 | 20.6 | -0.8 | 1279 | 3 | 718 | -5 |
Salta | 1037 | 12 | 19.9 | -0.3 | 1063 | -1 | 1188 | 2 |
Santa Fe | 593 | 58 | 23.1 | -0.6 | 1151 | -2 | 1058 | 14 |
Tucuman | 975 | 57 | 18.9 | -0.3 | 1101 | -3 | 1079 | 6 |
Table A.4 Australia, January 2022 – April 2022 agroclimatic indicators and biomass (by state)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure(°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
New South Wales | 386 | 64 | 21.3 | -0.3 | 1142 | -7 | 909 | 24 |
South Australia | 122 | 9 | 20.6 | 0.2 | 1174 | -2 | 570 | -1 |
Victoria | 254 | 36 | 19.2 | 0.6 | 1113 | -1 | 745 | 16 |
W. Australia | 194 | 11 | 22.2 | 0.3 | 1267 | 2 | 652 | 4 |
Table A.5 Brazil, January 2022 – April 2022 agroclimatic indicators and biomass (by state)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Ceara | 359 | -42 | 26.9 | 0.9 | 1279 | 3 | 1033 | -16 |
Goias | 288 | -72 | 25.6 | 2.6 | 1277 | 5 | 779 | -46 |
Mato Grosso Do Sul | 295 | -66 | 26.2 | 1.7 | 1172 | -3 | 895 | -35 |
Mato Grosso | 656 | -49 | 25.2 | 1.1 | 1226 | 10 | 1183 | -22 |
Minas Gerais | 477 | -48 | 23.3 | 1.6 | 1261 | 5 | 923 | -28 |
Parana | 551 | -35 | 22.7 | 1.3 | 1160 | -1 | 1136 | -13 |
Rio Grande Do Sul | 560 | 3 | 22.3 | 0.6 | 1158 | -2 | 1117 | 2 |
Santa Catarina | 659 | -13 | 20.2 | 0.5 | 1132 | 1 | 1234 | 2 |
Sao Paulo | 439 | -59 | 24.2 | 1.9 | 1173 | 2 | 935 | -32 |
Table A.6 Canada, January 2022 – April 2022 agroclimatic indicators and biomass (by province)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Alberta | 217 | 20 | -5.5 | -0.1 | 529 | -5 | 244 | -3 |
Manitoba | 298 | 56 | -9.3 | -2.1 | 519 | -10 | 170 | -26 |
Saskatchewan | 212 | 23 | -7.3 | -1.0 | 533 | -7 | 212 | -15 |
Table A.7 India, January 2022 – April 2022 agroclimatic indicators and biomass (by state)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Andhra Pradesh | 25 | -39 | 26.6 | -0.2 | 1337 | 2 | 506 | -2 |
Assam | 335 | -17 | 17.8 | -0.9 | 1034 | -1 | 690 | -4 |
Bihar | 29 | -34 | 22.5 | -0.2 | 1219 | 2 | 433 | -7 |
Chhattisgarh | 5 | -83 | 24.0 | -0.3 | 1317 | 4 | 435 | -7 |
Daman and Diu | 2 | 39 | 26.5 | 0.1 | 1359 | -1 | 470 | 44 |
Delhi | 96 | 67 | 20.4 | -0.2 | 1124 | -1 | 483 | 10 |
Gujarat | 7 | 125 | 26.3 | 0.1 | 1320 | 0 | 467 | 16 |
Goa | 5 | -61 | 26.7 | 0.0 | 1361 | -2 | 478 | -4 |
Himachal Pradesh | 246 | -24 | 11.5 | 1.2 | 1096 | 5 | 481 | -4 |
Haryana | 108 | 59 | 20.5 | 0.3 | 1115 | 0 | 495 | 12 |
Jharkhand | 13 | -65 | 22.9 | 0.0 | 1269 | 4 | 421 | -8 |
Kerala | 213 | -29 | 25.9 | -0.1 | 1303 | -1 | 787 | -7 |
Karnataka | 27 | -58 | 26.0 | 0.0 | 1343 | 1 | 500 | -8 |
Meghalaya | 257 | -13 | 18.3 | -0.8 | 1083 | -1 | 682 | 3 |
Maharashtra | 4 | -65 | 26.7 | 0.0 | 1345 | 1 | 463 | -1 |
Manipur | 176 | -33 | 15.2 | -0.3 | 1165 | 1 | 508 | -17 |
Madhya Pradesh | 5 | -70 | 24.1 | 0.2 | 1284 | 3 | 426 | -3 |
Mizoram | 136 | -26 | 17.5 | -0.8 | 1208 | -1 | 496 | -13 |
Nagaland | 360 | -20 | 13.2 | -1.5 | 1063 | -1 | 697 | -5 |
Orissa | 15 | -60 | 24.1 | -0.3 | 1300 | 5 | 454 | -6 |
Puducherry | 114 | -5 | 27.2 | 0.1 | 1378 | 0 | 718 | 6 |
Punjab | 174 | 22 | 19.9 | 0.8 | 1067 | 2 | 537 | 4 |
Rajasthan | 29 | 63 | 23.9 | 0.9 | 1218 | 0 | 454 | 10 |
Sikkim | 62 | -18 | 12.4 | 2.5 | 1239 | -1 | 319 | 4 |
Tamil Nadu | 129 | -40 | 26.2 | 0.4 | 1313 | -1 | 683 | -6 |
Tripura | 165 | -32 | 21.5 | -0.5 | 1166 | 0 | 598 | -9 |
Uttarakhand | 129 | 1 | 14.2 | 0.9 | 1158 | 2 | 439 | 6 |
Uttar Pradesh | 42 | -9 | 21.8 | -0.2 | 1189 | 1 | 434 | -4 |
West Bengal | 40 | -42 | 23.8 | 0.0 | 1226 | 2 | 477 | -7 |
Table A.8 Kazakhstan, January 2022 – April 2022 agroclimatic indicators and biomass (by oblast)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Akmolinskaya | 136 | -8 | -3.9 | 2.8 | 567 | -6 | 299 | 16 |
Karagandinskaya | 115 | -9 | -4.1 | 2.5 | 668 | -3 | 299 | 14 |
Kustanayskaya | 178 | 8 | -3.3 | 3.1 | 491 | -12 | 310 | 19 |
Pavlodarskaya | 101 | -15 | -4.5 | 2.3 | 597 | 1 | 280 | 10 |
Severo kazachstanskaya | 124 | -22 | -4.3 | 2.7 | 507 | -2 | 282 | 17 |
Vostochno kazachstanskaya | 171 | -6 | -4.5 | 1.6 | 716 | 1 | 273 | 6 |
Zapadno kazachstanskaya | 278 | 37 | 0.3 | 3.1 | 436 | -24 | 397 | 18 |
Table A.9 Russia, January 2022 – April 2022 agroclimatic indicators and biomass (by oblast, kray and republic)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Bashkortostan Rep. | 268 | 10 | -4.0 | 2.4 | 392 | -14 | 272 | 13 |
Chelyabinskaya Oblast | 173 | 1 | -4.7 | 2.2 | 439 | -11 | 266 | 13 |
Gorodovikovsk | 213 | -9 | 3.7 | 0.9 | 594 | 0 | 515 | 8 |
Krasnodarskiy Kray | 255 | 0 | -1.5 | 0.7 | 565 | 0 | 340 | 2 |
Kurganskaya Oblast | 167 | -4 | -4.7 | 2.4 | 411 | -8 | 262 | 13 |
Kirovskaya Oblast | 290 | 1 | -4.2 | 1.8 | 284 | -16 | 244 | 6 |
Kurskaya Oblast | 333 | 27 | -0.2 | 0.8 | 351 | -21 | 359 | 0 |
Lipetskaya Oblast | 329 | 29 | -0.6 | 1.4 | 356 | -20 | 349 | 5 |
Mordoviya Rep. | 332 | 26 | -1.7 | 2.0 | 318 | -24 | 313 | 8 |
Novosibirskaya Oblast | 154 | -20 | -5.5 | 2.7 | 447 | 0 | 243 | 11 |
Nizhegorodskaya O. | 298 | 10 | -2.6 | 1.7 | 293 | -23 | 286 | 5 |
Orenburgskaya Oblast | 254 | 10 | -2.3 | 2.9 | 429 | -19 | 322 | 17 |
Omskaya Oblast | 165 | -12 | -4.9 | 3.0 | 428 | 0 | 257 | 16 |
Permskaya Oblast | 296 | 7 | -4.6 | 2.3 | 293 | -16 | 240 | 11 |
Penzenskaya Oblast | 354 | 33 | -1.1 | 2.4 | 338 | -23 | 336 | 13 |
Rostovskaya Oblast | 258 | 4 | 2.7 | 1.4 | 552 | -2 | 477 | 9 |
Ryazanskaya Oblast | 339 | 26 | -1.2 | 1.5 | 314 | -24 | 323 | 3 |
Stavropolskiy Kray | 206 | -20 | 3.2 | 0.5 | 639 | 2 | 460 | -2 |
Sverdlovskaya Oblast | 217 | 3 | -5.2 | 2.1 | 344 | -11 | 235 | 8 |
Samarskaya Oblast | 335 | 35 | -1.6 | 2.9 | 348 | -26 | 329 | 16 |
Saratovskaya Oblast | 364 | 49 | -0.1 | 2.8 | 385 | -24 | 375 | 16 |
Tambovskaya Oblast | 355 | 34 | -0.5 | 1.9 | 364 | -20 | 356 | 9 |
Tyumenskaya Oblast | 190 | -3 | -5.0 | 2.6 | 386 | -2 | 249 | 14 |
Tatarstan Rep. | 329 | 27 | -2.9 | 2.3 | 305 | -23 | 283 | 11 |
Ulyanovskaya Oblast | 336 | 37 | -1.7 | 2.5 | 327 | -25 | 319 | 14 |
Udmurtiya Rep. | 314 | 12 | -4.0 | 2.2 | 283 | -20 | 252 | 10 |
Volgogradskaya O. | 305 | 35 | 1.2 | 2.2 | 479 | -12 | 426 | 13 |
Voronezhskaya Oblast | 310 | 22 | 0.3 | 1.6 | 434 | -14 | 389 | 8 |
Table A.10 United States, January 2022 – April 2022 agroclimatic indicators and biomass (by state)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Arkansas | 567 | 7 | 8.7 | -0.4 | 814 | 5 | 756 | -1 |
California | 126 | -66 | 9.6 | 0.5 | 989 | 9 | 354 | -33 |
Idaho | 283 | -22 | -1.8 | -1.1 | 720 | 1 | 323 | -11 |
Indiana | 445 | -5 | 2.7 | -0.5 | 698 | 2 | 497 | -5 |
Illinois | 456 | 8 | 2.2 | -1.0 | 699 | 0 | 483 | -8 |
Iowa | 284 | -8 | -1.2 | -1.4 | 684 | -1 | 373 | -13 |
Kansas | 158 | -27 | 5.8 | -0.1 | 907 | 5 | 404 | -17 |
Michigan | 369 | 4 | -2.5 | -1.0 | 561 | -8 | 305 | -11 |
Minnesota | 329 | 28 | -6.2 | -2.3 | 558 | -11 | 232 | -24 |
Missouri | 441 | 9 | 4.5 | -0.7 | 777 | 4 | 572 | -4 |
Montana | 242 | 4 | -2.7 | -0.8 | 702 | -1 | 314 | -7 |
Nebraska | 138 | -34 | 2.2 | -0.1 | 840 | 3 | 372 | -17 |
North Dakota | 282 | 52 | -5.5 | -1.9 | 627 | -6 | 252 | -20 |
Ohio | 422 | -5 | 2.4 | -0.2 | 696 | 4 | 484 | -2 |
Oklahoma | 276 | -8 | 8.8 | -0.4 | 903 | 5 | 533 | -10 |
Oregon | 384 | -21 | 2.7 | -0.7 | 683 | 4 | 431 | -7 |
South Dakota | 199 | -7 | -1.7 | -1.0 | 731 | -1 | 348 | -8 |
Texas | 183 | -30 | 12.9 | -0.6 | 966 | 5 | 468 | -18 |
Washington | 502 | 1 | 2.1 | -0.8 | 566 | -3 | 429 | -4 |
Wisconsin | 365 | 18 | -4.4 | -1.6 | 574 | -9 | 271 | -16 |
Table A.11 China, January 2022 – April 2022 agroclimatic indicators and biomass (by province)
RAIN Current (mm) | RAIN 15YA Departure (%) | TEMP Current (°C) | TEMP 15YA Departure (°C) | RADPAR Current (MJ/m2) | RADPAR 15YA Departure (%) | BIOMSS Current (gDM/m2) | BIOMSS 15YA Departure (%) | |
Anhui | 481 | 45 | 9.1 | 0.7 | 769 | -5 | 655 | 8 |
Chongqing | 460 | 30 | 9.3 | 0.2 | 702 | -2 | 718 | 13 |
Fujian | 610 | 1 | 12.7 | 0.6 | 747 | 4 | 878 | 5 |
Gansu | 179 | 36 | 0.6 | 0.2 | 916 | -6 | 331 | 12 |
Guangdong | 586 | 8 | 16.0 | 0.1 | 785 | 10 | 911 | 4 |
Guangxi | 510 | 13 | 14.0 | -0.2 | 646 | 4 | 847 | 6 |
Guizhou | 421 | 4 | 9.0 | -0.2 | 578 | -7 | 721 | 3 |
Hebei | 52 | -1 | 0.6 | -0.3 | 904 | -3 | 207 | 2 |
Heilongjiang | 124 | 19 | -7.1 | 1.2 | 709 | -5 | 240 | 15 |
Henan | 199 | 39 | 8.0 | 0.6 | 854 | -5 | 411 | 5 |
Hubei | 494 | 45 | 8.7 | 0.4 | 738 | -6 | 710 | 13 |
Hunan | 623 | 19 | 10.0 | 0.1 | 632 | -2 | 790 | 3 |
Jiangsu | 286 | 24 | 8.8 | 0.9 | 854 | 0 | 570 | 5 |
Jiangxi | 713 | 17 | 11.0 | 0.2 | 648 | -5 | 843 | 2 |
Jilin | 111 | 6 | -5.1 | 0.4 | 808 | -2 | 260 | 16 |
Liaoning | 87 | 8 | -2.1 | -0.3 | 866 | -1 | 247 | 10 |
Inner Mongolia | 72 | 22 | -6.5 | 0.3 | 841 | -2 | 189 | 10 |
Ningxia | 77 | 13 | 0.4 | -0.1 | 979 | -2 | 243 | 4 |
Shaanxi | 166 | 23 | 4.8 | 0.7 | 893 | -4 | 366 | 11 |
Shandong | 48 | -45 | 6.3 | 0.6 | 918 | -1 | 227 | -24 |
Shanxi | 54 | -19 | 1.4 | 0.5 | 930 | -3 | 217 | -10 |
Sichuan | 446 | 55 | 7.2 | 0.3 | 802 | -7 | 590 | 14 |
Yunnan | 270 | 22 | 11.1 | -0.2 | 998 | -4 | 605 | 13 |
Zhejiang | 594 | 16 | 9.7 | 0.6 | 709 | -4 | 795 | 5 |