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Focus and perspectives
Annex A. Agroclimatic indicators and BIOMSSAnnex
Authors: 超级管理员 | Edit: Changsheng
Table A.1. April 2019 - July 2019 agroclimatic indicators and biomass by global Monitoring and Reporting Unit (MRU)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| MRU01 | Equatorial central Africa | 534 | -11 | 22.4 | -0.1 | 1166 | 3 | 592 | 3 |
| MRU02 | East African highlands | 755 | -3 | 18.6 | 0.0 | 1185 | -1 | 539 | 1 |
| MRU03 | Gulf of Guinea | 567 | -10 | 27.0 | 0.0 | 1189 | 2 | 765 | 2 |
| MRU04 | Horn of Africa | 254 | 26 | 21.6 | 0.2 | 1142 | -1 | 584 | 1 |
| MRU05 | Madagascar (main) | 279 | 17 | 19.7 | 0.4 | 935 | 1 | 481 | 9 |
| MRU06 | Southwest Madagascar | 50 | -24 | 21.8 | 0.4 | 993 | 3 | 525 | 27 |
| MRU07 | North Africa-Mediterranean | 87 | -12 | 20.3 | -0.4 | 1587 | 2 | 622 | -2 |
| MRU08 | Sahel | 261 | -1 | 30.4 | -0.1 | 1316 | 1 | 701 | 9 |
| MRU09 | Southern Africa | 112 | 0 | 17.7 | 0.2 | 1023 | 3 | 338 | -3 |
| MRU10 | Western Cape (South Africa) | 177 | -23 | 13.3 | 0.1 | 693 | 4 | 249 | 4 |
| MRU11 | British Columbia to Colorado | 380 | 17 | 9.2 | -0.5 | 1345 | -2 | 405 | -4 |
| MRU12 | Northern Great Plains | 456 | 33 | 15.9 | -1.3 | 1303 | -3 | 574 | -8 |
| MRU13 | Corn Belt | 508 | 17 | 15.4 | -0.5 | 1199 | -3 | 516 | -3 |
| MRU14 | Cotton Belt to Mexican Nordeste | 547 | 30 | 23.1 | -0.2 | 1394 | 0 | 845 | 3 |
| MRU15 | Sub-boreal America | 302 | -16 | 10.1 | -0.4 | 1189 | 3 | 382 | 0 |
| MRU16 | West Coast (North America) | 219 | 20 | 15.4 | -0.1 | 1454 | -2 | 473 | -2 |
| MRU17 | Sierra Madre | 690 | -2 | 19.9 | -0.3 | 1518 | 4 | 580 | -5 |
| MRU18 | SW U.S. and N. Mexican highlands | 182 | 12 | 19.7 | -0.6 | 1573 | -1 | 644 | 4 |
| MRU19 | Northern South and Central America | 900 | -18 | 25.0 | 0.3 | 1261 | 3 | 778 | 3 |
| MRU20 | Caribbean | 461 | -27 | 25.7 | 0.1 | 1424 | 4 | 945 | 5 |
| MRU21 | Central-northern Andes | 484 | -7 | 12.8 | 0.2 | 1050 | 0 | 306 | 2 |
| MRU22 | Nordeste (Brazil) | 227 | 4 | 24.1 | 0.3 | 1084 | 5 | 667 | 7 |
| MRU23 | Central eastern Brazil | 287 | 2 | 21.4 | 0.4 | 984 | 4 | 484 | 4 |
| MRU24 | Amazon | 607 | -6 | 24.4 | 0.4 | 1086 | 3 | 622 | 4 |
| MRU25 | Central-north Argentina | 238 | 43 | 15.2 | -0.4 | 654 | -9 | 257 | -8 |
| MRU26 | Pampas | 433 | 6 | 15.0 | 0.5 | 585 | -6 | 231 | -2 |
| MRU27 | Western Patagonia | 639 | -23 | 7.1 | 0.0 | 477 | 3 | 112 | 2 |
| MRU28 | Semi-arid Southern Cone | 83 | -27 | 10.5 | 0.4 | 705 | 1 | 161 | -4 |
| MRU29 | Caucasus | 318 | 1 | 15.9 | 0.2 | 1464 | 1 | 536 | 3 |
| MRU30 | Pamir area | 460 | 44 | 16.1 | -0.8 | 1517 | -2 | 516 | 0 |
| MRU31 | Western Asia | 127 | 50 | 22.8 | -0.3 | 1541 | -1 | 522 | 7 |
| MRU32 | Gansu-Xinjiang (China) | 221 | 6 | 16.7 | -0.3 | 1412 | -2 | 639 | -2 |
| MRU33 | Hainan (China) | 804 | -14 | 27.3 | 0.9 | 1412 | 10 | 943 | 9 |
| MRU34 | Huanghuaihai (China) | 172 | -50 | 22.9 | 0.5 | 1347 | 2 | 745 | 1 |
| MRU35 | Inner Mongolia (China) | 207 | -1 | 16.5 | 0.3 | 1399 | 2 | 608 | 2 |
| MRU36 | Loess region (China) | 231 | -15 | 17.3 | -0.2 | 1335 | -2 | 610 | -1 |
| MRU37 | Lower Yangtze (China) | 1096 | -1 | 21.8 | -0.1 | 1062 | -2 | 620 | -3 |
| MRU38 | Northeast China | 288 | -14 | 15.5 | 0.1 | 1295 | 3 | 543 | 1 |
| MRU39 | Qinghai-Tibet (China) | 985 | -8 | 10.2 | 0.3 | 1175 | -1 | 339 | 2 |
| MRU40 | Southern China | 1192 | -13 | 23.4 | 0.6 | 1128 | 2 | 682 | 3 |
| MRU41 | Southwest China | 809 | -6 | 18.8 | 0.1 | 1011 | -7 | 503 | -7 |
| MRU42 | Taiwan (China) | 834 | -23 | 25.3 | 0.5 | 1192 | -3 | 760 | 0 |
| MRU43 | East Asia | 437 | -22 | 14.2 | 0.1 | 1220 | 4 | 459 | 0 |
| MRU44 | Southern Himalayas | 831 | -17 | 26.9 | 0.4 | 1285 | 3 | 726 | 9 |
| MRU45 | Southern Asia | 675 | -10 | 29.9 | 0.5 | 1254 | 3 | 705 | 4 |
| MRU46 | Southern Japan and the southern fringe of the Korea peninsula | 781 | -9 | 17.7 | -0.3 | 1177 | 1 | 548 | -2 |
| MRU47 | Southern Mongolia | 84 | 5 | 15.4 | 0.0 | 1547 | 1 | 642 | 9 |
| MRU48 | Punjab to Gujarat | 305 | -12 | 32.7 | 0.0 | 1426 | 0 | 671 | 11 |
| MRU49 | Maritime Southeast Asia | 1145 | -9 | 24.1 | 0.1 | 1120 | 4 | 719 | 4 |
| MRU50 | Mainland Southeast Asia | 907 | -20 | 27.2 | 0.6 | 1258 | 8 | 810 | 6 |
| MRU51 | Eastern Siberia | 273 | -15 | 9.9 | 0.2 | 1130 | 2 | 363 | 0 |
| MRU52 | Eastern Central Asia | 263 | 5 | 10.8 | -0.1 | 1306 | 1 | 431 | 0 |
| MRU53 | Northern Australia | 258 | -31 | 22.6 | -0.2 | 1028 | 0 | 513 | -6 |
| MRU54 | Queensland to Victoria | 129 | -34 | 13.1 | 0.6 | 669 | 4 | 217 | -1 |
| MRU55 | Nullarbor to Darling | 192 | -15 | 13.4 | -0.5 | 649 | 5 | 239 | 2 |
| MRU56 | New Zealand | 261 | -32 | 9.0 | 0.3 | 460 | 7 | 125 | 7 |
| MRU57 | Boreal Eurasia | 264 | -22 | 9.7 | -0.1 | 1086 | 2 | 337 | 0 |
| MRU58 | Ukraine to Ural mountains | 281 | -10 | 14.2 | 0.1 | 1171 | 2 | 482 | 3 |
| MRU59 | Mediterranean Europe and Turkey | 226 | 10 | 16.7 | -0.4 | 1454 | 0 | 599 | 2 |
| MRU60 | W. Europe (non Mediterranean) | 341 | -5 | 14.5 | 0.0 | 1235 | 2 | 517 | 6 |
| MRU61 | Boreal America | 282 | -20 | 7.7 | 1.5 | 1050 | 6 | 295 | 18 |
| MRU62 | Ural to Altai mountains | 252 | -7 | 12.6 | -0.8 | 1223 | 2 | 461 | -4 |
| MRU63 | Australian desert | 86 | -27 | 14.4 | 0.0 | 719 | 6 | 261 | 2 |
| MRU64 | Sahara to Afghan deserts | 47 | 89 | 28.6 | -0.1 | 1606 | -1 | 455 | 30 |
| MRU65 | Sub-arctic America | 113 | -7 | -3.2 | 1.0 | 1218 | -1 | 183 | 9 |
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 April and July.
Table A.2. April 2019 – July 2019 agroclimatic indicators and biomass by country
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| ARG | Argentina | 296 | 20 | 13.5 | 0.0 | 572 | -9 | 206 | -5 |
| AUS | Australia | 138 | -29 | 13.9 | 0.4 | 691 | 3 | 231 | -1 |
| BGD | Bangladesh | 1263 | -15 | 28.8 | 0.0 | 1298 | 4 | 867 | 4 |
| BRA | Brazil | 387 | -2 | 22.1 | 0.5 | 1004 | 3 | 524 | 4 |
| KHM | Cambodia | 825 | -19 | 27.1 | 0.2 | 1222 | 7 | 834 | 8 |
| CAN | Canada | 352 | -9 | 10.4 | -0.4 | 1186 | 1 | 382 | -1 |
| CHN | China | 744 | -8 | 19.6 | 0.1 | 1160 | -1 | 591 | -1 |
| EGY | Egypt | 7 | 12 | 23.8 | 0.3 | 1573 | -1 | 251 | -13 |
| ETH | Ethiopia | 733 | -5 | 19.4 | 0.0 | 1232 | 0 | 569 | 3 |
| FRA | France | 334 | -11 | 14.7 | 0.0 | 1295 | 5 | 542 | 9 |
| DEU | Germany | 283 | -18 | 14.1 | 0.2 | 1221 | 4 | 494 | 8 |
| IND | India | 644 | -13 | 30.2 | 0.4 | 1293 | 3 | 688 | 7 |
| IDN | Indonesia | 1056 | -11 | 24.0 | 0.0 | 1094 | 4 | 693 | 3 |
| IRN | Iran | 135 | 63 | 21.1 | -0.2 | 1609 | -1 | 473 | 12 |
| KAZ | Kazakhstan | 226 | 3 | 15.0 | -0.7 | 1342 | 2 | 542 | -4 |
| MEX | Mexico | 523 | -13 | 23.4 | 0.1 | 1512 | 3 | 695 | -2 |
| MMR | Myanmar | 1020 | -23 | 26.1 | 0.8 | 1225 | 7 | 717 | 2 |
| NGA | Nigeria | 545 | -12 | 27.8 | 0.1 | 1194 | 0 | 727 | 1 |
| PAK | Pakistan | 254 | 53 | 28.2 | -0.7 | 1555 | -1 | 737 | 18 |
| PHL | Philippines | 1314 | -9 | 25.5 | 0.1 | 1289 | 4 | 854 | 4 |
| POL | Poland | 251 | -22 | 15.1 | 0.6 | 1199 | 5 | 517 | 11 |
| ROU | Romania | 404 | 8 | 15.9 | -0.1 | 1298 | 0 | 590 | 2 |
| RUS | Russia | 272 | -13 | 13.0 | -0.2 | 1175 | 2 | 450 | -1 |
| ZAF | South Africa | 64 | -30 | 13.3 | 0.4 | 908 | 4 | 232 | -10 |
| THA | Thailand | 694 | -23 | 27.5 | 0.7 | 1257 | 8 | 836 | 8 |
| TUR | Turkey | 266 | 17 | 15.9 | -0.2 | 1479 | 0 | 540 | 0 |
| GBR | United Kingdom | 349 | -10 | 11.3 | -0.1 | 976 | 2 | 318 | 2 |
| UKR | Ukraine | 338 | 15 | 16.4 | 0.3 | 1243 | 1 | 584 | 6 |
| USA | United States | 481 | 29 | 18.3 | -0.5 | 1335 | -2 | 625 | -1 |
| UZB | Uzbekistan | 208 | 105 | 22.4 | -0.5 | 1522 | -3 | 576 | 3 |
| VNM | Vietnam | 926 | -18 | 25.6 | 0.6 | 1253 | 7 | 822 | 8 |
| AFG | Afghanistan | 248 | 67 | 18.6 | -0.6 | 1578 | -2 | 405 | 8 |
| AGO | Angola | 192 | 15 | 19.7 | 0.1 | 1218 | 1 | 326 | 9 |
| BLR | Belarus | 305 | -3 | 14.6 | 0.6 | 1155 | 5 | 478 | 9 |
| HUN | Hungary | 351 | 35 | 17.1 | -0.3 | 1289 | -1 | 628 | 1 |
| ITA | Italy | 405 | 13 | 17.0 | -0.3 | 1366 | -3 | 644 | 4 |
| KEN | Kenya | 584 | 5 | 19.7 | 0.2 | 1115 | 0 | 572 | 0 |
| LKA | Sri_Lanka | 689 | -20 | 27.2 | 0.6 | 1255 | 0 | 821 | 1 |
| MAR | Morocco | 74 | -16 | 19.4 | -0.9 | 1634 | 3 | 551 | -13 |
| MNG | Mongolia | 291 | 28 | 10.4 | -0.2 | 1366 | -1 | 430 | -3 |
| MOZ | Mozambique | 135 | -2 | 19.9 | -0.3 | 965 | 1 | 492 | -1 |
| ZMB | Zambia | 109 | 69 | 18.3 | 0.1 | 1138 | 1 | 319 | -10 |
See note table A.1.
Table A.3. Argentina, April 2019 - July 2019 agroclimatic indicators and biomass (by province)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| ARG_BAR | Buenos Aires | 177 | -19 | 11.9 | 0.4 | 568 | 1 | 188 | 5 |
| ARG_CAC | Chaco | 462 | 51 | 16.1 | -0.6 | 473 | -24 | 202 | -23 |
| ARG_CDB | Cordoba | 130 | 7 | 12.8 | -0.3 | 646 | -5 | 222 | 0 |
| ARG_CRT | Corrientes | 645 | 38 | 16.1 | 0.3 | 479 | -19 | 207 | -17 |
| ARG_ERS | Entre Rios | 451 | 31 | 13.9 | 0.0 | 508 | -13 | 195 | -8 |
| ARG_LPP | La Pampa | 77 | -43 | 11.9 | 0.4 | 607 | 2 | 205 | 15 |
| ARG_MSS | Misiones | 621 | -1 | 17.1 | 0.8 | 602 | -7 | 274 | -2 |
| ARG_SDE | Santiago Del Estero | 262 | 60 | 15.1 | -0.7 | 575 | -15 | 220 | -16 |
| ARG_SLS | San Luis | 64 | -29 | 11.6 | 0.1 | 699 | 2 | 227 | 15 |
| ARG_SLT | Salta | 268 | 47 | 14.0 | -0.2 | 723 | -10 | 257 | -9 |
| ARG_STF | Santa Fe | 348 | 41 | 14.3 | -0.4 | 511 | -17 | 197 | -14 |
| ARG_TCM | Tucuman | 150 | 41 | 12.5 | 0.4 | 795 | -6 | 268 | 4 |
See note table A.1.
Table A.4. Australia, April 2019 - July 2019 agroclimatic indicators and biomass (by state)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| AUS_NSW | New South Wales | 89 | -49 | 12.8 | 0.7 | 710 | 6 | 207 | -6 |
| AUS_SOU | South Australia | 166 | -18 | 13.8 | 0.3 | 564 | 3 | 223 | 8 |
| AUS_VCT | Victoria | 228 | -13 | 11.1 | 0.3 | 483 | 3 | 162 | 4 |
| AUS_WES | W. Australia | 181 | -16 | 14.2 | -0.5 | 683 | 5 | 241 | 1 |
See note table A.1.
Table A.5. Brazil, April 2019 - July 2019 agroclimatic indicators and biomass (by state)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| BRA_CEA | Ceara | 421 | 8 | 25.0 | -0.2 | 1166 | 2 | 771 | 2 |
| BRA_GOS | Goias | 228 | 16 | 21.7 | 0.3 | 1088 | 3 | 440 | -4 |
| BRA_MGD | Mato Grosso Do Sul | 237 | -12 | 21.1 | 0.4 | 880 | 5 | 449 | 6 |
| BRA_MGO | Mato Grosso | 247 | 3 | 23.7 | 0.4 | 1100 | 3 | 473 | -1 |
| BRA_MGS | Minas Gerais | 225 | 4 | 19.4 | 0.5 | 957 | 5 | 471 | 8 |
| BRA_PAA | Parana | 421 | -21 | 17.4 | 0.9 | 760 | 3 | 345 | 9 |
| BRA_RGD | Rio Grande Do Sul | 619 | 7 | 15.9 | 1.1 | 588 | -6 | 248 | -2 |
| BRA_SCA | Santa Catarina | 536 | -9 | 15.6 | 1.2 | 658 | -2 | 272 | 4 |
| BRA_SPL | Sao Paulo | 322 | 1 | 19.1 | 0.6 | 863 | 5 | 400 | 4 |
See note table A.1.
Table A.6. Canada, April2019 - July 2019 agroclimatic indicators and biomass(by province)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| CAN_ABT | Alberta | 304 | -13 | 10.8 | -0.3 | 1249 | 0 | 409 | -4 |
| CAN_MTB | Manitoba | 272 | -26 | 11.9 | -0.1 | 1277 | 8 | 459 | 6 |
| CAN_SKC | Saskatchewan | 252 | -24 | 11.8 | -0.2 | 1269 | 4 | 447 | 0 |
See note table A.1.
Table A.7. India, April 2019 - July 2019 agroclimatic indicators and biomass (by state)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| IND_ADP | Andhra Pradesh | 535 | 19 | 31.3 | 0.4 | 1213 | 2 | 737 | 8 |
| IND_ASM | Assam | 2156 | -1 | 25.1 | -0.4 | 1095 | -1 | 720 | -1 |
| IND_BIH | Bihar | 711 | 3 | 32.4 | 0.4 | 1343 | 1 | 873 | 17 |
| IND_CTG | Chhattisgarh | 592 | -12 | 30.9 | 0.4 | 1239 | 2 | 715 | 8 |
| IND_DAD | Daman and Diu | 875 | -27 | 29.5 | 0.5 | 1439 | 1 | 375 | -26 |
| IND_DLH | Delhi | 102 | -61 | 33.7 | 0.3 | 1475 | 2 | 918 | 22 |
| IND_GJR | Gujarat | 460 | -25 | 31.5 | 0.3 | 1390 | 2 | 429 | -7 |
| IND_GOA | Goa | 1980 | -14 | 27.1 | 0.0 | 1332 | 2 | 685 | 1 |
| IND_HCP | Himachal Pradesh | 497 | -18 | 19.0 | -0.6 | 1440 | -1 | 619 | 7 |
| IND_HYN | Haryana | 104 | -52 | 33.6 | 0.3 | 1480 | 2 | 822 | 12 |
| IND_JKH | Jharkhand | 549 | -15 | 30.9 | 0.4 | 1293 | 3 | 812 | 19 |
| IND_KRL | Kerala | 1499 | -9 | 26.3 | 0.5 | 1226 | 5 | 817 | 5 |
| IND_KRT | Karnataka | 518 | -26 | 27.4 | 0.8 | 1147 | 4 | 711 | 7 |
| IND_MHL | Meghalaya | 1930 | -15 | 24.4 | 0.4 | 1135 | 7 | 722 | 9 |
| IND_MHT | Maharashtra | 711 | -5 | 30.1 | 0.6 | 1298 | 6 | 566 | -5 |
| IND_MIP | Manipur | 1313 | -27 | 21.9 | 0.1 | 1174 | 6 | 683 | 7 |
| IND_MYP | Madhya Pradesh | 451 | -26 | 32.0 | 0.8 | 1327 | 4 | 569 | 3 |
| IND_MZR | Mizoram | 1234 | -15 | 23.9 | 0.2 | 1283 | 5 | 800 | 6 |
| IND_NGL | Nagaland | 1527 | -24 | 20.7 | -0.3 | 1145 | 4 | 622 | 3 |
| IND_ORS | Orissa | 691 | -7 | 30.4 | 0.2 | 1211 | 0 | 790 | 11 |
| IND_PDC | Puducherry | 143 | -57 | 31.5 | 0.4 | 1344 | 4 | 740 | -8 |
| IND_PJB | Punjab | 279 | 14 | 32.4 | -0.6 | 1450 | 1 | 892 | 11 |
| IND_RJT | Rajasthan | 221 | -20 | 33.6 | 0.4 | 1400 | 0 | 646 | 13 |
| IND_SKM | Sikkim | 546 | -19 | 15.5 | -0.1 | 1374 | 4 | 509 | 8 |
| IND_TND | Tamil Nadu | 307 | -21 | 29.7 | 0.7 | 1246 | 3 | 777 | 2 |
| IND_TPR | Tripura | 1099 | -32 | 27.6 | 0.1 | 1334 | 9 | 911 | 10 |
| IND_UTK | Uttarakhand | 299 | -48 | 22.7 | 0.1 | 1481 | 5 | 645 | 7 |
| IND_UTP | Uttar Pradesh | 350 | -27 | 33.7 | 0.6 | 1404 | 3 | 782 | 14 |
| IND_WBG | West Bengal | 921 | -12 | 30.5 | 0.0 | 1321 | 3 | 873 | 10 |
See note table A.1.
Table A.8. Kazakhstan, April 2019 - July 2019 agroclimatic indicators and biomass (by oblast)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| KAZ_ALS | Akmolinskaya | 187 | 0 | 13.9 | -1.2 | 1296 | 4 | 453 | -15 |
| KAZ_KGS | Karagandinskaya | 177 | 1 | 13.5 | -1.0 | 1383 | 4 | 477 | -11 |
| KAZ_KTS | Kustanayskaya | 161 | -21 | 15.2 | 0.0 | 1279 | 4 | 565 | 4 |
| KAZ_PLS | Pavlodarskaya | 194 | -5 | 13.6 | -1.7 | 1286 | 3 | 496 | -8 |
| KAZ_SKS | Severo kazachstanskaya | 233 | -7 | 12.7 | -1.1 | 1207 | 4 | 437 | -7 |
| KAZ_VKS | Vostochno kazachstanskaya | 256 | -3 | 13.1 | -1.0 | 1397 | 1 | 501 | -10 |
| KAZ_ZKS | Zapadno kazachstanskaya | 172 | 4 | 18.0 | 0.0 | 1361 | 4 | 682 | 7 |
See note table A.1.
Table A.9. Russia, April 2019 - July 2019 agroclimatic indicators and biomass(by oblast, kray and republic)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| RUS_BKR | Bashkortostan Rep. | 286 | -15 | 12.4 | -0.4 | 1175 | 3 | 432 | -3 |
| RUS_CBO | Chelyabinskaya Oblast | 213 | -26 | 12.9 | -0.1 | 1181 | 4 | 462 | 3 |
| RUS_GRD | Gorodovikovsk | 301 | 12 | 19.1 | 0.5 | 1278 | -4 | 681 | 2 |
| RUS_KDK | Krasnodarskiy Kray | 295 | -19 | 14.3 | 0.0 | 1246 | 2 | 499 | 2 |
| RUS_KGO | Kurganskaya Oblast | 204 | -22 | 12.6 | -0.6 | 1133 | 2 | 441 | 1 |
| RUS_KRO | Kirovskaya Oblast | 230 | -30 | 11.0 | -0.9 | 980 | -6 | 333 | -15 |
| RUS_KSO | Kurskaya Oblast | 324 | 8 | 15.0 | 0.4 | 1218 | 5 | 520 | 7 |
| RUS_LSO | Lipetskaya Oblast | 249 | -19 | 14.8 | 0.3 | 1203 | 3 | 502 | 4 |
| RUS_MDR | Mordoviya Rep. | 242 | -25 | 13.6 | 0.2 | 1198 | 6 | 464 | 3 |
| RUS_NBO | Novosibirskaya Oblast | 249 | -10 | 11.3 | -1.2 | 1125 | 1 | 400 | -6 |
| RUS_NZO | Nizhegorodskaya O. | 256 | -21 | 12.9 | -0.1 | 1102 | 1 | 415 | -3 |
| RUS_OBO | Orenburgskaya Oblast | 174 | -32 | 15.3 | 0.1 | 1294 | 5 | 573 | 5 |
| RUS_OKO | Omskaya Oblast | 289 | 8 | 11.4 | -1.2 | 1104 | 2 | 400 | -4 |
| RUS_PMO | Permskaya Oblast | 334 | 2 | 10.5 | -1.1 | 964 | -7 | 319 | -17 |
| RUS_PZO | Penzenskaya Oblast | 232 | -24 | 14.2 | 0.3 | 1211 | 6 | 490 | 5 |
| RUS_RSO | Rostovskaya Oblast | 280 | 3 | 18.0 | 0.3 | 1287 | -1 | 645 | 2 |
| RUS_RYO | Ryazanskaya Oblast | 270 | -15 | 14.1 | 0.0 | 1148 | 2 | 462 | 0 |
| RUS_SLK | Stavropolskiy Kray | 283 | -32 | 18.3 | 0.9 | 1310 | 0 | 672 | 7 |
| RUS_SLO | Sverdlovskaya Oblast | 222 | -30 | 11.3 | -0.5 | 1061 | 2 | 375 | -2 |
| RUS_SSO | Samarskaya Oblast | 232 | -24 | 14.5 | -0.1 | 1203 | 2 | 496 | -2 |
| RUS_STO | Saratovskaya Oblast | 207 | -17 | 16.0 | 0.2 | 1287 | 4 | 587 | 5 |
| RUS_TBO | Tambovskaya Oblast | 200 | -33 | 15.0 | 0.4 | 1227 | 3 | 523 | 5 |
| RUS_TSO | Tyumenskaya Oblast | 292 | 8 | 11.5 | -0.8 | 1078 | 3 | 393 | -2 |
| RUS_TSR | Tatarstan Rep. | 235 | -27 | 12.7 | -0.6 | 1113 | 0 | 415 | -5 |
| RUS_USO | Ulyanovskaya Oblast | 209 | -34 | 13.9 | 0.0 | 1186 | 4 | 472 | 1 |
| RUS_UTR | Udmurtiya Rep. | 271 | -13 | 11.2 | -1.0 | 1009 | -4 | 348 | -14 |
| RUS_VLO | Volgogradskaya O. | 248 | 14 | 17.3 | 0.2 | 1285 | 1 | 627 | 5 |
| RUS_VRO | Voronezhskaya Oblast | 305 | 3 | 15.8 | 0.3 | 1251 | 3 | 558 | 5 |
See note table A.1. Table A.10. United States, April 2019 - July 2019 agroclimatic indicators and biomass (by state)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| USA_AKS | Arkansas | 632 | 53 | 22.0 | -0.7 | 1345 | -2 | 795 | -1 |
| USA_CLF | California | 142 | 45 | 17.1 | -0.3 | 1575 | -3 | 475 | -2 |
| USA_IDH | Idaho | 302 | 28 | 11.6 | -0.6 | 1437 | -2 | 513 | 0 |
| USA_IDN | Indiana | 570 | 25 | 18.1 | -0.5 | 1227 | -6 | 626 | -7 |
| USA_ILN | Illinois | 618 | 46 | 18.3 | -0.9 | 1242 | -6 | 640 | -8 |
| USA_IOW | Iowa | 565 | 40 | 16.8 | -1.2 | 1186 | -9 | 575 | -13 |
| USA_KSS | Kansas | 493 | 50 | 19.9 | -1.7 | 1397 | -1 | 750 | -6 |
| USA_MCG | Michigan | 447 | 16 | 13.2 | -1.0 | 1158 | -6 | 463 | -8 |
| USA_MNS | Minnesota | 512 | 30 | 13.8 | -1.1 | 1168 | -5 | 479 | -10 |
| USA_MSR | Missouri | 554 | 45 | 19.8 | -0.9 | 1334 | -2 | 724 | -4 |
| USA_MTN | Montana | 422 | 32 | 11.6 | -1.3 | 1319 | -4 | 478 | -11 |
| USA_NBS | Nebraska | 536 | 66 | 16.7 | -2.1 | 1327 | -5 | 629 | -13 |
| USA_NDK | North Dakota | 377 | 7 | 13.7 | -1.0 | 1247 | -2 | 506 | -8 |
| USA_OHO | Ohio | 471 | 8 | 17.7 | 0.1 | 1240 | -3 | 619 | -2 |
| USA_OKH | Oklahoma | 503 | 48 | 21.8 | -1.6 | 1383 | -2 | 795 | -3 |
| USA_ORG | Oregon | 288 | 20 | 12.7 | -0.1 | 1353 | -1 | 487 | 0 |
| USA_SDK | South Dakota | 619 | 89 | 14.7 | -2.3 | 1235 | -9 | 540 | -17 |
| USA_TES | Texas | 412 | 31 | 24.3 | -0.8 | 1414 | -1 | 882 | 3 |
| USA_WHT | Washington | 219 | -12 | 13.7 | 0.1 | 1332 | -1 | 532 | 6 |
| USA_WSC | Wisconsin | 493 | 21 | 13.8 | -0.9 | 1169 | -5 | 487 | -7 |
See note table A.1.
Table A.11. China, April 2019 - July 2019 agroclimatic indicators and biomass (by province)
| RAIN | TEMP | RADPAR | BIOMSS | ||||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| CHN_AHS | Anhui | 450 | -40 | 22.1 | 0.0 | 1209 | 4 | 672 | 0 |
| CHN_CQS | Chongqing | 708 | -11 | 20.0 | -0.2 | 1009 | -8 | 548 | -10 |
| CHN_FJS | Fujian | 1412 | 5 | 21.4 | 0.1 | 978 | -6 | 571 | -6 |
| CHN_GSS | Gansu | 323 | -7 | 13.8 | -0.2 | 1286 | -2 | 512 | -2 |
| CHN_GDS | Guangdong | 1503 | -7 | 24.6 | 0.4 | 1086 | -2 | 711 | 0 |
| CHN_GXZ | Guangxi | 1433 | 0 | 23.7 | 0.4 | 1024 | -5 | 652 | -4 |
| CHN_GZS | Guizhou | 1054 | 1 | 19.1 | 0.2 | 871 | -10 | 449 | -10 |
| CHN_HEB | Hebei | 175 | -25 | 20.1 | 0.3 | 1398 | 1 | 676 | -1 |
| CHN_HLJ | Heilongjiang | 335 | 9 | 14.8 | -0.2 | 1246 | 1 | 508 | -3 |
| CHN_HEN | Henan | 226 | -43 | 22.9 | 0.3 | 1292 | 1 | 735 | 2 |
| CHN_HUB | Hubei | 511 | -34 | 20.8 | -0.2 | 1158 | 1 | 647 | 0 |
| CHN_HUN | Hunan | 1122 | 5 | 21.4 | -0.3 | 1021 | -4 | 594 | -5 |
| CHN_JSS | Jiangsu | 305 | -53 | 21.9 | 0.0 | 1232 | 4 | 669 | -1 |
| CHN_JXS | Jiangxi | 1388 | 11 | 22.2 | -0.1 | 1023 | -3 | 610 | -3 |
| CHN_JLS | Jilin | 266 | -29 | 16.3 | 0.5 | 1350 | 6 | 603 | 9 |
| CHN_LNS | Liaoning | 183 | -47 | 18.0 | 0.7 | 1367 | 6 | 653 | 8 |
| CHN_NMG | Inner Mongolia | 218 | 5 | 15.7 | 0.2 | 1375 | 2 | 578 | 1 |
| CHN_NXZ | Ningxia | 173 | 9 | 16.9 | -0.4 | 1382 | -2 | 644 | -3 |
| CHN_SHX | Shaanxi | 369 | -11 | 17.9 | -0.4 | 1223 | -6 | 604 | -6 |
| CHN_SDS | Shandong | 163 | -52 | 22.3 | 0.5 | 1362 | 2 | 759 | 3 |
| CHN_SXI | Shanxi | 209 | -13 | 17.3 | -0.1 | 1374 | 0 | 631 | 2 |
| CHN_SCS | Sichuan | 778 | -3 | 17.2 | -0.1 | 1034 | -10 | 463 | -11 |
| CHN_YNS | Yunnan | 819 | -12 | 18.9 | 1.0 | 1129 | 6 | 525 | 7 |
| CHN_ZJS | Zhejiang | 1032 | 0 | 20.3 | -0.5 | 1012 | -4 | 548 | -7 |
See note table A.1.