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Executive summary
Global agroclimatic patterns
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Focus and perspectives
Annex A. Agroclimatic indicators and BIOMSSAnnex
Authors: 超级管理员 | Edit: zhaoxf
Annex A. Agroclimatic indicators and BIOMSS
Table A.1. January 2019 - April 2019 agroclimatic indicators and biomass by global Monitoring andReporting Unit
| 65 global MRUs | RAIN | TEMP | RADPAR | BIOMSS | |||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| 1 | Equatorial central Africa | 500 | 0 | 26.3 | 0.4 | 1238 | 5 | 1527 | 1 |
| 2 | East African highlands | 192 | -10 | 21.6 | 0.7 | 1384 | 3 | 677 | -9 |
| 3 | Gulf of Guinea | 206 | 0 | 28.6 | -0.4 | 1314 | 1 | 675 | -1 |
| 4 | Horn of Africa | 274 | -17 | 25.1 | -0.1 | 1346 | 6 | 830 | -16 |
| 5 | Madagascar (main) | 1051 | 4 | 25.2 | 0.1 | 1187 | 2 | 2106 | 10 |
| 6 | Southwest Madagascar | 467 | 5 | 25.6 | -0.3 | 1216 | -1 | 1271 | 1 |
| 7 | North Africa-Mediterranean | 95 | -33 | 11.5 | -0.6 | 983 | 2 | 353 | -30 |
| 8 | Sahel | 21 | -27 | 29.3 | -0.7 | 1358 | -2 | 72 | -23 |
| 9 | Southern Africa | 530 | 4 | 24.6 | 0.2 | 1263 | 4 | 1303 | -3 |
| 10 | Western Cape (South Africa) | 98 | -14 | 19.3 | -0.2 | 1284 | 2 | 395 | -13 |
| 11 | British Columbia to Colorado | 250 | -11 | -4.5 | -1.5 | 686 | -2 | 477 | -6 |
| 12 | Northern Great Plains | 232 | 12 | -2.3 | -2.3 | 714 | -4 | 565 | -6 |
| 13 | Corn Belt | 455 | 34 | -0.1 | -0.7 | 635 | -5 | 682 | -4 |
| 14 | Cotton Belt to Mexican Nordeste | 374 | 6 | 12.1 | -0.2 | 826 | -6 | 1030 | 4 |
| 15 | Sub-boreal America | 203 | -7 | -10.2 | -1.4 | 544 | 3 | 349 | -4 |
| 16 | West Coast (North America) | 301 | 32 | 6.4 | -0.6 | 729 | -6 | 798 | 24 |
| 17 | Sierra Madre | 49 | -37 | 16.4 | 0.4 | 1311 | 1 | 224 | -25 |
| 18 | SW U.S. and N. Mexican highlands | 122 | 24 | 8.9 | -0.3 | 1019 | -5 | 443 | 16 |
| 19 | Northern South and Central America | 167 | -33 | 26.5 | -0.1 | 1195 | 4 | 476 | -28 |
| 20 | Caribbean | 169 | -4 | 24.3 | -0.1 | 1138 | 1 | 647 | 13 |
| 21 | Central-northern Andes | 671 | -3 | 16.8 | 0.1 | 1014 | -2 | 1373 | 1 |
| 22 | Nordeste (Brazil) | 560 | 23 | 28.2 | 0.5 | 1313 | 5 | 1495 | 21 |
| 23 | Central eastern Brazil | 734 | -3 | 26.4 | 0 | 1248 | 6 | 1819 | 0 |
| 24 | Amazon | 1119 | -6 | 27 | -0.5 | 1097 | 4 | 2219 | -3 |
| 25 | Central-north Argentina | 541 | 1 | 23.9 | -1.4 | 1092 | -6 | 1421 | -2 |
| 26 | Pampas | 676 | 15 | 22.7 | -0.8 | 1137 | -4 | 1624 | 7 |
| 27 | Western Patagonia | 71 | -26 | 13 | -1 | 1241 | 4 | 338 | -16 |
| 28 | Semi-arid Southern Cone | 121 | -30 | 17.9 | -1 | 1312 | 0 | 386 | -33 |
| 29 | Caucasus | 249 | -5 | 2.9 | -0.1 | 766 | -4 | 701 | -4 |
| 30 | Pamir area | 208 | -9 | 3 | -0.2 | 848 | -7 | 629 | 4 |
| 31 | Western Asia | 218 | 33 | 7.1 | -0.2 | 866 | -6 | 658 | 22 |
| 32 | Gansu-Xinjiang (China) | 122 | 61 | -1.1 | 0.7 | 878 | -2 | 414 | 36 |
| 33 | Hainan (China) | 100 | -39 | 23.6 | 2 | 1078 | 17 | 439 | -15 |
| 34 | Huanghuaihai (China) | 140 | 34 | 6.8 | 0.2 | 889 | -4 | 539 | 25 |
| 35 | Inner Mongolia (China) | 91 | 25 | -2.7 | 1.6 | 907 | 0 | 381 | 21 |
| 36 | Loess region (China) | 115 | 35 | 3.4 | 0.3 | 969 | -2 | 433 | 18 |
| 37 | Lower Yangtze (China) | 494 | 21 | 10.7 | -0.3 | 615 | -15 | 1188 | 8 |
| 38 | Northeast China | 62 | -28 | -3.9 | 3 | 823 | 4 | ||
| 39 | Qinghai-Tibet (China) | 148 | -23 | 2.1 | 0 | 1048 | -1 | ||
| 40 | Southern China | 345 | 43 | 16.9 | 0.6 | 816 | 0 | ||
| 41 | Southwest China | 183 | 15 | 10.3 | 0.2 | 777 | -3 | ||
| 42 | Taiwan (China) | 264 | 45 | 18.4 | 1.2 | 1019 | 7 | ||
| 43 | East Asia | 107 | -23 | -0.8 | 1.3 | 796 | 3 | ||
| 44 | Southern Himalayas | 161 | 3 | 19.5 | -0.1 | 1121 | -1 | ||
| 45 | Southern Asia | 94 | -13 | 26.6 | 0 | 1300 | 1 | ||
| 46 | Southern Japan and Korea | 341 | 12 | 7.5 | 0.6 | 820 | 2 | ||
| 47 | Southern Mongolia | 181 | 170 | -8.2 | -0.2 | 860 | 0 | ||
| 48 | Punjab to Gujarat | 59 | 20 | 22.3 | -1.1 | 1184 | -2 | ||
| 49 | Maritime Southeast Asia | 1012 | -8 | 25.6 | -0.4 | 1126 | 5 | ||
| 50 | Mainland Southeast Asia | 153 | -12 | 27.3 | 0.6 | 1240 | 6 | ||
| 51 | Eastern Siberia | 136 | -27 | -9.9 | 1.3 | 592 | 6 | ||
| 52 | Eastern Central Asia | 64 | -28 | -11.6 | 2.4 | 723 | 3 | ||
| 53 | Northern Australia | 807 | -3 | 26.7 | -0.4 | 1225 | 1 | ||
| 54 | Queensland to Victoria | 171 | -23 | 22.5 | 0.9 | 1258 | 5 | ||
| 55 | Nullarbor to Darling | 65 | -40 | 20.6 | -0.8 | 1298 | 5 | ||
| 56 | New Zealand | 112 | -32 | 15.3 | 0.2 | 1082 | 9 | ||
| 57 | Boreal Eurasia | 283 | -4 | -4 | 1.1 | 408 | 6 | ||
| 58 | Ukraine to Ural mountains | 250 | 0 | -0.4 | 1.7 | 442 | -1 | ||
| 59 | Mediterranean Europe and Turkey | 204 | -5 | 7.8 | -0.1 | 820 | 5 | ||
| 60 | W. Europe (non Mediterranean) | 231 | -4 | 5.5 | 0.5 | 586 | 4 | ||
| 61 | Boreal America | 370 | 21 | -5.4 | 3.5 | 419 | -5 | ||
| 62 | Ural to Altai mountains | 169 | -2 | -6.8 | 1.4 | 531 | -3 | ||
| 63 | Australian desert | 116 | -8 | 22.8 | -0.1 | 1337 | 4 | ||
| 64 | Sahara to Afghan deserts | 104 | 23 | 17.1 | -1.1 | 1161 | -2 | ||
| 65 | Sub-arctic America | 76 | -1 | -24.5 | -1.7 | 305 | -2 | ||
Note: Departures are expressed in relative terms (percentage) for all 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. January 2019 - April 2019 agroclimatic indicators and biomass by country
| 42 countries | RAIN | TEMP | RADPAR | BIOMSS | |||||
| Current(mm) | 15YA dep.(%) | Current(°C) | 15YA dep.(°C) | Current(MJ/m2) | 15YA dep.(%) | Current(gDM/m2) | 15YA dep.(%) | ||
| [AFG] | Afghanistan | 173 | -2 | 4.9 | -1.1 | 919 | -6 | 606 | 10 |
| [AGO] | Angola | 554 | -6 | 25.8 | 1.7 | 1228 | 6 | 1590 | -2 |
| [ARG] | Argentina | 605 | 14 | 21.6 | -1.2 | 1137 | -4 | 1396 | 1 |
| [AUS] | Australia | 225 | -17 | 22.6 | 0.5 | 1258 | 4 | 580 | -21 |
| [BGD] | Bangladesh | 299 | 32 | 24.2 | 0.3 | 1174 | -1 | 873 | 35 |
| [BLR] | Belarus | 229 | -14 | 1.9 | 1.9 | 455 | 9 | 806 | 13 |
| [BRA] | Brazil | 856 | -1 | 26.3 | -0.1 | 1207 | 5 | 1932 | 3 |
| [CAN] | Canada | 254 | 0 | -8.1 | -1.4 | 572 | 2 | 371 | -7 |
| [CHN] | China | 255 | 20 | 7.7 | 0.6 | 789 | -4 | 604 | 10 |
| [DEU] | Germany | 265 | 5 | 5.5 | 1.2 | 516 | 1 | 985 | 9 |
| [EGY] | Egypt | 42 | -20 | 15 | -1.3 | 1013 | -1 | 225 | 8 |
| [ETH] | Ethiopia | 194 | 5 | 22.4 | 0.8 | 1386 | 2 | 698 | 3 |
| [FRA] | France | 160 | -18 | 6.8 | -0.5 | 637 | 6 | 630 | -15 |
| [GBR] | UK | 324 | -15 | 6.3 | 0.2 | 441 | 6 | 974 | -3 |
| [HUN] | Hungary | 124 | -12 | 6.1 | 1 | 646 | 4 | 556 | -9 |
| [IDN] | Indonesia | 1147 | -2 | 25.7 | -0.4 | 1117 | 4 | 2194 | -2 |
| [IND] | India | 97 | -3 | 24 | -0.3 | 1240 | 0 | 324 | 8 |
| [IRN] | Iran | 289 | 39 | 6.9 | -0.7 | 956 | -5 | 755 | 19 |
| [ITA] | Italy | 119 | -24 | 8.1 | 0.1 | 776 | 9 | 495 | -19 |
| [KAZ] | Kazakhstan | 159 | 4 | -4.9 | 1.4 | 632 | -4 | 495 | 6 |
| [KEN] | Kenya | 184 | -40 | 23.7 | 0.1 | 1395 | 5 | 555 | -39 |
| [KHM] | Cambodia | 145 | -26 | 28.8 | 0 | 1206 | 4 | 550 | -17 |
| [LKA] | Sri_Lanka | 401 | -31 | 27.1 | -0.1 | 1264 | 4 | 1000 | -23 |
| [MAR] | Morocco | 99 | -39 | 11.8 | 0 | 1051 | 3 | 347 | -36 |
| [MEX] | Mexico | 50 | -49 | 20 | 0.1 | 1248 | 1 | 231 | -29 |
| [MMR] | Myanmar | 88 | 1 | 24.5 | 0.3 | 1295 | 4 | 335 | 1 |
| [MNG] | Mongolia | 48 | -27 | -11.1 | 2.2 | 815 | 2 | 211 | -18 |
| [MOZ] | Mozambique | 843 | 27 | 26.3 | -0.6 | 1182 | -1 | 1603 | 4 |
| [NGA] | Nigeria | 181 | 9 | 29.2 | -0.3 | 1324 | -1 | 476 | 8 |
| [PAK] | Pakistan | 153 | 6 | 14.7 | -1.3 | 1014 | -6 | 498 | 27 |
| [PHL] | Philippines | 325 | -49 | 25.5 | -0.5 | 1198 | 7 | 870 | -28 |
| [POL] | Poland | 249 | -4 | 4.1 | 1.6 | 492 | 4 | 932 | 12 |
| [ROU] | Romania | 229 | 12 | 4 | 0.8 | 661 | 3 | 768 | 5 |
| [RUS] | Russia | 209 | -3 | -4.1 | 1.7 | 481 | -1 | 484 | 4 |
| [THA] | Thailand | 177 | -12 | 27.8 | 0.6 | 1230 | 6 | 565 | -10 |
| [TUR] | Turkey | 309 | -1 | 4.3 | -0.1 | 790 | -3 | 848 | 1 |
| [UKR] | Ukraine | 209 | -4 | 2.9 | 1.2 | 521 | -1 | 755 | 2 |
| [USA] | USA | 353 | 19 | 4.6 | -0.7 | 747 | -6 | 742 | 3 |
| [UZB] | Uzbekistan | 213 | 5 | 7.3 | 1.4 | 761 | -10 | 716 | 9 |
| [VNM] | Vietnam | 167 | -6 | 23.8 | 1.2 | 1016 | 6 | 600 | 3 |
| [ZAF] | South Africa | 375 | 14 | 20.9 | 0.3 | 1296 | 5 | 1161 | 8 |
| [ZMB] | Zambia | 525 | -12 | 24.3 | 0 | 1220 | 4 | 1482 | -9 |
See note table A.1.
Table A.3. Argentina, January 2019 - April 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 | 443 | -2 | 19.5 | -1.1 | 1235 | 2 | 1199 | -8 |
| ARG_CAC | Chaco | 1131 | 69 | 24.5 | -1.4 | 1020 | -11 | 1955 | 20 |
| ARG_CDB | Cordoba | 520 | 13 | 21.1 | -1.2 | 1181 | -4 | 1464 | 5 |
| ARG_CRT | Corrientes | 904 | 35 | 24.1 | -1.3 | 1039 | -12 | 1912 | 19 |
| ARG_ERS | Entre Rios | 756 | 24 | 22.3 | -1.2 | 1112 | -7 | 1578 | 5 |
| ARG_LPP | La Pampa | 353 | -15 | 20.5 | -0.9 | 1286 | 2 | 1055 | -17 |
| ARG_MSS | Misiones | 714 | 2 | 24.1 | -0.8 | 1142 | -4 | 1946 | 14 |
| ARG_SDE | Santiago Del Estero | 625 | 23 | 23.7 | -1.6 | 1030 | -9 | 1538 | 7 |
| ARG_SLS | San Luis | 449 | 11 | 20.2 | -1.1 | 1222 | -2 | 1372 | 4 |
| ARG_SLT | Salta | 407 | -37 | 22.7 | -1.2 | 1029 | -7 | 1204 | -21 |
| ARG_STF | Santa Fe | 744 | 25 | 22.7 | -1.2 | 1090 | -8 | 1724 | 11 |
| ARG_TCM | Tucuman | 314 | -40 | 21.8 | -1.4 | 1094 | -8 | 930 | -35 |
See note table A.1.
Table A.4. Australia, January 2019 - April 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 | 187 | -16 | 23.4 | 1.2 | 1293 | 4 | 626 | -18 |
| AUS_SOU | South Australia | 89 | -24 | 20.5 | 0.3 | 1228 | 3 | 427 | -16 |
| AUS_VCT | Victoria | 95 | -33 | 19.6 | 0.6 | 1193 | 5 | 430 | -29 |
| AUS_WES | W. Australia | 105 | -34 | 21.3 | -0.7 | 1301 | 5 | 381 | -27 |
See note table A.1.
Table A.5. Brazil, January 2019 - April 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 | 882 | 44 | 27.8 | -0.1 | 1287 | 4 | 2172 | 40 |
| BRA_GOS | Goias | 774 | -1 | 25.7 | -0.1 | 1292 | 7 | 2068 | 4 |
| BRA_MGD | Mato Grosso Do Sul | 665 | -5 | 27.1 | -0.1 | 1263 | 7 | 1717 | -7 |
| BRA_MGO | Mato Grosso | 1003 | -6 | 27.1 | -0.1 | 1183 | 7 | 2333 | 1 |
| BRA_MGS | Minas Gerais | 577 | -6 | 25.5 | 0.6 | 1286 | 8 | 1590 | 1 |
| BRA_PAA | Parana | 626 | -2 | 24.1 | 0.1 | 1209 | 4 | 1738 | 0 |
| BRA_RGD | Rio Grande Do Sul | 710 | 14 | 23.5 | -0.2 | 1087 | -8 | 1832 | 12 |
| BRA_SCA | Santa Catarina | 721 | 8 | 22.2 | 0.1 | 1075 | -4 | 1933 | 10 |
| BRA_SPL | Sao Paulo | 673 | -2 | 25.2 | 0.2 | 1201 | 6 | 1830 | 1 |
See note table A.1.
Table A.6. Canada, January2019 - April 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 | 137 | -26 | -7.9 | -1.9 | 581 | 5 | 432 | -4 |
| CAN_MTB | Manitoba | 161 | -19 | -10.6 | -1.7 | 588 | 2 | 348 | -7 |
| CAN_SKC | Saskatchewan | 129 | -29 | -10 | -1.8 | 606 | 7 | 384 | -4 |
See note table A.1.
Table A.7. India, January 2019 - April 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 | 27 | -55 | 28.1 | -0.1 | 1317 | 1 | 129 | -43 |
| IND_ASM | Assam | 310 | -19 | 22.5 | 0.4 | 1057 | 0 | 1005 | 2 |
| IND_BIH | Bihar | 120 | 59 | 22.5 | -1.4 | 1175 | -3 | 536 | 72 |
| IND_CTG | Chhattisgarh | 73 | 6 | 25.4 | -0.2 | 1251 | -1 | 336 | 15 |
| IND_DAD | Daman and Diu | 26 | 92 | 24.8 | -1.4 | 1385 | 1 | 122 | 93 |
| IND_DLH | Delhi | 125 | 97 | 20.6 | -1.2 | 1077 | -7 | 598 | 103 |
| IND_GJR | Gujarat | 11 | 6 | 25.6 | -0.6 | 1330 | 0 | 67 | 21 |
| IND_GOA | Goa | 9 | -60 | 25.2 | -0.4 | 1428 | 1 | 51 | -42 |
| IND_HCP | Himachal Pradesh | 229 | 8 | 4.6 | -0.2 | 980 | -7 | 546 | 6 |
| IND_HYN | Haryana | 117 | 20 | 19.5 | -1.2 | 1077 | -5 | 515 | 29 |
| IND_JKH | Jharkhand | 137 | 96 | 23 | -1 | 1179 | -4 | 577 | 88 |
| IND_KRL | Kerala | 169 | -35 | 26.7 | -0.2 | 1353 | 4 | 536 | -27 |
| IND_KRT | Karnataka | 54 | -37 | 26.4 | -0.3 | 1368 | 3 | 222 | -24 |
| IND_MHL | Meghalaya | 414 | -14 | 19.8 | 1 | 1112 | 3 | 1077 | 7 |
| IND_MHT | Maharashtra | 18 | -53 | 27.1 | 0.4 | 1378 | 3 | 91 | -45 |
| IND_MIP | Manipur | 167 | -34 | 18.2 | 1 | 1178 | 2 | 592 | -17 |
| IND_MYP | Madhya Pradesh | 48 | -12 | 24.2 | -0.3 | 1256 | 0 | 220 | -2 |
| IND_MZR | Mizoram | 226 | 2 | 20.4 | 0.7 | 1243 | 0 | 669 | 5 |
| IND_NGL | Nagaland | 248 | -14 | 17 | 0.8 | 1112 | 2 | 928 | 5 |
| IND_ORS | Orissa | 116 | 41 | 25.7 | -0.4 | 1216 | -2 | 473 | 38 |
| IND_PDC | Puducherry | 51 | -41 | 28.2 | 141.2 | 1395 | 0 | 233 | -15 |
| IND_PJB | Punjab | 163 | 30 | 18.6 | -0.4 | 999 | -6 | 627 | 25 |
| IND_RJT | Rajasthan | 34 | 19 | 22.5 | -1.2 | 1195 | -2 | 160 | 22 |
| IND_SKM | Sikkim | 329 | 18 | 5.1 | -0.4 | 1205 | -4 | 558 | 4 |
| IND_TND | Tamil Nadu | 54 | -53 | 28.5 | 0.3 | 1357 | 3 | 225 | -41 |
| IND_TPR | Tripura | 342 | 4 | 23.4 | 0.3 | 1182 | 2 | 832 | 2 |
| IND_UTK | Uttarakhand | 212 | 15 | 9.9 | -0.3 | 1090 | -5 | 562 | 10 |
| IND_UTP | Uttar Pradesh | 109 | 47 | 21.8 | -1 | 1148 | -3 | 466 | 50 |
| IND_WBG | West Bengal | 204 | 51 | 24.4 | -0.3 | 1183 | -2 | 696 | 43 |
See note table A.1.
Table A.8. Kazakhstan, January 2019 - April 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 | 162 | 17 | -7.4 | 1.3 | 574 | -7 | 429 | 1 |
| KAZ_KGS | Karagandinskaya | 138 | 10 | -6.6 | 1.3 | 678 | -4 | 451 | 2 |
| KAZ_KTS | Kustanayskaya | 154 | -3 | -6.9 | 1 | 539 | -5 | 456 | 2 |
| KAZ_PLS | Pavlodarskaya | 121 | 6 | -7.3 | 1.2 | 581 | -3 | 445 | 3 |
| KAZ_SKS | Severo kazachstanskaya | 153 | 1 | -7.5 | 1.2 | 511 | -3 | 416 | 1 |
| KAZ_VKS | Vostochno kazachstanskaya | 133 | -15 | -7.5 | 1.7 | 723 | 1 | 409 | 4 |
| KAZ_ZKS | Zapadno kazachstanskaya | 224 | 16 | -2.9 | 0.9 | 577 | -1 | 609 | 4 |
See note table A.1.
Table A.9. Russia, January2019 - April 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. | 244 | 2 | -5.3 | 1.6 | 450 | -3 | 468 | 7 |
| RUS_CBO | Chelyabinskaya Oblast | 170 | 1 | -6.3 | 1.2 | 479 | -3 | 440 | 3 |
| RUS_GRD | Gorodovikovsk | 281 | -1 | 4.3 | 1.2 | 584 | -1 | 887 | 6 |
| RUS_KDK | Krasnodarskiy Kray | 154 | -24 | -1.9 | 1.5 | 559 | 1 | 476 | -4 |
| RUS_KGO | Kurganskaya Oblast | 188 | 10 | -6.8 | 1.2 | 423 | -6 | 431 | 2 |
| RUS_KRO | Kirovskaya Oblast | 287 | 3 | -4.3 | 1.8 | 316 | -10 | 478 | 7 |
| RUS_KSO | Kurskaya Oblast | 279 | 6 | 0.6 | 1.5 | 428 | -5 | 734 | 8 |
| RUS_LSO | Lipetskaya Oblast | 240 | -8 | -0.5 | 1.7 | 429 | -6 | 677 | 9 |
| RUS_MDR | Mordoviya Rep. | 226 | -11 | -2.3 | 1.8 | 426 | -1 | 587 | 9 |
| RUS_NBO | Novosibirskaya Oblast | 163 | -14 | -8.4 | 1.6 | 447 | -2 | 395 | 3 |
| RUS_NZO | Nizhegorodskaya O. | 246 | -6 | -2.2 | 2 | 367 | -6 | 582 | 10 |
| RUS_OBO | Orenburgskaya Oblast | 237 | 7 | -5 | 1.2 | 538 | 0 | 507 | 5 |
| RUS_OKO | Omskaya Oblast | 175 | -3 | -8.4 | 1.2 | 416 | -6 | 386 | 0 |
| RUS_PMO | Permskaya Oblast | 290 | 9 | -5.8 | 1.8 | 326 | -10 | 432 | 6 |
| RUS_PZO | Penzenskaya Oblast | 255 | -1 | -2.6 | 1.5 | 434 | -3 | 581 | 7 |
| RUS_RSO | Rostovskaya Oblast | 169 | -16 | 2.5 | 1.1 | 558 | -1 | 660 | -7 |
| RUS_RYO | Ryazanskaya Oblast | 252 | -5 | -1.3 | 1.8 | 397 | -5 | 633 | 9 |
| RUS_SLK | Stavropolskiy Kray | 171 | -15 | 4.4 | 1.1 | 622 | 0 | 678 | -10 |
| RUS_SLO | Sverdlovskaya Oblast | 222 | 7 | -6.1 | 1.7 | 370 | -5 | 437 | 6 |
| RUS_SSO | Samarskaya Oblast | 283 | 19 | -3.9 | 1.4 | 466 | -3 | 534 | 6 |
| RUS_STO | Saratovskaya Oblast | 277 | 19 | -2.5 | 1 | 498 | -3 | 603 | 4 |
| RUS_TBO | Tambovskaya Oblast | 238 | -10 | -1.4 | 1.6 | 434 | -6 | 638 | 8 |
| RUS_TSO | Tyumenskaya Oblast | 205 | 8 | -7.6 | 1.4 | 370 | -8 | 404 | 2 |
| RUS_TSR | Tatarstan Rep. | 275 | 11 | -3.9 | 1.7 | 386 | -6 | 513 | 7 |
| RUS_USO | Ulyanovskaya Oblast | 257 | 10 | -3.5 | 1.5 | 434 | -3 | 538 | 6 |
| RUS_UTR | Udmurtiya Rep. | 285 | 6 | -4.7 | 1.8 | 335 | -8 | 469 | 7 |
| RUS_VLO | Volgogradskaya O. | 229 | 1 | 0.1 | 1 | 547 | -1 | 710 | 3 |
| RUS_VRO | Voronezhskaya Oblast | 274 | 5 | 0.2 | 1.6 | 474 | -7 | 721 | 8 |
See note table A.1.
Table A.10. United States, January 2019 - April 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 | 618 | 24 | 9.7 | -0.6 | 727 | -8 | 1379 | 5 |
| USA_CLF | California | 312 | 67 | 7.7 | -0.3 | 821 | -9 | 778 | 37 |
| USA_IDH | Idaho | 267 | 10 | -2.1 | -0.9 | 695 | -3 | 598 | 0 |
| USA_IDN | Indiana | 451 | 26 | 3.3 | -0.5 | 639 | -8 | 912 | -3 |
| USA_ILN | Illinois | 375 | 18 | 2.6 | -1.1 | 653 | -9 | 880 | -3 |
| USA_IOW | Iowa | 351 | 28 | -1.5 | -2 | 654 | -7 | 674 | -12 |
| USA_KSS | Kansas | 218 | 16 | 3.7 | -1.8 | 820 | -6 | 681 | 8 |
| USA_MCG | Michigan | 362 | 21 | -2.3 | -1.1 | 592 | -4 | 573 | -9 |
| USA_MNS | Minnesota | 316 | 24 | -6.6 | -2.6 | 597 | -6 | 453 | -17 |
| USA_MSR | Missouri | 450 | 11 | 4.9 | -1.1 | 706 | -8 | 1028 | -1 |
| USA_MTN | Montana | 196 | -3 | -5.2 | -3.5 | 698 | -2 | 510 | -15 |
| USA_NBS | Nebraska | 214 | 22 | -0.4 | -2.4 | 768 | -7 | 687 | 0 |
| USA_NDK | North Dakota | 252 | 24 | -7.9 | -3 | 645 | -3 | 435 | -15 |
| USA_OHO | Ohio | 468 | 31 | 3.2 | 0 | 648 | -4 | 904 | 1 |
| USA_OKH | Oklahoma | 245 | -16 | 7.9 | -1.5 | 816 | -7 | 857 | 2 |
| USA_ORG | Oregon | 261 | 16 | 2.9 | -0.9 | 629 | -4 | 776 | 7 |
| USA_SDK | South Dakota | 274 | 41 | -4.4 | -3.5 | 682 | -8 | 542 | -16 |
| USA_TES | Texas | 230 | -4 | 12.9 | -1 | 865 | -7 | 761 | 9 |
| USA_WHT | Washington | 222 | -12 | 1.5 | -1.5 | 595 | 0 | 742 | 4 |
| USA_WSC | Wisconsin | 391 | 33 | -4 | -1.8 | 618 | -4 | 528 | -13 |
See note table A.1.
Table A.11. China, January 2019 - April 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 | 262 | -14 | 9.3 | -0.2 | 730 | -11 | 908 | -5 |
| CHN_CQS | Chongqing | 180 | -7 | 9.4 | 0 | 709 | -6 | 690 | 1 |
| CHN_FJS | Fujian | 534 | 27 | 13.2 | 0.7 | 650 | -8 | 1259 | 13 |
| CHN_GDS | Guangdong | 658 | 69 | 17.1 | 0.9 | 671 | -4 | 1097 | 11 |
| CHN_GSS | Gansu | 82 | 10 | 2.3 | 0.4 | 985 | 0 | 337 | 15 |
| CHN_GXZ | Guangxi | 421 | 39 | 15.3 | -0.1 | 508 | -18 | 1001 | 21 |
| CHN_GZS | Guizhou | 266 | 38 | 10.6 | 0.2 | 584 | -8 | 817 | 25 |
| CHN_HEB | Hebei | 99 | 57 | 2.4 | 0.5 | 912 | -3 | 409 | 39 |
| CHN_HEN | Henan | 122 | -12 | 7.9 | -0.2 | 877 | -4 | 527 | -7 |
| CHN_HLJ | Heilongjiang | 61 | -24 | -5.2 | 3.6 | 786 | 6 | 301 | -8 |
| CHN_HUB | Hubei | 237 | -13 | 8.7 | -0.5 | 734 | -9 | 862 | 1 |
| CHN_HUN | Hunan | 485 | 20 | 9.8 | -0.9 | 548 | -17 | 1206 | 8 |
| CHN_JLS | Jilin | 65 | -26 | -2.8 | 2.8 | 861 | 4 | 315 | -14 |
| CHN_JSS | Jiangsu | 217 | 4 | 8.4 | 0.1 | 782 | -10 | 812 | 6 |
| CHN_JXS | Jiangxi | 589 | 17 | 11.4 | -0.4 | 569 | -17 | 1398 | 9 |
| CHN_LNS | Liaoning | 64 | -31 | 0.3 | 1.7 | 890 | 1 | 308 | -23 |
| CHN_NMG | Inner Mongolia | 83 | 9 | -4.5 | 2.3 | 876 | 1 | 358 | 20 |
| CHN_NXZ | Ningxia | 50 | 21 | 1.9 | 0.5 | 999 | -1 | 226 | 23 |
| CHN_SCS | Sichuan | 106 | -9 | 9.4 | 0.3 | 867 | -1 | 421 | -8 |
| CHN_SDS | Shandong | 175 | 77 | 6.4 | 0.3 | 905 | -3 | 608 | 44 |
| CHN_SHX | Shaanxi | 118 | 16 | 5.1 | 0.2 | 935 | -1 | 478 | 13 |
| CHN_SXI | Shanxi | 135 | 66 | 1.7 | 0.4 | 942 | -2 | 489 | 32 |
| CHN_YNS | Yunnan | 149 | 42 | 13.8 | 0.4 | 1103 | 6 | 505 | 23 |
| CHN_ZJS | Zhejiang | 500 | 26 | 10.2 | 0.3 | 630 | -16 | 1370 | 21 |
See note table A.1.