245 related articles for article (PubMed ID: 29706974)
1. A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.
Puntel LA; Sawyer JE; Barker DW; Thorburn PJ; Castellano MJ; Moore KJ; VanLoocke A; Heaton EA; Archontoulis SV
Front Plant Sci; 2018; 9():436. PubMed ID: 29706974
[TBL] [Abstract][Full Text] [Related]
2. Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.
Puntel LA; Sawyer JE; Barker DW; Dietzel R; Poffenbarger H; Castellano MJ; Moore KJ; Thorburn P; Archontoulis SV
Front Plant Sci; 2016; 7():1630. PubMed ID: 27891133
[TBL] [Abstract][Full Text] [Related]
3. Extreme weather-year sequences have nonadditive effects on environmental nitrogen losses.
Iqbal J; Necpalova M; Archontoulis SV; Anex RP; Bourguignon M; Herzmann D; Mitchell DC; Sawyer JE; Zhu Q; Castellano MJ
Glob Chang Biol; 2018 Jan; 24(1):e303-e317. PubMed ID: 28805279
[TBL] [Abstract][Full Text] [Related]
4. Forecasting Corn Yield With Machine Learning Ensembles.
Shahhosseini M; Hu G; Archontoulis SV
Front Plant Sci; 2020; 11():1120. PubMed ID: 32849688
[TBL] [Abstract][Full Text] [Related]
5. How efficiently do corn- and soybean-based cropping systems use water? A systems modeling analysis.
Dietzel R; Liebman M; Ewing R; Helmers M; Horton R; Jarchow M; Archontoulis S
Glob Chang Biol; 2016 Feb; 22(2):666-81. PubMed ID: 26391215
[TBL] [Abstract][Full Text] [Related]
6. Economically optimal nitrogen rate reduces soil residual nitrate.
Hong N; Scharf PC; Davis JG; Kitchen NR; Sudduth KA
J Environ Qual; 2007; 36(2):354-62. PubMed ID: 17255622
[TBL] [Abstract][Full Text] [Related]
7. Nitrogen Management for Corn and Groundwater Quality in Upper Midwest Irrigated Sands.
Struffert AM; Rubin JC; Fernández FG; Lamb JA
J Environ Qual; 2016 Sep; 45(5):1557-1564. PubMed ID: 27695740
[TBL] [Abstract][Full Text] [Related]
8. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.
Shahhosseini M; Hu G; Huber I; Archontoulis SV
Sci Rep; 2021 Jan; 11(1):1606. PubMed ID: 33452349
[TBL] [Abstract][Full Text] [Related]
9. Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach.
Sela S; van Es HM; Moebius-Clune BN; Marjerison R; Moebius-Clune D; Schindelbeck R; Severson K; Young E
J Environ Qual; 2017 Mar; 46(2):311-319. PubMed ID: 28380574
[TBL] [Abstract][Full Text] [Related]
10. Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions.
Ban HY; Ahn JB; Lee BW
PLoS One; 2019; 14(2):e0211874. PubMed ID: 30802254
[TBL] [Abstract][Full Text] [Related]
11. In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt.
Joshi VR; Kazula MJ; Coulter JA; Naeve SL; Garcia Y Garcia A
Int J Biometeorol; 2021 Apr; 65(4):489-502. PubMed ID: 33222025
[TBL] [Abstract][Full Text] [Related]
12. Increasing crop diversity mitigates weather variations and improves yield stability.
Gaudin AC; Tolhurst TN; Ker AP; Janovicek K; Tortora C; Martin RC; Deen W
PLoS One; 2015; 10(2):e0113261. PubMed ID: 25658914
[TBL] [Abstract][Full Text] [Related]
13. County-scale crop yield prediction by integrating crop simulation with machine learning models.
Sajid SS; Shahhosseini M; Huber I; Hu G; Archontoulis SV
Front Plant Sci; 2022; 13():1000224. PubMed ID: 36518505
[TBL] [Abstract][Full Text] [Related]
14. Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China.
Li Z; Song M; Feng H; Zhao Y
J Sci Food Agric; 2016 Jun; 96(8):2906-16. PubMed ID: 26382017
[TBL] [Abstract][Full Text] [Related]
15. Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat.
Banakara KB; Sharma N; Sahoo S; Dubey SK; Chowdary VM
Environ Monit Assess; 2022 Nov; 195(1):51. PubMed ID: 36316588
[TBL] [Abstract][Full Text] [Related]
16. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.
Jiang H; Hu H; Zhong R; Xu J; Xu J; Huang J; Wang S; Ying Y; Lin T
Glob Chang Biol; 2020 Mar; 26(3):1754-1766. PubMed ID: 31789455
[TBL] [Abstract][Full Text] [Related]
17. A CNN-RNN Framework for Crop Yield Prediction.
Khaki S; Wang L; Archontoulis SV
Front Plant Sci; 2019; 10():1750. PubMed ID: 32038699
[TBL] [Abstract][Full Text] [Related]
18. Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015.
van der Velde M; Nisini L
Agric Syst; 2019 Jan; 168():203-212. PubMed ID: 30774183
[TBL] [Abstract][Full Text] [Related]
19. Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis.
Martinez-Feria RA; Licht MA; Ordóñez RA; Hatfield JL; Coulter JA; Archontoulis SV
Sci Rep; 2019 May; 9(1):7167. PubMed ID: 31073235
[TBL] [Abstract][Full Text] [Related]
20. Long-term Trends in Corn Yields and Soil Carbon under Diversified Crop Rotations.
Jarecki M; Grant B; Smith W; Deen B; Drury C; VanderZaag A; Qian B; Yang J; Wagner-Riddle C
J Environ Qual; 2018 Jul; 47(4):635-643. PubMed ID: 30025058
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
