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Publications Using POWER Data

2020s

2022

Akram, H., D. F. Levia, J. E. Herrick, H. Lydiasari, and N. Schütze, 2022: Water requirements for oil palm grown on marginal lands: A simulation approach. Agricultural Water Management, 260, 107292, https://doi.org/10.1016/j.agwat.2021.107292.
Araghi, A., C. J. Martinez, and J. E. Olesen, 2022: Evaluation of multiple gridded solar radiation data for crop modeling. European Journal of Agronomy, 133, 126419, https://doi.org/10.1016/j.eja.2021.126419.

2021

Gunes, O. E., A.C. Basara, and Y. Sisman, 2021: Determination of Precipitation Trend by Time Series: A case study Erbaa Plain. Advanced GIS,1(1)8-14.
Ali-Ou-Salah, Hamza, Benyounes Oukarfi, and Tlemcani Mouhaydine, 2021: Short-Term Solar Radiation Forecasting Using a New Seasonal Clustering Technique and Artificial Neural Network. International Journal of Green Energy, 6, 37, https://doi.org/10.1007/s41207-021-00241-y.
Banik, R., P. Das, S. Ray, and A. Biswas, 2021: An Improved ANN Model for Prediction of Solar Radiation Using Machine Learning Approach. Applications of Internet of Things, J.K. Mandal, S. Mukhopadhyay, and A. Roy, Eds., Vol. 137 of Lecture Notes in Networks and Systems, Springer Singapore, 233–242.
Bansal, N., P. Pany, and G. Singh, 2021: Visual degradation and performance evaluation of utility scale solar photovoltaic power plant in hot and dry climate in western India. Case Studies in Thermal Engineering, 26, 101010, https://doi.org/10.1016/j.csite.2021.101010.
Berghuijs, H. N. C., and Coauthors, 2021: Calibrating and testing APSIM for wheat-faba bean pure cultures and intercrops across Europe. Field Crops Research, 264, 108088, https://doi.org/10.1016/j.fcr.2021.108088.
Bhattacharjee, S., and C. Nandi, 2021: Design of a voting based smart energy management system of the renewable energy based hybrid energy system for a small community. Energy, 214, 118977, https://doi.org/10.1016/j.energy.2020.118977.
Costa-Neto, G., G. Galli, H. F. Carvalho, J. Crossa, and R. Fritsche-Neto, 2021: EnvRtype : a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics, 11, jkab040, https://doi.org/10.1093/g3journal/jkab040.
Damak, F., M. S. M. Bougi, D. Araoka, K. Baba, M. Furuya, M. Ksibi, and K. Tamura, 2021: Soil geochemistry, edaphic and climatic characteristics as components of Tunisian olive terroirs: Relationship with the multielemental composition of olive oils for their geographical traceability. Euro-Mediterr J Environ Integr, 6, 37, https://doi.org/10.1007/s41207-021-00241-y.
Elkhrachy, I., Q. B. Pham, R. Costache, M. Mohajane, K. U. Rahman, H. Shahabi, N. T. T. Linh, and D. T. Anh, 2021: Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. J Flood Risk Management, 14, https://doi.org/10.1111/jfr3.12692.
Hajjarpoor, A., and Coauthors, 2021: Environmental characterization and yield gap analysis to tackle genotype-by-environment-by-management interactions and map region-specific agronomic and breeding targets in groundnut. Field Crops Research, 267, 108160, https://doi.org/10.1016/j.fcr.2021.108160.
Isaza Cuervo, F., C. A. Arredondo-Orozco, and G. C. Marenco-Maldonado, 2021: Photovoltaic power purchase agreement valuation under real options approach. Renewable Energy Focus, 36, 96–107, https://doi.org/10.1016/j.ref.2020.12.006.
Jarrett, C., L. L. Powell, T. T. R. Claire, M. Tchoumbou, and B. Helm, 2021: Moult of overwintering Wood Warblers Phylloscopus sibilatrix in an annual-cycle perspective. J Ornithol, https://doi.org/10.1007/s10336-021-01859-z.
Jat, R., B. R. Gurjar, and D. Lowe, 2021: Regional pollution loading in winter months over India using high resolution WRF-Chem simulation. Atmospheric Research, 249, 105326, https://doi.org/10.1016/j.atmosres.2020.105326.
Jha, P. K., A. Araya, Z. P. Stewart, A. Faye, H. Traore, B. J. Middendorf, and P. V. V. Prasad, 2021: Projecting potential impact of COVID-19 on major cereal crops in Senegal and Burkina Faso using crop simulation models. Agricultural Systems, 190, 103107, https://doi.org/10.1016/j.agsy.2021.103107.
Kadiri, W. O. J., A. S. Fasina, and T. S. Babalola, 2021: Soil organic carbon concentration and stock of arable land use of two agro-ecological zones of Nigeria. Journal of the Saudi Society of Agricultural Sciences, 20, 180–189, https://doi.org/10.1016/j.jssas.2021.01.004.
Komarek, A. M., C. Thierfelder, and P. R. Steward, 2021: Conservation agriculture improves adaptive capacity of cropping systems to climate stress in Malawi. Agricultural Systems, 190, 103117, https://doi.org/10.1016/j.agsy.2021.103117.
Kudria, S., I. Ivanchenko, B. Tuchynskyi, K. Petrenko, O. Karmazin, and O. Riepkin, 2021: Resource potential for wind-hydrogen power in Ukraine. International Journal of Hydrogen Energy, 46, 157–168, https://doi.org/10.1016/j.ijhydene.2020.09.211.
Li, C., Y. Zheng, Z. Li, L. Zhang, L. Zhang, Y. Shan, and Q. Tang, 2021: Techno-economic and environmental evaluation of grid-connected and off-grid hybrid intermittent power generation systems: A case study of a mild humid subtropical climate zone in China. Energy, 230, 120728, https://doi.org/10.1016/j.energy.2021.120728.
Li, Z., and Coauthors, 2021: Integrating an interferometric synthetic aperture radar technique and numerical simulation to investigate the Tongmai old deposit along the Sichuan-Tibet Railway. Geomorphology, 377, 107586, https://doi.org/10.1016/j.geomorph.2020.107586.
Martin, C., Q. Zhang, D. Zhai, X. Zhang, and C. M. Duarte, 2021: Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea. Data in Brief, 36, 107056, https://doi.org/10.1016/j.dib.2021.107056.
Marzouk, O. A., 2021: Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements. Heliyon, 7, e06625, https://doi.org/10.1016/j.heliyon.2021.e06625.
Mompremier, R., Y. Her, G. Hoogenboom, K. Migliaccio, R. Muñoz-Carpena, Z. Brym, R. W. Colbert, and W. Jeune, 2021: Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology. Agricultural Water Management, 243, 106429, https://doi.org/10.1016/j.agwat.2020.106429.
Morais Filho, L. F. F., K. C. de Meneses, G. A. de A. Santos, E. da S. Bicalho, G. de S. Rolim, and N. La Scala Jr, 2021: xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach. Journal of Environmental Management, 288, 112433, https://doi.org/10.1016/j.jenvman.2021.112433.
Moreno-Gamboa, F., and C. Nieto-Londoño, 2021: Hybrid Brayton Multi-Stage Concentrated Solar Power Plant Energy and Exergy Performance Study. Journal of Energy Resources Technology, 143, 062108, https://doi.org/10.1115/1.4050486.
Mugi-Ngenga, E., S. Zingore, L. Bastiaans, N. P. R. Anten, and K. E. Giller, 2021: Farm‐scale assessment of maize–pigeonpea productivity in Northern Tanzania. Nutr Cycl Agroecosyst, https://doi.org/10.1007/s10705-021-10144-7.
Ndiaye, P. M., A. Bodian, L. Diop, A. Dezetter, E. Guilpart, A. Deme, and A. Ogilvie, 2021: Future trend and sensitivity analysis of evapotranspiration in the Senegal River Basin. Journal of Hydrology: Regional Studies, 35, 100820, https://doi.org/10.1016/j.ejrh.2021.100820.
de Oliveira Aparecido, P. A. Lorençone, J. A. Lorençone, K. C. de Meneses, and J. R. da Silva Cabral de Moraes, 2021: Climate changes and their influences in water balance of Pantanal biome. Theor Appl Climatol, 143, 659–674, https://doi.org/10.1007/s00704-020-03445-4.
Pushpalatha, R., and B. Gangadharan, 2021: Assessing the influence of climate model biases in predicting yield and irrigation requirement of cassava. Model. Earth Syst. Environ., 7, 307–315, https://doi.org/10.1007/s40808-020-01038-8.
Parenti, A., G. Cappelli, W. Zegada-Lizarazu, C. Martín Sastre, M. Christou, A. Monti, and F. Ginaldi, 2021: SunnGro: A new crop model for the simulation of sunn hemp (Crotalaria juncea L.) grown under alternative management practices. Biomass and Bioenergy, 146, 105975, https://doi.org/10.1016/j.biombioe.2021.105975.
Rana, N., and Coauthors, 2021: A preliminary assessment of the 7th February 2021 flashflood in lower Dhauli Ganga valley, Central Himalaya, India. J Earth Syst Sci, 130, 78, https://doi.org/10.1007/s12040-021-01608-z.
Rodrigues, G. and R.Braga, 2021: Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy, 11, 6, https://doi.org/10.3390/agronomy11061207.
Rodrigues, G. C., and R. P. Braga, 2021: Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy, 11, 1207, https://doi.org/10.3390/agronomy11061207.
Rodrigues, G. C., and R. P. Braga, 2021: Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate. Agronomy, 11, 2077, https://doi.org/10.3390/agronomy11102077.
Shahhosseini, M., G. Hu, I. Huber, and S. V. Archontoulis, 2021: Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci Rep, 11, 1606, https://doi.org/10.1038/s41598-020-80820-1.
Shin, J., I. Hwang, D. Kim, T. Moon, J. Kim, W. H. Kang, and J. E. Son, 2021: Evaluation of the light profile and carbon assimilation of tomato plants in greenhouses with respect to film diffuseness and regional solar radiation using ray-tracing simulation. Agricultural and Forest Meteorology, 296, 108219, https://doi.org/10.1016/j.agrformet.2020.108219.
Tambo, J. A., M. Matimelo, M. Ndhlovu, F. Mbugua, and N. Phiri, 2021: Gender-differentiated impacts of plant clinics on maize productivity and food security: Evidence from Zambia. World Development, 145, 105519, https://doi.org/10.1016/j.worlddev.2021.105519.
Zagarese, H. E., and Coauthors, 2021: Patterns of CO2 concentration and inorganic carbon limitation of phytoplankton biomass in agriculturally eutrophic lakes. Water Research, 190, 116715, https://doi.org/10.1016/j.watres.2020.116715.

2020

Almeida Silva, K., G. de Souza Rolim, T. T. Borges Valeriano, and J. R. da Silva Cabral de Moraes, 2020: Influence of El Niño and La Niña on coffee yield in the main coffee-producing regions of Brazil. Theor Appl Climatol, 139, 1019–1029, https://doi.org/10.1007/s00704-019-03039-9.
Amos, C., A. Rahman, J. Gathenya, E. Friedler, F. Karim, and A. Renzaho, 2020: Roof-Harvested Rainwater Use in Household Agriculture: Contributions to the Sustainable Development Goals. Water, 12, 332, https://doi.org/10.3390/w12020332.
Awe, G. O., T. N. Akomolafe, J. Umam, and M. B. Ayuba, 2020: Efficiency of small pan evaporimeter in monitoring evapotranspiration under poly-covered house and open-field conditions in a hot, tropical region of Nigeria. Journal of Hydrology: Regional Studies, 32, 100735, https://doi.org/10.1016/j.ejrh.2020.100735.
Bessah, E., A. O. Raji, O. J. Taiwo, S. K. Agodzo, O. O. Ololade, and A. Strapasson, 2020: Hydrological responses to climate and land use changes: The paradox of regional and local climate effect in the Pra River Basin of Ghana. Journal of Hydrology: Regional Studies, 27, 100654, https://doi.org/10.1016/j.ejrh.2019.100654.
Chan, H.-P., and K. I. Konstantinou, 2020: Multiscale and multitemporal surface temperature monitoring by satellite thermal infrared imagery at Mayon Volcano, Philippines. Journal of Volcanology and Geothermal Research, 401, 106976, https://doi.org/10.1016/j.jvolgeores.2020.106976.
Duarte, Y. C. N., and P. C. Sentelhas, 2020: NASA/POWER and DailyGridded weather datasets—how good they are for estimating maize yields in Brazil? Int J Biometeorol, 64, 319–329, https://doi.org/10.1007/s00484-019-01810-1.
Duarte, Y. C. N., and P. C. Sentelhas, 2020: Correction to: NASA/POWER and DailyGridded weather datasets—how good they are for estimating maize yields in Brazil? Int J Biometeorol, 64, 331–332, https://doi.org/10.1007/s00484-019-01834-7.
Frid, S. E., V. M. Simonov, N. V. Lisitskaya, N. R. Avezova, and A. E. Khaitmukhamedov, 2020: Efficiency of Solar Trackers and Bifacial Photovoltaic Panels for Southern Regions of the Russian Federation and the Republic of Uzbekistan. Appl. Sol. Energy, 56, 425–430, https://doi.org/10.3103/S0003701X20060031.
Gárate-Escamilla, H., C. C. Brelsford, A. Hampe, T. M. Robson, and M. B. Garzón, 2020: Greater capacity to exploit warming temperatures in northern populations of European beech is partly driven by delayed leaf senescence. Agricultural and Forest Meteorology, 284, 107908, https://doi.org/10.1016/j.agrformet.2020.107908.
Hariharan, R., 2020: COVID-19: A Boon for Tropical Solar Parks?: A Time Series Based Analysis and Forecasting of Solar Irradiance. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–9, https://doi.org/10.1080/15567036.2020.1839603.
Kyatengerwa, C., D. Kim, and M. Choi, 2020: A national-scale drought assessment in Uganda based on evapotranspiration deficits from the Bouchet hypothesis. Journal of Hydrology, 580, 124348, https://doi.org/10.1016/j.jhydrol.2019.124348.
Laarabi, B., Y. El Baqqal, A. Dahrouch, and A. Barhdadi, 2020: Deep analysis of soiling effect on glass transmittance of PV modules in seven sites in Morocco. Energy, 213, 118811, https://doi.org/10.1016/j.energy.2020.118811.
Jourdier, B., 2020: Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France. Adv. Sci. Res., 17, 63–77, https://doi.org/10.5194/asr-17-63-2020.
Liu, X., M. Charles, and B. R. Bakshi, 2019: Including Ecosystem Services in Life Cycle Assessment: Methodology and Application to Urban Farms. Procedia CIRP, 80, 287–291, https://doi.org/10.1016/j.procir.2018.12.004.
Mao, J., T. Addanki, M. Gebhard, F. Ghio, and H. Li, 2020: Eco-agriculture and Renewable Energy System Concept for a Rural Community in Ghana. IOP Conf. Ser.: Earth Environ. Sci., 568, 012046, https://doi.org/10.1088/1755-1315/568/1/012046.
Meneses, K. C. de, L. E. D. O. Aparecido, K. C. de Meneses, and M. F. de Farias, 2020: Estimating Potential Evapotranspiration in Maranhão State Using Artificial Neural Networks. Rev. bras. meteorol., 35, 675–682, https://doi.org/10.1590/0102-77863540072.
Moreno-Cadena, L. P., and Coauthors, 2020: Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model. European Journal of Agronomy, 115, 126031, https://doi.org/10.1016/j.eja.2020.126031.
Nathan, O. O., N. K. Felix, K. N. Milka, M. Anne, A. Noah, and M. N. Daniel, 2020: Suitability of different data sources in rainfall pattern characterization in the tropical central highlands of Kenya. Heliyon, 6, e05375, https://doi.org/10.1016/j.heliyon.2020.e05375.
Nyagumbo, I., W. Mupangwa, L. Chipindu, L. Rusinamhodzi, and P. Craufurd, 2020: A regional synthesis of seven-year maize yield responses to conservation agriculture technologies in Eastern and Southern Africa. Agriculture, Ecosystems & Environment, 295, 106898, https://doi.org/10.1016/j.agee.2020.106898.
Okal, H. A., F. K. Ngetich, and J. M. Okeyo, 2020: Spatio-temporal characterisation of droughts using selected indices in Upper Tana River watershed, Kenya. Scientific African, 7, e00275, https://doi.org/10.1016/j.sciaf.2020.e00275.
de Oliveira Aparecido, L. E., J. R. da Silva Cabral de Moraes, K. C. de Meneses, G. B. Torsoni, R. F. de Lima, and C. T. S. Costa, 2020: Köppen-Geiger and Camargo climate classifications for the Midwest of Brasil. Theor Appl Climatol, 142, 1133–1145, https://doi.org/10.1007/s00704-020-03358-2.
Okal, H. A., F. K. Ngetich, and J. M. Okeyo, 2020: Spatio-temporal characterisation of droughts using selected indices in Upper Tana River watershed, Kenya. Scientific African, 7, e00275, https://doi.org/10.1016/j.sciaf.2020.e00275.
Ozerova, M. G., A. V. Bastron, A. S. Debrin, N. B. Mikheeva, and I. N. Ermakova, 2020: The use of light filters in the photovoltaic solar power station to improve economic efficiency. IOP Conf. Ser.: Earth Environ. Sci., 421, 032016, https://doi.org/10.1088/1755-1315/421/3/032016.
Otter, P., and Coauthors, 2020: Economic evaluation of water supply systems operated with solar-driven electro-chlorination in rural regions in Nepal, Egypt and Tanzania. Water Research, 187, 116384, https://doi.org/10.1016/j.watres.2020.116384.
Rattalino Edreira, J. I., S. Mourtzinis, G. Azzari, J. F. Andrade, S. P. Conley, D. Lobell, J. E. Specht, and P. Grassini, 2020: From sunlight to seed: Assessing limits to solar radiation capture and conversion in agro-ecosystems. Agricultural and Forest Meteorology, 280, 107775, https://doi.org/10.1016/j.agrformet.2019.107775.
Rodrigo, P. M., 2020: Balancing the shading impact in utility-scale dual-axis tracking concentrator photovoltaic power plants. Energy, 210, 118490, https://doi.org/10.1016/j.energy.2020.118490.
Ramborun, V., S. Facknath, and B. Lalljee, 2020: Moving toward sustainable agriculture through a better understanding of farmer perceptions and attitudes to cope with climate change. The Journal of Agricultural Education and Extension, 26, 37–57, https://doi.org/10.1080/1389224X.2019.1690012.
Rattalino Edreira, J. I., S. Mourtzinis, G. Azzari, J. F. Andrade, S. P. Conley, J. E. Specht, and P. Grassini, 2020: Combining field-level data and remote sensing to understand impact of management practices on producer yields. Field Crops Research, 257, 107932, https://doi.org/10.1016/j.fcr.2020.107932.
Salazar, G., C. Gueymard, J. B. Galdino, O. de Castro Vilela, and N. Fraidenraich, 2020: Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil. Renewable and Sustainable Energy Reviews, 117, 109478, https://doi.org/10.1016/j.rser.2019.109478.
Schwalbert, R. A., T. Amado, G. Corassa, L. P. Pott, P. V. V. Prasad, and I. A. Ciampitti, 2020: Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology, 284, 107886, https://doi.org/10.1016/j.agrformet.2019.107886.
Shadab, A., S. Ahmad, and S. Said, 2020: Spatial forecasting of solar radiation using ARIMA model. Remote Sensing Applications: Society and Environment, 20, 100427, https://doi.org/10.1016/j.rsase.2020.100427.
Sayago, S., G. Ovando, J. Almorox, and M. Bocco, 2020: Daily solar radiation from NASA-POWER product: assessing its accuracy considering atmospheric transparency. null, 41, 897–910, https://doi.org/10.1080/01431161.2019.1650986.
Fried, S. E., Lisitskaya, N. V., Tarasenko, A. B., Frolov, N. D., and Suleimanov, М. Zh., 2020: Using photovoltaic water heaters in hot climates. https://doi.org/10.5281/ZENODO.4018982.
Teixeira de Aguiar, J., and M. Lobo, 2020: Reliability and discrepancies of rainfall and temperatures from remote sensing and Brazilian ground weather stations. Remote Sensing Applications: Society and Environment, 18, 100301, https://doi.org/10.1016/j.rsase.2020.100301.

2010s

2019

Aboelkhair, H., M. Morsy, and G. El Afandi, 2019: Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt. Advances in Space Research, 64, 129–142, https://doi.org/10.1016/j.asr.2019.03.032.
Aristizábal, A. J., J. Herrera, M. Castaneda, S. Zapata, D. Ospina, and E. Banguero, 2019: A new methodology to model and simulate microgrids operating in low latitude countries. Energy Procedia, 157, 825–836, https://doi.org/10.1016/j.egypro.2018.11.248.
Barzegar, R., M. Ghasri, Z. Qi, J. Quilty, and J. Adamowski, 2019: Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada. Journal of Hydrology, 577, 123903, https://doi.org/10.1016/j.jhydrol.2019.06.075.
Bauer, M., and Coauthors, 2019: Association between solar insolation and a history of suicide attempts in bipolar I disorder. Journal of Psychiatric Research, 113, 1–9, https://doi.org/10.1016/j.jpsychires.2019.03.001.
Esan, A. B., A. F. Agbetuyi, O. Oghorada, K. Ogbeide, Ayokunle. A. Awelewa, and A. E. Afolabi, 2019: Reliability assessments of an islanded hybrid PV-diesel-battery system for a typical rural community in Nigeria. Heliyon, 5, e01632, https://doi.org/10.1016/j.heliyon.2019.e01632.
Freire, M. M., and Coauthors, 2019: Site analysis in the Argentinean Andean region for the placement of astrophysical observatories and solar photovoltaic power plants. The case of the “Leoncito 2” site. Advances in Space Research, 64, 551–566, https://doi.org/10.1016/j.asr.2019.04.023.
Jha, P. K., P. Athanasiadis, S. Gualdi, A. Trabucco, V. Mereu, V. Shelia, and G. Hoogenboom, 2019: Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal’s Terai with the NCEP CFSv2. Theor Appl Climatol, 135, 1143–1156, https://doi.org/10.1007/s00704-018-2433-5.
Kimaru, A. N., J. M. Gathenya, and C. K. Cheruiyot, 2019: The Temporal Variability of Rainfall and Streamflow into Lake Nakuru, Kenya, Assessed Using SWAT and Hydrometeorological Indices. Hydrology, 6, 88, https://doi.org/10.3390/hydrology6040088.
Lollato, R. P., J. T. Edwards, and T. E. Ochsner, 2017: Meteorological limits to winter wheat productivity in the U.S. southern Great Plains. Field Crops Research, 203, 212–226, https://doi.org/10.1016/j.fcr.2016.12.014.
Rattalino Edreira, J. I., N. Guilpart, V. Sadras, K. G. Cassman, M. K. van Ittersum, R. L. M. Schils, and P. Grassini, 2018: Water productivity of rainfed maize and wheat: A local to global perspective. Agricultural and Forest Meteorology, 259, 364–373, https://doi.org/10.1016/j.agrformet.2018.05.019.
Rodrigo, P. M., D. L. Talavera, E. F. Fernández, F. M. Almonacid, and P. J. Pérez-Higueras, 2019: Optimum capacity of the inverters in concentrator photovoltaic power plants with emphasis on shading impact. Energy, 187, 115964, https://doi.org/10.1016/j.energy.2019.115964.
Shelia, V., J. Hansen, V. Sharda, C. Porter, P. Aggarwal, C. J. Wilkerson, and G. Hoogenboom, 2019: A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environmental Modelling & Software, 115, 144–154, https://doi.org/10.1016/j.envsoft.2019.02.006.
Solangi, Y. A., S. A. A. Shah, H. Zameer, M. Ikram, and B. O. Saracoglu, 2019: Assessing the solar PV power project site selection in Pakistan: based on AHP-fuzzy VIKOR approach. Environmental Science and Pollution Research, 26, 30286–30302, https://doi.org/10.1007/s11356-019-06172-0.
Sonkar, G., R. K. Mall, T. Banerjee, N. Singh, T. V. L. Kumar, and R. Chand, 2019: Vulnerability of Indian wheat against rising temperature and aerosols. Environmental Pollution, 254, 112946, https://doi.org/10.1016/j.envpol.2019.07.114.
Valeriano, T. T. B., G. de Souza Rolim, R. C. Bispo, J. R. da Silva Cabral de Moraes, and L. E. de O. Aparecido, 2019: Evaluation of air temperature and rainfall from ECMWF and NASA gridded data for southeastern Brazil. Theor Appl Climatol, 137, 1925–1938, https://doi.org/10.1007/s00704-018-2706-z.
Valenzuela, L., A. Iglesias, M. Faraldos, A. Bahamonde, and R. Rosal, 2019: Antimicrobial surfaces with self-cleaning properties functionalized by photocatalytic ZnO electrosprayed coatings. Journal of Hazardous Materials, 369, 665–673, https://doi.org/10.1016/j.jhazmat.2019.02.073.
Vimpere, L., P. Kindler, and S. Castelltort, 2019: Chevrons: Origin and relevance for the reconstruction of past wind regimes. Earth-Science Reviews, 193, 317–332, https://doi.org/10.1016/j.earscirev.2019.04.005.

2018

Abualqumboz, M., and D. Rodley, 2018: Mathematical modelling of smart solar heating system with the deployment of borehole thermal energy storage to increase renewable heat share in Dundee, UK. Energy Procedia, 151, 37–46, https://doi.org/10.1016/j.egypro.2018.09.024.
Amin, A., and Coauthors, 2018: Evaluation and analysis of temperature for historical (1996–2015) and projected (2030–2060) climates in Pakistan using SimCLIM climate model: Ensemble application. Atmospheric Research, 213, 422–436, https://doi.org/10.1016/j.atmosres.2018.06.021.
Bai, J., X. Chen, A. Dobermann, H. Yang, K. G. Cassman, and F. Zhang, 2010: Evaluation of NASA Satellite- and Model-Derived Weather Data for Simulation of Maize Yield Potential in China. Agron. J., 102, 9–16, https://doi.org/10.2134/agronj2009.0085.
Dossou-Yovo, E. R., A. M. Kouyaté, T. Sawadogo, I. Ouédraogo, O. S. Bakare, and S. J. Zwart, 2018: A geospatial database of drought occurrence in inland valleys in Mali, Burkina Faso and Nigeria. Data in Brief, 19, 2008–2014, https://doi.org/10.1016/j.dib.2018.06.105.
van Loon, M. P., N. Deng, P. Grassini, J. I. Rattalino Edreira, E. Wolde-meskel, F. Baijukya, H. Marrou, and M. K. van Ittersum, 2018: Prospect for increasing grain legume crop production in East Africa. European Journal of Agronomy, 101, 140–148, https://doi.org/10.1016/j.eja.2018.09.004.
Monteiro, L. A., P. C. Sentelhas, and G. U. Pedra, 2018: Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation: SUGARCANE YIELD SIMULATION WITH NASA/POWER SATELLITE-BASED DATA. Int. J. Climatol, 38, 1571–1581, https://doi.org/10.1002/joc.5282.
Negm, A., M. Minacapilli, and G. Provenzano, 2018: Downscaling of American National Aeronautics and Space Administration (NASA) daily air temperature in Sicily, Italy, and effects on crop reference evapotranspiration. Agricultural Water Management, 209, 151–162, https://doi.org/10.1016/j.agwat.2018.07.016.
Ovando, G., S. Sayago, and M. Bocco, 2018: Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data. ISPRS Journal of Photogrammetry and Remote Sensing, 138, 208–217, https://doi.org/10.1016/j.isprsjprs.2018.02.015.
Patra, S., S. Sahoo, P. Mishra, and S. C. Mahapatra, 2018: Impacts of urbanization on land use /cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management, 7, 70–84, https://doi.org/10.1016/j.jum.2018.04.006.
Rizzo, G., J. I. R. Edreira, S. V. Archontoulis, H. S. Yang, and P. Grassini, 2018: Do shallow water tables contribute to high and stable maize yields in the US Corn Belt? Global Food Security, 18, 27–34, https://doi.org/10.1016/j.gfs.2018.07.002.
Schils, R., and Coauthors, 2018: Cereal yield gaps across Europe. European Journal of Agronomy, 101, 109–120, https://doi.org/10.1016/j.eja.2018.09.003.
Tesfaye, K., and Coauthors, 2018: Potential benefits of drought and heat tolerance for adapting maize to climate change in tropical environments. Climate Risk Management, 19, 106–119, https://doi.org/10.1016/j.crm.2017.10.001.

2017

Alderman, P. D., and B. Stanfill, 2017: Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis. European Journal of Agronomy, 88, 1–9, https://doi.org/10.1016/j.eja.2016.09.016.
Amin, A., and Coauthors, 2017: Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan. Atmospheric Research, 194, 214–225, https://doi.org/10.1016/j.atmosres.2017.05.002.
Bote, A. D., and J. Vos, 2017: Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality. NJAS - Wageningen Journal of Life Sciences, 83, 39–46, https://doi.org/10.1016/j.njas.2017.09.002.
Kondylis, F., V. Mueller, and J. Zhu, 2017: Seeing is believing? Evidence from an extension network experiment. Journal of Development Economics, 125, 1–20, https://doi.org/10.1016/j.jdeveco.2016.10.004.
Han, E., A. V. M. Ines, and W. E. Baethgen, 2017: Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture. Environmental Modelling & Software, 95, 102–114, https://doi.org/10.1016/j.envsoft.2017.06.024.
Lopez, J. R., J. M. Winter, J. Elliott, A. C. Ruane, C. Porter, and G. Hoogenboom, 2017: Integrating growth stage deficit irrigation into a process based crop model. Agricultural and Forest Meteorology, 243, 84–92, https://doi.org/10.1016/j.agrformet.2017.05.001.
Mourtzinis, S., J. I. Rattalino Edreira, S. P. Conley, and P. Grassini, 2017: From grid to field: Assessing quality of gridded weather data for agricultural applications. European Journal of Agronomy, 82, 163–172, https://doi.org/10.1016/j.eja.2016.10.013.
Negm, A., J. Jabro, and G. Provenzano, 2017: Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy. Agricultural and Forest Meteorology, 244–245, 111–121, https://doi.org/10.1016/j.agrformet.2017.05.022.
Saha, D., A. R. Kemanian, B. M. Rau, P. R. Adler, and F. Montes, 2017: Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models. Atmospheric Environment, 155, 189–198, https://doi.org/10.1016/j.atmosenv.2017.01.052.
Seyoum, S., Y. Chauhan, R. Rachaputi, S. Fekybelu, and B. Prasanna, 2017: Characterising production environments for maize in eastern and southern Africa using the APSIM Model. Agricultural and Forest Meteorology, 247, 445–453, https://doi.org/10.1016/j.agrformet.2017.08.023.
Steward, P. R., A. J. Dougill, C. Thierfelder, C. M. Pittelkow, L. C. Stringer, M. Kudzala, and G. E. Shackelford, 2018: The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields. Agriculture, Ecosystems & Environment, 251, 194–202, https://doi.org/10.1016/j.agee.2017.09.019.
Tollenaar, M., J. Fridgen, P. Tyagi, P. W. Stackhouse Jr, and S. Kumudini, 2017: The contribution of solar brightening to the US maize yield trend. Nature Clim Change, 7, 275–278, https://doi.org/10.1038/nclimate3234.

2016

Basir Khan, M. R., R. Jidin, and J. Pasupuleti, 2016: Data from renewable energy assessments for resort islands in the South China Sea. Data in Brief, 6, 117–120, https://doi.org/10.1016/j.dib.2015.11.043.
El-Bendary, N., E. Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, 2016: Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt. Procedia Computer Science, 96, 110–119, https://doi.org/10.1016/j.procs.2016.08.109.
Espe, M. B., and Coauthors, 2016: Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research, 196, 276–283, https://doi.org/10.1016/j.fcr.2016.07.011.
Espe, M. B., H. Yang, K. G. Cassman, N. Guilpart, H. Sharifi, and B. A. Linquist, 2016: Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems. Field Crops Research, 193, 123–132, https://doi.org/10.1016/j.fcr.2016.04.003.
Jain, M., A. Srivastava, Balwinder-Singh, R. Joon, A. McDonald, K. Royal, M. Lisaius, and D. Lobell, 2016: Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sensing, 8, 860, https://doi.org/10.3390/rs8100860.
Mondal, S., R. P. Singh, E. R. Mason, J. Huerta-Espino, E. Autrique, and A. K. Joshi, 2016: Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia. Field Crops Research, 192, 78–85, https://doi.org/10.1016/j.fcr.2016.04.017.
Pandey, P. K., P. P. Dabral, and V. Pandey, 2016: Evaluation of reference evapotranspiration methods for the northeastern region of India. International Soil and Water Conservation Research, 4, 52–63, https://doi.org/10.1016/j.iswcr.2016.02.003.

2015

Aramburu Merlos, F., and Coauthors, 2015: Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Research, 184, 145–154, https://doi.org/10.1016/j.fcr.2015.10.001.
van Bussel, L. G. J., and Coauthors, 2015: From field to atlas: Upscaling of location-specific yield gap estimates. Field Crops Research, 177, 98–108, https://doi.org/10.1016/j.fcr.2015.03.005.
Fichera, E., and D. Savage, 2015: Income and Health in Tanzania. An Instrumental Variable Approach. World Development, 66, 500–515, https://doi.org/10.1016/j.worlddev.2014.09.016.
Grassini, P., and Coauthors, 2015: How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research, 177, 49–63, https://doi.org/10.1016/j.fcr.2015.03.004.
Swain, R., and B. Sahoo, 2015: Variable parameter McCarthy–Muskingum flow transport model for compound channels accounting for distributed non-uniform lateral flow. Journal of Hydrology, 530, 698–715, https://doi.org/10.1016/j.jhydrol.2015.10.030.
Van Wart, J., P. Grassini, H. Yang, L. Claessens, A. Jarvis, and K. G. Cassman, 2015: Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology, 209–210, 49–58, https://doi.org/10.1016/j.agrformet.2015.02.020.

2014

Hochrainer-Stigler, S., M. van der Velde, S. Fritz, and G. Pflug, 2014: Remote sensing data for managing climate risks: Index-based insurance and growth related applications for smallhold-farmers in Ethiopia. Climate Risk Management, 6, 27–38, https://doi.org/10.1016/j.crm.2014.09.002.

2013

Schulthess, U., J. Timsina, J. M. Herrera, and A. McDonald, 2013: Mapping field-scale yield gaps for maize: An example from Bangladesh. Field Crops Research, 143, 151–156, https://doi.org/10.1016/j.fcr.2012.11.004.
van Wart, J., K. C. Kersebaum, S. Peng, M. Milner, and K. G. Cassman, 2013: Estimating crop yield potential at regional to national scales. Field Crops Research, 143, 34–43, https://doi.org/10.1016/j.fcr.2012.11.018.
White, J. W., and Coauthors, 2013: Integrated description of agricultural field experiments and production: The ICASA Version 2.0 data standards. Computers and Electronics in Agriculture, 96, 1–12, https://doi.org/10.1016/j.compag.2013.04.003.