Patricio Grassini

Patricio Grassini

Patricio Grassini

Assistant Professor

B.Sc. (Agricultural Engineer). University of Buenos Aires, Argentina (2005)

Ph.D. (Agronomy). University of Nebraska-Lincoln (2010)

Area of Focus

Agronomy

Research Interests

  • Crop physiology
  • Yield potential
  • Yield-gap analysis
  • Resource- and Energy-use efficiency
  • Crop simulation models

Major Project Activities

I have performed yield-gap and resource-use efficiency analysis in a diverse range of cropping systems, ranging from rainfed crops in Sub-Saharan Africa to high-yield irrigated maize-soybean systems in the U.S. Corn Belt. I am currently involved in two major projects:

1. Global Yield Gap and Water Productivity Atlas. The global community must find a way to provide food and water security for a population expected to reach 9 billion by 2050. Global carrying capacity for food production and our ability to protect carbon-rich and biodiverse natural ecosystems from conversion to cropland ultimately depend on achieving maximum possible yields on every acre of currently used arable land. Yet for most major crop producing regions of the world, there are no reliable data on yield potential.

Water resources to support rainfed and irrigated agriculture also are under pressure, making the efficiency with which water is converted to food, water productivity, another critical benchmark. The target is to find the exploitable yield gap -the difference between current average farm yields and yield potential (van Ittersum and Rabbinge, 1997; Field Crop Res. 52:197-208). Accurate estimates of yield potential, yield gaps, and water productivity are critical to increase yields and make the best use of land and water resources. The Global Yield Gap and Water Productivity Atlas will provide a web-based platform for estimating yield potential, yield gaps, and water productivity that is transparent, accessible, reproducible, geospatially explicit, agronomically robust and applied in a consistent manner throughout the world.

The Atlas will enable farmers, governments, policy makers, foundations, the private sector, and others to: (i) identify regions with the greatest potential to increase food supply and water use efficiency; (ii) provide input to economic global models that assess food security and land use; (iii) prioritize research investments, and (iv) evaluate the impact of climate change and other issues that deal with water, food, and weather. Initial efforts focus on Sub-Saharan Africa (Burkina Faso, Ghana, Mali, Niger, Nigeria, Ethiopia, Kenya, Tanzania, Uganda, and Zambia), Middle East (Morocco, Tunisia, and Jordan), south Asia (India and Bangladesh), South America (Argentina and Brazil), Australia, Europe, and the United States. The Bill & Melinda Gates Foundation, the Robert B. Daugherty Water for Food Institute at the University of Nebraska, USAID, and Wageningen University  are funding the atlas development.

2. Benchmarking yield and input-use efficiency of corn-soybean systems in Nebraska. Though it has been speculated that the efficiency with which applied inputs result in increased yield can be greater in intensively managed high-yield cropping systems than in their low-input low-yield counterparts due to optimization of growing conditions in the former (de Wit, 1992; Agric. Sys. 40:125-151), this hypothesis has not been evaluated in actual cropping systems where farmer's yields approach yield potential. Corn and soybean production is expected to increase to satisfy the increasing demand for food, biodiesel, and livestock feed, both in the USA and globally.

Likewise, there is concern about future water availability for agriculture due to the depletion of surface and groundwater resources, increasing competition by the domestic and industrial sectors for water, and the impact of climate change on precipitation patterns. Hence, it is necessary to orient research towards identification of management practices that allow achieving high yield with high efficiency, and without sacrificing producer profit. Nebraska is the third and fifth largest corn- and soybean-producing state, respectively. Irrigated crop production accounts for a respective 45 and 55% of NE land area and production of these two crops.

As a complement to the more traditional research approach of running experiments in experimental stations or a limited number of farms, in this project we built collaborative links with 20 out of the 23 Natural Resources Districts (NRD), which provided us with geo-referenced yield and applied inputs data (irrigation water, N fertilizer) collected annually from 10,000+ farmers' fields in Nebraska during the last 10 years. We also conducted a survey across 1000+ farms in NE to collect more detailed field-specific management data such as planting dates, crop varieties, tillage, fertilizer use, and incidence of biotic constrains.

We are using these data to (i) diagnose current yields, yield gaps, and input-use efficiency of irrigated and dryland crop systems in Nebraska, (ii) identify opportunities for improving yields and efficiencies by identification of underpinning causes of yield gaps and inefficiencies and assessment of management practices that lead to higher yield, efficiency and profit, and (iii) evaluate the water, energy, and greenhouse-gas emission (GHG) footprint at the field and regional level.

Extension Interests

I actively participate in extension activities, including UNL Crop Extension Clinics, NRD-sponsored workshops, and training sessions on the use of crop simulation models as decision-support tools to optimize crop management and producer profit, and write extension publications, including CropWatch articles, Crop Production Clinic Proceedings, and Extension Circulars. I also perform bi-weekly, in-season, real-time forecasting of irrigated and rainfed end-of season corn yield potential for several locations across the US Corn Belt , which are periodically published as CropWatch articles during the crop season. Simulations are performed using the UNL Hybrid-Maize models based on actual weather, soil, and management practices for each of the locations.

To predict end-of season yield, Hybrid-Maize uses observed weather data until the date of the yield forecast and historical weather data to predict the rest of the season. This gives a range of possible end-of-season yields. By comparing this range of simulated end-of-season yields against the long-term average simulated yield, we can estimate the likelihood of below-, near-, or above-average yields and the magnitude of difference from average yields. This information can aid crop producers, consultants and ag industry for making in-season decisions on crop management, farm logistics, and grain marketing.

Recognition/Awards

  • Agronomy Society of America (ASA) Early Career Award (2016)
  • Member of Science Advisory Council for Field to Market (since May 2016)
  • Junior Faculty Excellence in Research Award. UNL (2015)
  • Member of the Editorial Advisory Board of Field Crops Research journal
  • Water for Food Institute Fellow
  • Center for Great Plains Studies Fellow
  • ASABE Blue Ribbon Award, Educational Aids Competition (2013)
  • Fulbright Scholar (2007-2009)
  • Maude Hammond Fling Fellowship. UNL (2009-2010)
  • Widaman Distinguished Graduate Assistant Award. UNL (2009)
  • Gerald O. Mott Meritorious Graduate Student Award in Crop Science. Agronomy Society of America (2009).
  • William J. Curtis Endowed Fellowship. UNL (2007)
  • Diploma of Honor. University of Buenos Aires (2006)

Publications

H-index: 17; total citations: 1,170 (by June 2016)

  • Morell FJ, Yang HS, Cassman KG, Van Wart J, Elmore RW, Licht M, Coulter JA, Ciampitti IA, Pittelkow CM, Brouder SM, Thomison P, Lauer J, Graham C, Massey R, Grassini P, 2016. Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt? Field Crops Res. 192, 1-12.
  • Gobett D, Hochman Z, Horan H, Navarro-Garcia J, Grassini P, Cassman KG,  2016. Yield gap analysis of rainfed wheat demonstrates local to global relevance. Journal of Agric. Sci. (Cambridge) (Accepted)
  • Zanon AJ, Steck NA, Grassini P, 2016. Climate and management factors influence soybean yield potential in a subtropical environment. Agron. J. 108, 1-8.
  • Marin F, Martha G, Cassman KG, Grassini P, 2016. Prospects for increasing sugarcane and bioethanol production on existing crop area in Brazil. BioScience 66, 307-316.
  • Farmaha BS, Lobell DB, Boone K, Cassman KG, Yang, SH, Grassini P, 2016. Contribution of persistent factors to yield gaps in high-yield irrigated maize. Field Crops Research 186, 124-132.
  • Aramburu Merlos F., Monzon JP, Mercau JL, Taboada, M, Andrade, FH, Hall AJ, Jobbagy E, Cassman KG, Grassini P, 2015. Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Research 184, 145-154.
  • Makowski D, Asseng S, Ewert F, Bassu S., Durand JL, Lie T, Martre P, Adamh M, Aggarwal PK, Angulo C, Baron C, Basso B, Bertuzzi P, Biernath C, Boogaard H, Boote KJ, Bouman B, Bregaglio S, Brisson N, Buis S, Cammarano D, Challinorr AJ, Confalonieri R, Conijnu JG, Corbeels M, Deryng  D, De Sanctis G, Doltra J, Fumoto T, Gaydon D, Gayler S, Goldberg R, Grant RF, Grassini P, et al., 2015.  A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agric. Forest Meteoro. 214, 483-493. 
  • Grassini P, Torrion JA, Yang HS, Rees J, Andersen D, Cassman KG, Specht JE, 2015. Soybean yield gaps and water productivity in the western U.S. Corn Belt. Field Crops Res. 179, 150-163.
  • Van Wart J, Grassini P, Yang HS, Claessens L, Jarvis A, Cassman KG, 2015. Creating long-term weather data from thin air for crop simulation modelling. Agric. Forest Meteoro. 208, 49-58.  LINK: http://www.sciencedirect.com/science/article/pii/S0168192315000696
  • Grassini P, Van Bussel LGJ, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, Van Ittersum MK, Cassman KG, 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res. 177, 49-63. LINK: http://www.sciencedirect.com/science/article/pii/S0378429015000866
  • Van Bussel LGJ, Grassini P, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, Saito K, Cassman KG, Van Ittersum MK, 2015. From field to atlas: Upscaling of location-specific yield gap estimates. Field Crops Res. 177, 98-108. 
    LINK: http://www.sciencedirect.com/science/article/pii/S0378429015000878
  • Kranz W, Burr C, Farmaha B, Grassini P, Hergert G, Irmak I, Martin D, Nygren A, Shapiro C, Shaver T,  Zoubek G. 2015. Irrigation and Nitrogen Management: User Education/Certification Program. EC2008 Nebraska Extension Division. 117 pp.
  • Grassini P, Morell Soler F, Yang H, Cassman KG, Elmore R, Rees J, Glewen K, Shapiro C, Kruger G. 2015. In-season yield forecasting using a computer simulation model. 2015 Crop Production Clinics Proceedings. University of Nebraska-Lincoln - Extension. LINK: documents/15-5010-2015%20Crop%20Production%20Clinic.pdf
  • Sadras V, Cassman KG, Grassini P, Hall AJ, Bastiaansen, WGM, Laborte AG, Milne AE, Sileshi G, Steduto P, 2015. Yield gap analysis of rainfed and irrigated crops: Methods and case studies. In: FAO Water Reports 41. Food and Agriculture Organization of the United Nations (FAO), Rome. LINK: http://waterforfood.nebraska.edu/wp-content/uploads/2015/06/FAO_WR41FINAL_low.pdf
  • Sadras VO, Grassini P, Costa R, Cohan L, Hall AJ, 2014. How reliable are crop production data? Case studies in USA and Argentina. Food Security 6, 447-459.
  • Sadras VO, Grassini P, Costa R, Cohan L, Hall AJ. (2014). How reliable are crop production data? Case studies in USA and Argentina. Food Security DOI 10.1007/s12571-014-0361-5
  • Grassini P, Torrion JA, Cassman KG, Yang HS, Specht JE., (2014). Drivers of spatial and temporal variation in soybean yield and irrigation requirements. Field Crops Res. 163, 32-46
  • Bassu S, Brisson N, Durand J-L, Boote K, Lizaso J, Jones JW, Rosenzweig C, Ruane AC, Adam M, Baron C, Basso B, Biernath C, Boogaard H, Conijn S, Corbeels M, Deryng D, De Sanctis G, Gayler S, Grassini P, Hatfield J, Hoek S, Izaurralde C, Jongschaap R, Kemanian AR, Kersebaum KC, Kim S-H, Kumar NS, Makowski D, Mø¼ller C, Nendel C, Priesack E, Pravia MV, Sau F, Shcherbak I, Tao F, Teixeira E, Timlin D, Waha K. (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology 20, 2301-2320.
  • Grassini P., Eskridge K., Cassman K.G. (2014) Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4:2918 | DOI: 10.1038/ncomms3918.  LINK: http://www.nature.com/ncomms/2013/131217/ncomms3918/full/ncomms3918.html
  • Specht, J.E., Diers, B.W., Nelson, R.L., Toledo, J.F., Torrion, J.A., Grassini, P., (2014). Soybean (Glycine max (L.) Merr.). In: Smith, J.S.C, Carver, B., Diers, B.W., Specht, J.E. (Eds.), Yield Gains in Major US Field Crops: Contributing Factors and Future Prospects. CSSA Special Publication #33, ASA-CSSA-SSSA, Madison, WI.
  • Sibley A.M., Grassini P., Thomas N.E., Cassman K.G., Lobell D.B. (2014) Testing remote sensing approaches for assessing yield variability among maize fields. Agron J 106:24-32. LINK: https://dl.sciencesocieties.org/publications/aj/abstracts/106/1/24
  • Cassman KG, Grassini P, (2013). Can there be a green revolution in Sub-Saharan Africa without large expansion of irrigated crop production? Global Food Sec. 2, 203-209.
  • Van Wart J., Grassini P., Cassman K.G. (2013) Impact of derived global weather data on simulated crop yields.. Global Change Biology 19:3822-3834. LINK: http://onlinelibrary.wiley.com/doi/10.1111/gcb.12302/abstract
  • Van Ittersum M.K., Cassman K.G., Grassini P., Wolf J., Tittonell P., Hochman Z. (2013) Yield gap analysis with local to global relevance - a review. Field Crops Research 143:4-17. LINK: http://www.sciencedirect.com/science/article/pii/S037842901200295X
  • Van Wart J., Van Bussel L.G.J., Wolf J., Licker R., Grassini P., Nelson A., Boogaard H., Gerber J., Muelle N.D., Claessens L., Cassman K.G., Van Ittersum M.K. (2013) Reviewing the use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research 143:44-55. LINK: http://www.sciencedirect.com/science/article/pii/S0378429012004121
  •  Grassini P., Yang H., Irmak S., Rees J., Burr C., Cassman K.G. (2012) Evaluation of water productivity and irrigation efficiency in Nebraska corn production. Extension Circular 105. University of Nebraska-Lincoln.
  • Grassini P., Yang H., Irmak S., Rees J., Burr C., Cassman K.G. (2012) Yield gaps and input-use efficiency of high-yield irrigated corn in Nebraska. Extension Circular 106. University of Nebraska-Lincoln.
  • Grassini P., Cassman K.G. (2012) High-yield maize with large net energy yield and small global warming intensity. Proceedings of the National Academy of Sciences (PNAS) 109:1074-1079. LINK: http://www.pnas.org/content/109/4/1074.full
  • Irmak S., Burgert M.J., Yang H.S., Cassman K.G., Walters D.T., Rathje W.R., Payero J.O., Grassini P., Kuzila M.S., Brunkhorst K.J., Van DeWalle B., Rees J.M., Kranz W.L., Eisenhauer D.E., Shapiro C.A., Zoubek G.L., Teichmeier G.J. (2012) Large scale on-farm implementation of soil moisture-based irrigation management strategies for increasing maize water productivity. Transactions of the ASABE 55:881-894.
  • Sadras V, Grassini P., Steduto P. (2011) Status of water use efficiency of main crops. In: The state of world's land and water resources for food and agriculture (SOLAW). FAO, Rome and Earthscan, London.
  • Grassini P., Thorburn J., Burr C., Cassman K.G. (2011). High-yield irrigated maize in the Western U.S. Corn-Belt: I. On-farm yield, yield-potential, and impact of management practices. Field Crops Res. 120:142-150.
  • Grassini P., Yang, H., Irmak S., Thorburn J., Burr C., Cassman K.G. (2011) High-yield irrigated maize in the Western U.S. Corn-Belt: II. Irrigation management and crop water productivity. Field Crops Res 120:133-141.
  • Cassman K.G., Grassini P., van Wart J. (2010) Global food security in a changing climate: crop yield trends and yield potential. In: Hillel D., Rosenzweig C. (Eds), Handbook of climate change and agroecosystems: impacts, adaptation, and mitigation. Imperial College Press, London, UK, pp. 37-51.
  • Grassini P., You J., Hubbard K.G., Cassman K.G. (2010) Soil water recharge in a semi-arid template climate of the Central US Great Plains. Agric. Water Manage. 97:1063-1069.
  • Grassini P., Yang H.S., Cassman K.G. (2009) Limits to maize productivity in Western Corn Belt: a simulation analysis for fully-irrigated and rainfed conditions. Agric For Meteor 149:1254-1265.
  • Grassini P., Hunt E., Mitchell R., Weiss A. (2009) Simulating switchgrass growth and development under potential and water limiting conditions. Agron J 101:564-571.
  • Grassini P., Hall A.J., Mercau J.L. (2009) Benchmarking sunflower water productivity in semiarid environments. Field Crops Res 110:251-262.
  • Grassini P., Indaco G.V., López Pereira M., Hall A.J., Trápani N. (2007) Physiological responses to temporary waterlogging during grain filling in sunflower. Field Crops Res 101:353-363.