Clyde Fraisse - Academia.edu (original) (raw)

Papers by Clyde Fraisse

ICP series on climate change impacts, adaptation, and mitigation, Sep 1, 2010

... USAID. The project was led by Drs. Goro Uehara and Fred Beinroth. Henry Nix, Joe Ritchie, JB ... more ... USAID. The project was led by Drs. Goro Uehara and Fred Beinroth. Henry Nix, Joe Ritchie, JB Dent, LA Hunt, J. Comerma, and Paul Teng served as a Technical Advisory Board during the next 10 years of this project. These ...

Computers and Electronics in Agriculture, Nov 1, 2020

European Journal of Agronomy, Sep 1, 2019

Mexico's 3.3 million tons current wheat production is projected to decline due to climate change.... more Mexico's 3.3 million tons current wheat production is projected to decline due to climate change. To counteract these negative impacts, we explored a range of plausible adaptation measures including change in crop management (early sowing and nitrogen fertilizer applications), crop genetic traits (early vigor, late flowering and heat tolerance) and wheat growing area expansion. Adaptation measures were simulated individually and in various combinations with a multi-crop model and multi-Global Climate Model ensemble across representative wheat growing regions and aggregated to national wheat production. Under both baseline (current) and future climate scenarios, most of the suggested individual and combined genetic traits resulted in a positive impact on irrigated wheat but were less beneficial in rainfed systems, with the largest responses observed with late flowering and increased N fertilizer. Increased N fertilizer applications on its own, but particularly combined with crop genetic traits showed the highest yield increase in the baseline, with further positive impacts in the future scenarios. Yield benefits from new crop genetic traits combined with increased N fertilizer applications could add about 672,000 t year −1 to national wheat production, after losing 200,000 t year −1 due to climate change by 2050s. Most effectively, expanding wheat to include all areas where wheat was previously grown during the last two decades could add 1.5 million t year −1 now and 1.2 million t year −1 in the future. Breeding for new crop genetic traits will reduce some of the negative impacts from future climate change, but improved cultivars need to be implemented with suitable crop management, especially N fertilizer management. ha −1 due to high temperature and uneven rainfall distribution, but also low applications of N fertilizer (Marquez-Berber et al., 2014; SAGARPA, 2017). Wheat production faces the challenge of climate change, particularly due to increasing global temperatures (Asseng et al., 2015). By mid-century, under the highest emission scenario (RCP 8.5), daily temperatures during the winter are projected to increase between 2.5 to 3.2°C for maximum temperature, and by 0.9 to 2.4°C for minimum temperature, depending on the Global Climate Model (GCM) (IPCC, 2013). Similar temperature trends are projected for the summer season, when rainfed production occurs. As a result, wheat production in Mexico is estimated to decline by up to 7.9% by the 2050s (Hernandez

DOAJ (DOAJ: Directory of Open Access Journals), 2013

Agrometeoros, Nov 7, 2016

Computers and Electronics in Agriculture, 2019

Agricultural Systems, Mar 1, 2014

EDIS, 2018

This 5-page publication details a new tool available to growers and Extension professionals to ma... more This 5-page publication details a new tool available to growers and Extension professionals to manage risks related to climate during seasonal planning stages. The Crop Season Planning tool is a climate-based tool that enables growers to plan planting strategies that will minimize risk to climate extremes based on historical climate data at their location. Written by Caroline G. Staub, Daniel Perondi, Diego Noleto Luz Pequeno, Patrick Troy, Michael J. Mulvaney, Calvin Perry, Brian Hayes, Willingthon Pavan, and Clyde W. Fraisse, and published by the UF/IFAS Department of Agricultural and Biological Engineering, March 2018. http://edis.ifas.ufl.edu/ae525

International Journal of Climatology, 2016

Computers and Electronics in Agriculture, 2011

Agricultural Systems, 2014

Agronomy Journal, 2004

different management zones within a field? Two, how can information be processed into unique mana... more different management zones within a field? Two, how can information be processed into unique management Producers using site-specific crop management (SSCM) have a units (i.e., procedures for classification)? And three, how need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management many unique zones should a field be divided into? Quick, zones. Quick and automated procedures are desirable for creating efficient, and automated procedures are needed that admanagement zones and for testing the question of the number of zones dress these questions. to create. A software program called Management Zone Analyst A number of information sources have been used to (MZA) was developed using a fuzzy c-means unsupervised clustering delineate subfield management zones for SSCM. Tradialgorithm that assigns field information into like classes, or potential tional soil surveys often provide estimates of crop promanagement zones. An advantage of MZA over many other software ductivity for each soil map unit. In the USA, county programs is that it provides concurrent output for a range of cluster soil surveys report the average yield of major crops and numbers so that the user can evaluate how many management zones various soil properties by soil map unit; but the spatial should be used. Management Zone Analyst was developed using Microscale of county soil surveys has often been found inadesoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are pre-

Water demand for irrigation in the Southeast is expected to increase in the future. There is a ne... more Water demand for irrigation in the Southeast is expected to increase in the future. There is a need to combine climate information and risk analysis for peanut irrigation in the southeastern US. This paper describes a peanut irrigation decision support system which was developed to assist growers and to provide information on the levels of profitability of peanut production with and without irrigation under different climate forecasts. The system provides probability distributions of the seasonal cost to irrigate peanuts and amount of water required. Yields were simulated for both irrigated and non-irrigated peanuts using the CSM-CROPGRO-Peanut model. Results of a case study were presented for the Georgia Green variety grown in Miller County, Georgia. The probability of obtaining a high net return under irrigated conditions increased when planting dates were delayed for El Niño years. Dryland peanut production was profitable in a La Niña year if peanuts were planted between mid-April and early May. The prototype irrigation decision support system will be deployed as a web-based tool on the AgClimate web site (www.AgClimate.org) after additional testing and evaluation.

The mission of the Southeast Climate Consortium (SECC) is to provide climate information and deci... more The mission of the Southeast Climate Consortium (SECC) is to provide climate information and decision support tools to help stakeholders manage risks arising from climate variability. While the SECC has pursued this mission with a focus on seasonal climate since 1998, in response to stakeholder interest we began developing climate change information to support decision making in 2007. In this

Predictability of seasonal climate variability associated with the El Niño Southern Oscillation (... more Predictability of seasonal climate variability associated with the El Niño Southern Oscillation (ENSO) suggests a potential to reduce farm risk by selecting crop insurance products with the purpose of increasing farm income stability. A hypothetical 50% peanut, 50% cotton, non−irrigated, 40 ha (100 ac) north Florida farm was used to study the interactions of different crop insurance products with ENSO−based climate information and levels of risk aversion under uncertain conditions of climate and prices. Crop yields simulated by the DSSAT suite of crop models using multi-year weather data combined with historical series of prices were used to generate long series of stochastic income distributions in a whole−farm model portfolio. The farm model optimized planting dates and simulated uncertain incomes for 50 alternative crop insurance combinations for different levels of risk aversion under different planning horizons. Results suggested that incomes are greatest and most stable for lo...