Growing degree days – Ecosystem indicator for changing diurnal temperatures and their impact on corn growth stages in Kansas

Abstract

Understanding how climate change will affect plant phenology (shifts in the timing of plant activity) is central to many ecological and biogeochemical studies. This aspect of plant ecology often has been overlooked, but addressing the consequences of climate change for adaptive/mitigative management is now high on the list of priorities for funding agencies. This study is innovative because it uses growing degree days (GDD), which has existed since the 1730s, as an ecosystem indicator to study changing diurnal temperatures; their effects on different plant growth stages in the last century; and as a basis for development of future adaptive management strategies. Our results show the most recent time period (1980-2009) had the earliest emergence and the least variability among stations in the day at which the crop stage occurred for most stages except emergence and physiological maturity. 100 year linear trends in the stations indicated all seven crop stages except tassel initiation occurred earlier by one day per decade during the study period. The number of stations with significant decreases varied from 11 to 17 stations out of 23 stations in Kansas. Tassel initiation stage occurred later by one day per decade during the study period. The most recent time period (1980-2009) had the highest variability among stations and 30 year time periods. The variability in trends are higher in western Kansas when compared to eastern Kansas. This knowledge has transformative potential to improve our understanding of the occurrence and duration of the different plant growth stages, add local precision to earlier findings for changes in overall GDD that encompassed larger areas, and help explain the differences in trends from some earlier studies. These shifts in the phenology of agricultural plants as a result of climate change have implications for required increases in food production to feed the growing population.

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

“…They are expressing the need for climate information that can support adaptation-related decision-making, provide straightforward estimations of variability, and be tailored to specific user groups (Mastrandrea et al, 2010), so stakeholders can make informed decisions that lead to economic development. Addressing the consequences of climate change for adaptive and/or mitigative management is now high on the list of priorities for funding agencies (Anandhi, 2015), particularly in developed countries. However, funding is a significant challenge in many Asian cities (Porse, 2013) and developing countries.…”

“…• Increasing temperatures can impact the phenology of plants and insects (e.g., shifts in the timing of activity; Anandhi, 2015). Specific growth stages (e.g., flowering, pollination, and grain filling) are particularly sensitive to weather conditions and critical for final yield (Lavalle et al, 2009).…”

A system’s approach to assess the exposure of agricultural production to climate change and variability

“…The potential evapotranspiration rate calculated from weather variablesalso could be applied as a CF because it represents the integrated effects of changes in water availability and temperature on plant growth. Growing degree days (Anandhi 2016) and length or duration of warm/cold/wet/dry spells ) during specific growth stages can also CFs. Warm nights and hot or cold days could also be selected as CFs because of their effects on crop yield (Prasad et al 1999;Wheeler et al 2000;Prasad et al 2008).…”

Climate change effects on the processing tomato growing season in California using growing degree day model

“…In California, Zalom and Wilson (1999) evaluated GDD model with different base temperature and upper cutoff limits and found that the combination of 10 °C base temperature and 30 °C upper cutoff provided optimum match with observed growth stages in processing tomatoes. Apart from predicting various growth stages, GDD model can provide helpful prediction of time to maturity and length of the total growing season (Anandhi 2016;Castillo and Gaitán Ospina 2016;Machado et al 2004;Saadi et al 2015). The ability to predict maturity and identifying trends over the future could assist in many important strategic and management decisions such as labor management, linking with tomato processors, and long-term water management based on the length of the growing season trend and GDD accumulations.…”

“…These are often overlooked aspects of plant ecology (Cleland et al 2007;Anandhi 2016;Darand and Mansouri Daneshvar 2015) and have important implications for agricultural production. Study done by Saadi et al (2015) show that air temperature is foreseen to have a dominant role on the shortening and anticipation of the tomato growing cycle over the Mediterranean region.…”