Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Computers and Electronics in Agriculture, 178.
Abstract: Predicting crop yields and their spatio-temporal variability under a changing climate is a challenging but essential undertaking for crop management and policymaking purposes. The availability of information on risks associated with effects of climatic variability on agricultural activity outcomes is critical for stakeholders ranging from individual landowners to national economists alike. This research was conducted as a pilot study to (1) develop satellite remote sensing based estimates of maize acreage in a typical Maize growing region in Pakistan, (2) to develop a statistical-empirical model for prediction of maize yields, and finally, (3) to assess the influence of temperature on inter-annual variability in maize yields across a decade. A total of eight machine learning algorithms were tested for identifying maize growing operations in the Faisalabad district of Pakistan using Landsat 8 imagery. Classification models were evaluated via 200 randomly selected ground-verified points across the study region. Results of the maize mapping exercise were used to estimate interannual maize yields using Landsat-derived multi-temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) data as predictors. Predictors for the yield forecasting model were selected via principal component screening and were fed into a least absolute shrinkage and selection (LASSO) regression model. The yield model thus developed was applied to 10 years of past data (2006-2017) and validated against data recorded by government sources. Finally, predictions spanning the ten years were tested for effects of temperature variability to find evidence of influence of ambient temperature on maize yields. Results indicate that support vector machine classifiers work the best in this landscape (accuracies >90%) and reveal that maize cropping area may be underestimated in government sources by as much as 14%. The LASSO regression models also showed very good fits (validation R2 = 0.95) and were fairly accurate in tracking interannual variations in maize yields (R2 = 0.78.) Results also indicate that the maximum temperature has significant negative influence (R2 = 0.76, P < 0.0001) on maize yields in Faisalabad district. Methods presented in this study should be of use to policymakers for better formulating export-import policies and decisions governing food security issues in the larger region.
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Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall: Ocean mean temperature and Indian summer monsoon rainfall. Atmos. Sci. Lett., 16(3), 408–413.
Abstract: Besides improving the understanding of the physics of the challenging problem of monsoon prediction, it is necessary to evaluate the efficiency of the input parameters used in models. Sea-surface temperature (SST) is the only oceanographic parameter applied in most of the monsoon forecasting models, which many times do not represent the heat energy available to the atmosphere. We studied the impacts of ocean mean temperature (OMT), representing the heat energy of the upper ocean, and SST on the all India summer monsoon rainfall through a statistical relation during 1993�2013 and found that OMT has a better link than SST.
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Alza, C. M., Donnelly, M. A., & Whitfield, S. M. (2016). Additive effects of mean temperature, temperature variability, and chlorothalonil to red-eyed treefrog (Agalychnis callidryas) larvae: Temperature variability and chlorothalonil toxicity. Environ Toxicol Chem, 35(12), 2998–3004.
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Anandhi, A., & Blocksome, C. E. (2017). Developing adaptation strategies using an agroecosystem indicator: Variability in crop failure temperatures. Ecological Indicators, 76, 30–41.
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Angelo, C. L., & Daehler, C. C. (2015). Temperature is the major driver of distribution patterns for C4 and C3 BEP grasses along tropical elevation gradients in Hawaii, and comparison with worldwide patterns. Botany, 93(1), 9–22.
Abstract: The distribution patterns of C4 and C3 grasses in relation to climate have attracted much attention, but few studies have examined grass distributions along tropical elevation gradients. Previous studies identified either temperature, precipitation, or both variables as the major climatic factor(s) driving these distributions. Here we investigated relative dominance of C4 grasses in relation to climate along five elevation gradients in Hawai�i. The transition temperature between C4 and C3 BEP (Bambusoideae, Ehrhartoideae, and Pooideae) grasses (where their relative dominance is equal) was determined; in our study, the subfamily Bambusoideae was not included. A worldwide synthesis of previous studies testing climatic factors and transition temperatures associated with C4 and C3 grass distributions was also carried out. Mean July maximum temperature was significantly correlated with C4 dominance along all elevation transects in Hawai�i, while precipitation was only correlated along three transects when precipitation was correlated with temperature. A spatially explicit multiple regression model indicated that C4 relative cover was best explained by temperature. Temperature appears to be the major climatic factor shaping distribution patterns of C4 and C3 BEP grasses in Hawai�i. According to the worldwide analysis, temperature primarily influenced grass distribution patterns more often in temperate studies (70%) than in tropical studies (45%). Degree of correlation or covariance between temperature and precipitation was rarely reported in previous studies, although this can strongly affect conclusions. C4-C3 BEP transition temperatures (mean July maximum) ranged from 18 to 21 °C in Hawai�i; these transition temperatures are lower than those reported in temperate localities (26�31 °C), but similar to transition temperatures for other localities at tropical latitudes (21�22 °C). A warming climate is likely to shift C4 grass dominance upward in elevation, threatening higher elevation native communities by perpetuating a grass�fire cycle.
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Asseng, S., Cammarano, D., Basso, B., Chung, U., Alderman, P. D., Sonder, K., et al. (2017). Hot spots of wheat yield decline with rising temperatures. Glob Change Biol, 23(6), 2464–2472.
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Ayankojo, I. T., Ayankojo IT, Morgan, K. T., & Morgan KT. (2020). Increasing Air Temperatures and Its Effects on Growth and Productivity of Tomato in South Florida. Plants (Basel), 9(9), 1245.
Abstract: Florida ranks first among US states in fresh-market tomato production with annual production exceeding one-third of the total annual production in the country. Although tomato is a signature crop in Florida, current and future ambient temperatures could impose a major production challenge, especially during the fall growing season. This problem is increasingly becoming an important concern among tomato growers in south Florida, but studies addressing these concerns have not been conducted until now. Therefore, this study was conducted to determine the impacts of the present ambient temperature conditions and planting dates on tomato productivity in south Florida. The study was conducted using crop simulation model CROPGRO-Tomato of DSSAT (Decision Support System for Agricultural Transfer) version 4.7. Five treatments were evaluated, and included AT (simulated treatment using 14 years of actual daily weather conditions at the study location) while other treatments were conducted based on a percentage (-20%, -10%, +10%, +20%) of AT to simulate cooler and warmer temperature regimes. The results suggested that under the current temperature conditions during the fall growing season in south Florida, average tomato yield was up to 29% lower compared to the cooler temperature regimes. Tomato yield further decreased by 52% to 85% at air temperatures above the current condition. Yield reduction under high temperature was primarily due to lower fruit production. Contrary to yield, both tomato biomass accumulation and leaf area index increased with increase in temperature. Results also indicated that due to changes in air temperature pattern, tomato yield increased as planting date increased from July to December. Therefore, planting date modification during the fall season from the current July-September to dates between November and December will reduce the impacts of heat stress and increase tomato productivity in south Florida.
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Balling Jr., R. C., Kiany, M. S. K., & Roy, S. S. (2016). Anthropogenic signals in Iranian extreme temperature indices. Atmospheric Research, 169, 96–101.
Abstract: We analyzed spatial and temporal patterns in temperature extremes from 31 stations located throughout Iran for the period 1961 to 2010. As with many other parts of the globe, we found that the number of days (a) with high maximum temperatures was rising, (b) with high minimum temperatures was rising, and (c) with low minimum temperatures was declining; all of these trends were statistically significant at the 0.05 level of confidence. Population records from 1956 to 2011 at the station locations allowed us to reveal that the rate of human population growth was positively related to the increase in the number of days with high maximum temperatures and negatively related to days with low maximum temperatures. Our research shows a number of identifiable anthropogenic signals in the temperature records from Iran, but unlike most other studies, the signals are stronger with indices related to maximum, not minimum, temperatures.
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Barker, B. D., Horodysky, A. Z., & Kerstetter, D. W. (2018). Hot or not? Comparative behavioral thermoregulation, critical temperature regimes, and thermal tolerances of the invasive lionfish Pterois sp. versus native western North Atlantic reef fishes. Biol Invasions, 20(1), 45–58.
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Barreca, A., Deschenes, O., & Guldi, M. (2018). Maybe Next Month? Temperature Shocks and Dynamic Adjustments in Birth Rates. Demography, 55(4), 1269–1293.
Abstract: We estimate the effects of temperature shocks on birth rates in the United States between 1931 and 2010. We find that days with a mean temperature above 80 degrees F cause a large decline in birth rates 8 to 10 months later. Unlike prior studies, we demonstrate that the initial decline is followed by a partial rebound in births over the next few months, implying that populations mitigate some of the fertility cost by shifting conception month. This shift helps explain the observed peak in late-summer births in the United States. We also present new evidence that hot weather most likely harms fertility via reproductive health as opposed to sexual activity. Historical evidence suggests that air conditioning could be used to substantially offset the fertility costs of high temperatures.
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Barreras, H. J., Barreras H Jr, Kelly, E. A., Kelly EA, Kumar, N., Kumar N, et al. (2019). Assessment of local and regional strategies to control bacteria levels at beaches with consideration of impacts from climate change. Mar Pollut Bull, 138, 249–259.
Abstract: The objective of this study was to evaluate relationships between local factors (beach geomorphology and management) and regional factors (infrastructure improvements and temperature changes) against levels of fecal indicator bacteria (FIB) at recreational beaches. Data were evaluated for 17 beaches located in Monroe County, Florida (Florida Keys), USA, including an assessment of sanitary infrastructure improvements using equivalent dwelling unit (EDU) connections. Results show that elevated FIB levels were associated with beach geomorphologies characterized by impeded flow and by beaches with lax management policies. The decrease in EDUs not connected coincided with a decrease in the fraction of days when bacteria levels were out of compliance. Multivariate factor analysis also identified beach management practices (presence of homeless and concession stands) as being associated with elevated FIB. Overall, results suggest that communities can utilize beach management strategies and infrastructure improvements to overcome the negative water quality impacts anticipated with climate change.
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Bassu, S., Brisson, N., Durand, J. - L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors? Glob Change Biol, 20(7), 2301–2320.
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
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Battisti, R., Sentelhas, P. C., & Boote, K. J. (2018). Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. Int J Biometeorol, 62(5), 823–832.
Abstract: Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 degrees C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha(-1) for the ensemble at + 6 degrees C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.
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Bjorndal, K. A., Bolten, A. B., Chaloupka, M., Saba, V. S., Bellini, C., Marcovaldi, M. A. G., et al. (2017). Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Glob Change Biol, 23(11), 4556–4568.
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Bjorndal, K. A., Chaloupka, M., Saba, V. S., Diez, C. E., van Dam, R. P., Krueger, B. H., et al. (2016). Somatic growth dynamics of West Atlantic hawksbill sea turtles: a spatio-temporal perspective. Ecosphere, 7(5), e01279.
Abstract: Somatic growth dynamics are an integrated response to environmental conditions. Hawksbill sea turtles (Eretmochelys imbricata) are long-lived, major consumers in coral reef habitats that move over broad geographic areas (hundreds to thousands of kilometers). We evaluated spatio-temporal effects on hawksbill growth dynamics over a 33-yr period and 24 study sites throughout the West Atlantic and explored relationships between growth dynamics and climate indices. We compiled the largest ever data set on somatic growth rates for hawksbills -3541 growth increments from 1980 to 2013. Using generalized additive mixed model analyses, we evaluated 10 covariates, including spatial and temporal variation, that could affect growth rates. Growth rates throughout the region responded similarly over space and time. The lack of a spatial effect or spatio-temporal interaction and the very strong temporal effect reveal that growth rates in West Atlantic hawksbills are likely driven by region-wide forces. Between 1997 and 2013, mean growth rates declined significantly and steadily by 18%. Regional climate indices have significant relationships with annual growth rates with 0- or 1-yr lags: positive with the Multivariate El Nino Southern Oscillation Index (correlation = 0.99) and negative with Caribbean sea surface temperature (correlation = -0.85). Declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs.
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Bladow, R. A., & Milton, S. L. (2019). Embryonic mortality in green (Chelonia mydas) and loggerhead (Caretta caretta) sea turtle nests increases with cumulative exposure to elevated temperatures. Journal of Experimental Marine Biology and Ecology, 518.
Abstract: As climate change continues, sea turtle nests will be increasingly exposed to elevated incubation temperatures. Higher incubation temperatures influence many aspects of sea turtle development including sex determination and incubation length, but also survival. If temperatures in the nest increase above a thermal tolerance limit, then embryonic mortality may increase. The purpose of this research was to determine if there are differences in vulnerability to elevated temperatures across different stages of embryonic development and between loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtles. Temperature dataloggers recorded nest temperature in the approximate center of loggerhead and green nests laid on the Boca Raton, Florida beach during the 2016 and 2017 nesting seasons. All unhatched eggs were collected from these nests following hatchling emergence. The eggs were dissected and the developmental stage at embryonic death was determined. The point of embryonic death was compared to the nest temperatures during that stage of the incubation period to determine if death corresponded to specific periods of elevated temperatures. Elevated nest temperatures increased embryonic mortality, but no developmental stage had higher mortality rates when exposed to any specific elevated temperatures compared to embryos that had not been exposed to that temperature. The most significant relationship was between mortality and the percent of time embryos were exposed to temperatures above 34 degrees C. This resulted in greater mortality of more developed embryos, as those embryos had a longer cumulative exposure to elevated temperatures. Loggerhead turtles exhibited higher rates of mortality compared to green turtles for almost all temperature exposure periods above 34 degrees C. Although few green nests reached 34 degrees C, green sea turtle embryos in south Florida may also have a higher thermal tolerance than loggerheads. Due to the increased embryonic mortality, and therefore, decreased hatching success, future management strategies may need to protect sea turtle nests from extended periods at elevated temperatures.
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Blanc-Betes, E., Welker, J. M., Sturchio, N. C., Chanton, J. P., & Gonzalez-Meler, M. A. (2016). Winter precipitation and snow accumulation drive the methane sink or source strength of Arctic tussock tundra. Glob Change Biol, 22(8), 2818–2833.
Abstract: Arctic winter precipitation is projected to increase with global warming, but some areas will experience decreases in snow accumulation. Although Arctic CH4 emissions may represent a significant climate forcing feedback, long-term impacts of changes in snow accumulation on CH4 fluxes remain uncertain. We measured ecosystem CH4 fluxes and soil CH4 and CO2 concentrations and C-13 composition to investigate the metabolic pathways and transport mechanisms driving moist acidic tundra CH4 flux over the growing season (Jun-Aug) after 18years of experimental snow depth increases and decreases. Deeper snow increased soil wetness and warming, reducing soil %O-2 levels and increasing thaw depth. Soil moisture, through changes in soil %O-2 saturation, determined predominance of methanotrophy or methanogenesis, with soil temperature regulating the ecosystem CH4 sink or source strength. Reduced snow (RS) increased the fraction of oxidized CH4 (Fox) by 75-120% compared to Ambient, switching the system from a small source to a net CH4 sink (21 +/- 2 and -31 +/- 1mgCH(4)m(-2)season(-1) at Ambient and RS). Deeper snow reduced Fox by 35-40% and 90-100% in medium- (MS) and high- (HS) snow additions relative to Ambient, contributing to increasing the CH4 source strength of moist acidic tundra (464 +/- 15 and 3561 +/- 97mgCH(4)m(-2)season(-1) at MS and HS). Decreases in Fox with deeper snow were partly due to increases in plant-mediated CH4 transport associated with the expansion of tall graminoids. Deeper snow enhanced CH4 production within newly thawed soils, responding mainly to soil warming rather than to increases in acetate fermentation expected from thaw-induced increases in SOC availability. Our results suggest that increased winter precipitation will increase the CH4 source strength of Arctic tundra, but the resulting positive feedback on climate change will depend on the balance between areas with more or less snow accumulation than they are currently facing.
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Bonebrake, T. C., Brown, C. J., Bell, J. D., Blanchard, J. L., Chauvenet, A., Champion, C., et al. (2018). Managing consequences of climate-driven species redistribution requires integration of ecology, conservation and social science. Biol Rev, 93(1), 284–305.
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Boote, J. K., Rybak, M. R., Scholberg, J. M. S., & Jones, J. W. (2012). Improving the CROPGRO-Tomato Model for Predicting Growth and Yield Response to Temperature. HortScience, 47(8), 1038–1049.
Abstract: Parameterizing crop models for more accurate response to climate factors such as temperature is important considering potential temperature increases associated with climate change, particularly for tomato (Lycopersicon esculentum Mill.), which is a heat-sensitive crop. The objective of this work was to update the cardinal temperature parameters of the CROPGRO-Tomato model affecting the simulation of crop development, daily dry matter (DM) production, fruit set, and DM partitioning of field-grown tomato from transplanting to harvest. The main adaptation relied on new literature values for cardinal temperature parameters that affect tomato crop phenology, fruit set, and fruit growth. The new cardinal temperature values are considered reliable because they come from recent published experiments conducted in controlled-temperature environments. Use of the new cardinal temperatures in the CROPGRO-Tomato model affected the rate of crop development compared with prior default parameters; thus, we found it necessary to recalibrate genetic coefficients that affect life cycle phases and growth simulated by the model. The model was recalibrated and evaluated with 10 growth analyses data sets collected in field experiments conducted at three locations in Florida (Bradenton, Quincy, and Gainesville) from 1991 to 2007. Use of modified parameters sufficiently improved model performance to provide accurate prediction of crop and fruit DM accumulation throughout the season. Overall, the average root mean square error (RMSE) over all experiments was reduced 44% for leaf area index, 71% for fruit number, and 36% for both aboveground biomass and fruit dry weight simulations with the modified parameters compared with the default. The Willmott d index was higher and was always above 0.92. The CROPGRO-Tomato model with these modified cardinal temperature parameters will predict more accurately tomato growth and yield response to temperature and thus be useful in model applications.
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Boote, K. J., Jones, J. W., White, J. W., Asseng, S., & Lizaso, J. I. (2013). Putting mechanisms into crop production models: Putting mechanisms into crop production models. Plant Cell Environ, 36(9), 1658–1672.
Abstract: Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30–40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.
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