pdf impact of temperature and precipitation variability on crop model predictions

Pdf Impact Of Temperature And Precipitation Variability On Crop Model Predictions

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Published: 17.03.2021

Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon SOC under historical and future climate, and ii to evaluate the effectiveness of climate adaptation strategies no-tillage and new cultivars in mitigating impacts of increased frequencies of extreme events and warming.

Impact of temperature and precipitation variability on crop model predictions

Higher temperatures associated with decreasing relative humidity conditions can lead to severe drought and affect yield potential and impact crop production. Canadian climate model projection studies indicate a gradual decline in annual precipitation in the prairie province of Saskatchewan Price et al. Given the projected changes in annual precipitation and temperature in Saskatchewan, the aim of this paper is to estimate the effect of precipitation and temperature on crop yield distributions by a full- and partial-moment-based approach. Such moment-based methods are proposed as a flexible way to characterize and estimate the asymmetric relationships between climate variables and the higher-order moments of crop yields Antle This study contributes to the earlier work by Antle , Antle et al. The full- and partial-moment-based approach is applied to yields of two major agricultural field crops canola, spring wheat in the province of Saskatchewan, to illustrate its flexibility to capture the variances and skewness effects of climate and nonclimate variables on the positive and negative yield distributions.

Metrics details. Climate is a key input of rain-fed agriculture. Climate variability and change has been the most important determinant of crop yields in Kenya and other parts of the world. However, there has been not much research on local understanding of the effect of climate variability on maize yields in Arid and Semi arid Lands ASALs of lower eastern Kenya counties. The effect of three climatic parameters on maize yields on different temporal and spatial scales was evaluated in order to provide basis for maize crop monitoring and modeling. This paper argues that maize yields were declining at high levels in Machakos County followed by Kitui, Mwingi, and Makueni Counties. The maize yields Z -values and thus the effect of climate was predominately negative in the period — in all the counties.

Impact of temperature and precipitation variability on crop model predictions

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. How mean historical and future climate change affects crop yields has received a great deal of attention 1 , 2 , 3 , 4 , 5. However, how variations in climate impact crop yield, and how they vary over time, has received less attention 6 , 7.


Crop growth is simulated for two locations and three soil types. Results indicate that average predicted yield decreases with increasing temperature variability.


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Future climate changes, as well as differences in climates from one location to another, may involve changes in climatic variability as well as changes in means. In this study, a synthetic weather generator is used to systematically change the within-year variability of temperature and precipitation and therefore also the interannual variability , without altering long-term mean values. For precipitation, both the magnitude and the qualitative nature of the variability are manipulated. The synthetic daily weather series serve as input to four crop simulation models. Crop growth is simulated for two locations and three soil types.

Introduction

Climate Graph Maker. Monthly temperature, precipitation and hours of sunshine. CCAFS research on climate-smart technologies and practices addresses the challenge of how to transition to climate-smart agriculture CSA at a large scale for enabling agricultural systems to be. This is less than the highest amount of rainfall that Rome gets. Climate graphs. There's no learning.

These three districts are located in high-moisture-stress areas because of crop season rainfall variability. The study used ordinary least square OLS regression to examine the effect of climate variability. The multinomial logistic regression result reveals that households adopt hybrid crops maize and sorghum and dry-sowing adaptation strategies if there is shortage during the cropping season. Cropland increment has positive and significant effect on employing each adaptation strategy. The probability of adopting techniques such as water harvesting, hybrid seeds and dry sowing significantly reduces if a household has a large livestock.

Impact of temperature and precipitation variability on crop model predictions

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