*3.2.1 Years 2016–2040*

*Agronomy - Climate Change and Food Security*

increase after 2100. HadGEM2-ES is a second-generation global model developed by the Hadley Center, a research organization affiliated with the UK Met Office [12].

∑si ∑pi ∑hi ∑ki ∑ti ∑mi ∑vi n\* ∑vi N

**Z matrix W** 

<sup>2</sup> ∑pisi ∑hisi ∑kisi ∑tisi ∑misi ∑visi ∑si ∑siy A

<sup>2</sup> ∑hipi ∑kipi ∑tipi ∑mipi ∑vipi ∑pi ∑pi B

<sup>2</sup> ∑kihi ∑pihi ∑mihi ∑vihi ∑hi ∑hi C

<sup>2</sup> ∑tiki ∑miki ∑viki ∑ki ∑ki D

<sup>2</sup> ∑miti ∑viti ∑ti ∑ti M

<sup>2</sup> ∑vimi ∑mi ∑mi F

<sup>2</sup> ∑vi ∑vi G

**matrix**

**X matrix**

In this study, province-based regression equations were established using the least squares method (LSM) with sunflower yield values from the 29 provinces from 1985 through 2014 and the 7 selected climate parameters. After that, the potential impact of climate changes that are projected for the future periods (2016–2040, 2041–2070, and 2071–2099) on yield of sunflower has been put forward with using the generated high-rate regression equations and climate

In the study, the regression analysis equation created by LSM was as follows:

*y* = *As* + *Bp* + *Ch* + *Dk* + *Et* + *Fm* + *Gv* + *H* (1)

where the dependent variable *y = yield. A, B, C, D, E, F, G, and H* are coeffi-

This study was conducted in order to determine the possible effects of climate change on sunflower yield in 29 provinces where intensive sunflower cultivation has been conducted in Turkey. The periods of 2016–2040, 2041–2070, and 2071–2099

*k* = number of days with daily average relative humidity >70%

*m* = number of days with daily maximum temperature >35°C *v* = number of days with daily minimum temperature ≤−5°C

cients, and the independent variables were as follows:

*s* = monthly total sunshine duration (h) *p* = monthly total precipitation (mm) *h* = monthly average relative humidity (%)

*t* = monthly average temperature (°C)

**28**

**2.2 Method**

**Table 1.**

∑si

∑sipi ∑pi

∑sihi ∑pihi ∑hi

∑siki ∑piki ∑hiki ∑ki

∑siti ∑piti ∑hiti ∑kiti ∑ti

*Parameter matrices used in the least squares method.*

∑simi ∑pimi ∑himi ∑kimi ∑timi ∑mi

∑sivi ∑pivi ∑hivi ∑kivi ∑tivi ∑mivi ∑vi

*2.2.1 Regression analysis*

projection data (**Table 1**).

**3. Results and discussion**

were determined as future periods.

The results of the analyses conducted for 2016–2040 using the climate projections data in the regression equations predicted that sunflower yield would increase in 16 of the 29 provinces, decrease in 12 of the 29 provinces, and no change in 1 of the 29 provinces (**Figure 3**).
