This paper aimed to examine the impact of climate change on wheat productivity in the five rains- fed districts: El-Negaila, Sidi Barrani, El-Daba'a, Marsa Matrouh, and EL-Alamain in the northern west coast of Egypt in which the cultivated wheat area represents about 41.4% of wheat area in Matrouh Governorate (1990-2019). The productivity is fluctuated between 1.167 to 13.38 Ardab/Fadden accompanied with the fluctuation in precipitation between 24.35 to 115.10 MM3/Season, and fluctuation of average difference between Max. Min. temperature from 8.07 to 7 ºC. Fully Modified Ordinary Least Square (FMOLS) technique was applied to investigate the relationship between wheat productivity and the independent variables (precipitation, temperature, cultivated area, labor and technology). OLS function showed that the model suffers from endogenity and heteroscedasticity. LLC and IPS statistics of panel unit root test proved that the included variables have unit root, i.e. they are non-stationary at level. Pedroni panel residual cointegration test confirmed the long run relationship between the first-order integrated variables [I (1)]. FMOLS function proved that natural climatic variables are the main determinants of wheat productivity, as a 1% increase in annual rainfall improves wheat productivity significantly by 3.3%, while temperature affects the wheat productivity negatively by 5.7%. The far west districts are the most affected by rainfall, as 1% increase of rainfall in EL-Negaila and Sidi- Barrani districts increase wheat productivity by 8.4%, 5.1% respectively. Results in all districts except EL-Negaila and Sidi Barrani showed the extent of labor intensification to enhance productivity, also all districts showed the importance of technical improvements. It is recommend adopting water policy as rain harvesting, building stone dykes and cisterns to provide: 355.5, 301.7, 287.9, 339.8, and 245.8 MM3/Fadden in El-Negaila, Sidi Barrani, EL-Daba'a, Marsa Matrouh, and EL-Alamain districts respectively to improve wheat yield to 12 Ardab/Fadden under drought climate of north coast

The research aimed at identifying risks facing agriculture from water availability and natural hazards by planning the cropping pattern under low risk, and studying the impact of both climate change and geographical area on the productivity per Feddan. The study estimate risk of cropping pattern in the first scenario was estimated to be about 6.6% under normal restrictions. In the second scenario, it was 3.04% under normal restrictions in addition to stabilize the area of rice at 750,000 feddans, with self-sufficiency of 10% and 20% of wheat and maize The total return on risk under the first and second scenarios was about 73.315 and 75.133 million pounds. The cropping pattern in the second scenario is better than the first, as it achieves a higher net return and also achieves state policies  in terms of reducing the rice area. It also increases the self-sufficiency of  wheat and maize. The effect of climate change and geographical area variation on productivity during the period (2013-2016) showed that their impact ranged from 61 to 92%. There Also was a negative impact of climate change on the productivity per Feddan. except for wheat crop the effect of either wheat has not been determined. The study recommended pulling the optimal cropping pattern, the need to increase the awareness of the seriousness of the negative effects of climate change and the development of awareness programs through the various media, and the production of new varieties afford climate change and salinity, and the use of modern technological methods Such as protected agriculture.

Early blight caused by Alternaria solani (Ell. and Mart.) is one of the most important economic diseases, which caused considerable loss in their yield and quality under Egyptian conditions. Aim the research was to study the relationship between climate change and disease severity for prediction in future seasons. Disease severity was recorded for three growing periods i.e. summer (May. to Aug), autumn (Jul. to Oct) and winter (Nov. to Mar.), at three governorates (Behira, Ismailia, and Assuit) during growing seasons 2016/2017-2017/2018. Severity of early blight disease on tomato has been predicted by regression estimated accumulative disease severity values during 2007/2008 to 2015/2016 season and average max and min temperature and humidity through these seasons. Prediction of disease has been formed as Y= b0+b1x1+b2x2+.......... bqxq. Three models were created to describe the severity disease by multiple regressions (MINITAB® program). The highest value of early blight disease was recorded through season 2017/2018, while the lowest value was recorded during season 2016/2017. Also, highly disease severity was estimated during summer period compared with autumn and winter growing periods, but through winter growing period was the least severity and moderately severity estimated in the autumn growing period. Influence of environmental conditions on the severity of early blight disease during seasons from 2007/2008 to 2015/2016, in Behira governorate, the highest disease severity was showed in season 2010-2011, and the lowest disease was in season 2012-2013, but in Ismailia governorate, highest disease severity was estimated in season 2010-2011and lowest disease was recorded in season 2013-2014 and in Assuit governorate, the highest value of disease was in season 2010-2011, and the lowest value was in season 2013-2014. For Forecasting, significantly differences noticed between disease severity through 2020/2030, 2030/2040 and 2040/ 2050s seasons compared with 2008/2018s seasons and thier relation with climate change in tested governorates, where severity of tomato early blight disease was icreased from 11.8% during 2008/2018s seasons to 2040/ 2050s seasons 15.4% during at Behira governorate, from 18.8% during 2008/2018s seasons to 2040/ 2050s seasons 36.3% during at Ismailia governorate and from 18.8% during 2008/2018s seasons to 2040/ 2050s seasons 40.4% during at Assuit governorate with slight change in maximum or minimum temperatures and percentage of relative humidity.