Predicting Changes of Rainfed Barley (Hordeum vulgare L.) Farming Calendar using Downscaling LARS-WG and HadCM3 Models in Lorestan Province in 2011-2030 period

بوم شناسی کشاورزی

Article 3, Volume 9, Issue 2 - Serial Number 32, June 2017, Pages 475-489   

Document Type: Scientific - Research

DOI: 10.22067/jag.v9i2.46511

Authors

Mahmoud Ahmadi^* ; mostafa falahi khoshji; omid mafakheri

kharazmi university

Abstract

Introduction
The results of climate change studies in recent years confirm this phenomenon occurrence in Iran. The climatic characteristics (potential and limitations of climate) are considered in the long run, to determine the pattern of cultivation and distribution of different plant species. Unfortunately, the agricultural sector due to the low speed and power compliance, will suffer the greatest impact of climate change. General circulation models provide accurate tools to predict future climatic conditions, and the necessary data for the implementation of simulation models and the development of crops under climate change conditions. The study of the effects of climate change on the agricultural sector seems to be necessary due to increase the demand for agricultural production. The aim of this study was to investigate the effects of climate change on the rainfed barley farming calendar in Lorestan province as an effective pole in cereal cultivation in Iran.
Materials and methods
In order to study the effects of climate change on the rainfed barley farming calendar, outputs from the HadCM3 model simulations were used. After evaluating the LARS-WG stochastic weather generator model using performance indicators and ensure the suitability of the model, this model was applied to downscale HadCM3 model outputs. A2 scenario was chosen to evaluate climate impacts for the period 2011–2030. In this study, due to the suitable temperature for germination in the region, has been emphasized only on the precipitation to find the most suitable time for barley cultivation. Planting date was calculated by Weibull probability with 50 and 75% confidence intervals. Growing degree days (GDD) were used to calculate the phenological stages. For the forecast period, the same method was used to determine the farming calendar.

Results Discussion
The results showed that in the observation period, the earliest planting date was observed in northern province in Borujerd and Aleshtar cities, and as we go south, the planting date postponed. The beginning of the cultivation is a function of temperature, so that the latest planting date was observed in Poldokhtar city as the warmest region of the province. In the observation period, the latest harvesting date was observed in Aligoodarz and Aleshtar cities and the earliest harvesting date was observed in Poldokhtar city. In the forecast period, the beginning of crop cultivation did not change much and remained constant for less than 10 days compared to the observation period. However, many changes occurred in the harvesting date. So that the most changes with 60 days earlier occurred at the Poldokhtar city. The duration of the growth period reduced at all the stations. The greatest reduction in the duration of the growth period was observed at the Aligudarz city with 62 days. The decreasing duration of the growth period was due to changes in temperature and precipitation. This shows that the fall precipitation, which is related to the cold season, does not change much, but the precipitation of the warm season decreases. One of the limitations of rainfed barley cultivation in Lorestan province is temperature in the tillering stage. This restriction will continue in future. In the observation period, temperature of flowering and grain filling period was in optimal conditions, but in the forecast period, with increasing temperature, we will encounter high temperature stress in these two stages. The adaptation strategies are different depending on the type of farming and the climate change scenario. Among these strategies we can mention changes in planting date and crop rotation, use of resistant varieties to the warm conditions and irrigation management.
Conclusion
The results showed that at all stations, the planting date will be earlier and the duration of the growth period will decrease in the period 2011–2030.

Keywords

Adaptability; Agriculture; Climate change; Modeling

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