Role of early warning systems for sustainable agriculture in Iran

2023

IJAm

The effects of climate changes on agroecosystems can cause relevant issues. Using principal component analysis (PCA) we determined the 67 agricultural climate indicators (ACI) at 44 of Iran’s synoptic stations under current (1990-2019) and future (2025, 2050, 2075, and 2100) conditions. Based on UNESCO aridity index, the agroecological zonation (AEZ) was used to classify Iran’s regions (very dry, dry, semidry and humid climates). Using the PCA method, the first 5 principal components were determined by including data sets for temperature (winter, spring, summer and autumn maximum and winter minimum temperature), precipitation (winter and summer precipitation), reference evapotranspiration (ETref), and the degree of growth days in spring and winter, which explained about 96 percent of the total variance. For each climate empirical equation for ETref was selected. In order to accurate evaluation of ETref were used The Penman-Monteith based on FAO56 (PM-FAO56) for the very dry climate, the Hargreaves equation for the semidry climate, and the Penman 1 and 2 equations for the dry and humid climates, respectively. According to the results, the first component alone, with an eigenvalue of 41.15, explained more than 74 percent of the total variance. Based on the results of zoning by the PCA outcomes, the stations for 1990-2019 were classified into 7 zones. While 2025, 2050, 2075, and 2100 were classified in 6, 7, 6, and 5 zones, respectively. Under the future climatic conditions of the country, in terms of climatic indicators, the similarity between the stations will increase and the climatic diversity of the country will decline compared to current conditions. The results demonstrated that the PCA method would be valuable for monitoring AEZ in semidry climates at reasonably long periods.

Section: Introductionmentioning

confidence: 99%

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

2023

IJAm

“…On the basis of the FAO 56 index, the climate of Iran is divided into 4 climates (Fig. 1) including; very dry, dry, semidry, and humid (Shara et al, 2016). In this study, the climatic data collected were used to compute reference evapotranspiration (ET ref ) by using the Penman-Monteith method.…”

Section: Climate Classi Cationmentioning

confidence: 99%

MLR approaches for the estimation of the grain yield of rainfed wheat and barley across Iran

Nahvinia

Ghaleni

et al. 2022

Preprint

Self Cite

Climate variability has a crucial role in rainfed farming, especially in dry climates and evaluation of these fluctuations under different climates provides a framework for further studies. Iran was classified into very dry, dry, semi-dry, and humid climates by using the FAO56 index. This study investigated the equations obtained from multiple linear regression (MLR) and the gap between observed and predicted yield of rainfed wheat and barley yield in different climates across Iran. Climatic data including; rainfall (R), mean temperature (Tmean), solar radiation (S), and wind speed (U2) collected from 44 synoptic stations during 1981–2020 and were used as the inputs of a MLR models to simulate rainfed wheat and barley yield. Global Performance Indicator (GPI) was utilized to evaluate the performances of the MLR models, which is a 5- statistical criteria index. The results showed that the lower statistical error criteria values of MLR models confirmed their better performance than MLR models in dry climates (R2 = 0.84 for wheat and R2 = 0.9 for barley) than in humid climates (R2 = 0.69 for wheat and R2 = 0.66 for barley). Also, the MLR models estimated the yield of rainfed wheat (GPI = 1559.3) better than rainfed barely (GPI = 1536) in all climates.

“…On the other hand, on an annual scale, the effect of seasonal fluctuations in precipitation and relative humidity is somewhat combined, so the drought (humidity) in each season was evaluated independently of other seasons in this study, which this is also a special case on a quarterly scale. In terms of agriculture and water resources, the study of seasonal fluctuations in rainfall and drought is also very important (Sharafi et al, 2016).…”

Section: Determine the Changing Point (Cp) Using Pettit's Testmentioning

confidence: 99%

“…Drought and water scarcity can cause serious crises in human social life, such as the competition over access to water resources, endangering public health, and adverse environmental and ecological impacts (Hosseini-Moghari & Araghinejad, 2015;Haile et al, 2019). Therefore, considering the climatic conditions, especially in arid and semi-arid regions, the study of drought status can be an important role in providing forecasting to achieve the stability of systems (Sharafi et al, 2016). Since drought is a natural and recurring phenomenon that occurs due to reduced rainfall over a specified and relatively long period of time (Sharma & Mujumdar, 2017), one of the important components of drought planning is the provision of timely and reliable climate information, including seasonal forecasts, which assist decision makers at all levels in managerial decisions.…”

Section: Introductionmentioning

confidence: 99%

Spatial assessment of drought features over different climates and seasons across Iran

Ghaleni

2021

Theor Appl Climatol

Self Cite

Drought is one of the most complex phenomena in the world, so proper management is very important in monitoring and reducing its damage. For this purpose, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) indices were used to analyze the intensity and frequency of drought in the coastal wet, mountain, semi mountain, semi desert, desert and coastal desert climates of Iran in four seasons, separately: autumn, winter, spring and summer. 43 synoptic stations with a common statistical period of 50 years (1969-2019) were selected. The results showed that the trend of drought in winter and summer is increasing in all studied climates. The highest correlation (0.80-0.99) between SPI-RDI and SPEI-RDI indices in coastal desert, mountain and semi mountain climates and the lowest correlation (0.34) between SPI-SPEI and SPEI-RDI indices in semi desert, desert and coastal desert climates were obtained. The comparison of the results in the trend analysis of the drought showed the same trend, but the SPEI index compared to the other indicators showed a quicker response to changes in drier climates. SPI-RDI variations showed similar values in colder climates. The SPEI is based on precipitation and temperature data, and it has the advantage of combining multi-scalar character with the capacity to include the effects of temperature variability in the drought assessment. Thus, SPEI is recommended as a suitable index for studying and identifying the effect of climate change on drought conditions.