Habibioallah Farrokhi, A. Asgharzadeh, Malihe Kazemi Samadi
Saffron is highly valued for its unique aroma, taste, color, and medicinal properties. Iran is one of the most important saffron-producing countries. The present study aimed to investigate the effect of climatic and environmental characteristics of six sites (Shirvan, Faruj, Zavareh, Torbat-e Heydarieh, Ghayen, and Birjand) on the yield and qualitative, and biochemical characteristics of saffron. The studied sites were considered as treatments. The obtained data were analyzed based on a nested design, where the village within the site was considered an experimental error, and the farm within the village within each site was considered a sampling error. The Torbat-e Heydarieh treatment with altitudes of ~1323.3 m produced the maximum saffron flower yield (0.83 g m2), stigma yield (0.098 g m2), safranal content (15.8%), picrocrocin content (30.6%), and crocins content (69.3%). Evidently that the low maximum summer temperature in the area is one of the reasons for its superiority. The correlation analysis between traits shows that the maximum summer temperature had a significant negative correlation with saffron flower yield, stigma yield, and picrocrocin and crocin content. Results showed the highest total flavonoid and phenol content and DPPH activity related to Shirvan and Faruj. Although the results showed that selenium could increase the quantitative and qualitative yield of saffron, this requires further studies to confirm it. Based on the findings, it is concluded that I) qualitative and quantitative characteristics of saffron are strongly controlled by the environmental and climatic conditions and II) Razavi Khorasan province had a significant advantage in terms of flower and stigma yield and safranal, picrocrocin and crocin content of saffron and North Khorasan province in terms of biochemical characteristics.
藏红花因其独特的香气、味道、颜色和药用特性而受到高度重视。伊朗是最重要的藏红花生产国之一。本研究旨在探讨6个产地(Shirvan、Faruj、Zavareh、torbate Heydarieh、Ghayen和Birjand)的气候和环境特征对藏红花产量、质量和生化特性的影响。所研究的部位被认为是治疗。获得的数据基于嵌套设计进行分析，其中站点内的村庄被认为是实验误差，每个站点内的村庄中的农场被认为是抽样误差。海拔~1323.3 m的Torbat-e Heydarieh处理的藏红花花产量最高(0.83 g m2)，柱头产量最高(0.098 g m2)，藏红花素含量最高(15.8%)，微番红花素含量最高(30.6%)，藏红花素含量最高(69.3%)。显然，该地区夏季最高气温较低是其优势的原因之一。性状间相关分析表明，夏季最高温度与藏红花花产量、柱头产量、藏红花素和藏红花素含量呈显著负相关。结果表明，黄酮类化合物、酚类化合物含量和DPPH活性最高的品种为石首菜和法鲁菜。虽然结果表明硒可以提高藏红花的定量和定性产量，但这需要进一步的研究来证实。综上所述，1)藏红花的定性和定量特征受环境和气候条件的强烈控制;2)拉萨维省在花和柱头产量以及藏红花、微藏红花素和藏红花素含量方面具有显著优势，而北呼罗珊省在生化特征方面具有显著优势。
{"title":"Yield and qualitative and biochemical characteristics of saffron (Crocus sativus L.) cultivated in different soil, water, and climate conditions","authors":"Habibioallah Farrokhi, A. Asgharzadeh, Malihe Kazemi Samadi","doi":"10.36253/ijam-1216","DOIUrl":"https://doi.org/10.36253/ijam-1216","url":null,"abstract":"Saffron is highly valued for its unique aroma, taste, color, and medicinal properties. Iran is one of the most important saffron-producing countries. The present study aimed to investigate the effect of climatic and environmental characteristics of six sites (Shirvan, Faruj, Zavareh, Torbat-e Heydarieh, Ghayen, and Birjand) on the yield and qualitative, and biochemical characteristics of saffron. The studied sites were considered as treatments. The obtained data were analyzed based on a nested design, where the village within the site was considered an experimental error, and the farm within the village within each site was considered a sampling error. The Torbat-e Heydarieh treatment with altitudes of ~1323.3 m produced the maximum saffron flower yield (0.83 g m2), stigma yield (0.098 g m2), safranal content (15.8%), picrocrocin content (30.6%), and crocins content (69.3%). Evidently that the low maximum summer temperature in the area is one of the reasons for its superiority. The correlation analysis between traits shows that the maximum summer temperature had a significant negative correlation with saffron flower yield, stigma yield, and picrocrocin and crocin content. Results showed the highest total flavonoid and phenol content and DPPH activity related to Shirvan and Faruj. Although the results showed that selenium could increase the quantitative and qualitative yield of saffron, this requires further studies to confirm it. Based on the findings, it is concluded that I) qualitative and quantitative characteristics of saffron are strongly controlled by the environmental and climatic conditions and II) Razavi Khorasan province had a significant advantage in terms of flower and stigma yield and safranal, picrocrocin and crocin content of saffron and North Khorasan province in terms of biochemical characteristics.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year.
{"title":"Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning","authors":"I. Volpi, D. Guidotti, Michele Mammini, S. Marchi","doi":"10.36253/ijam-1131","DOIUrl":"https://doi.org/10.36253/ijam-1131","url":null,"abstract":"Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year. ","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41358788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drought is a climatic event that threatens the environment and human life with an ambiguity of location and time. Recently, droughts can be analyzed for different periods with the help of different mathematical methods and developing technology. This study aims to perform a drought analysis in 126 designated study points of Turkey. The analyzed data includes monthly total precipitation values between March 2000 and February 2021, obtained from PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Monthly precipitation totals of these designated points were used as input parameters in the Drought Exceedance Probability Index (DEPI) which is a new drought analysis method. The analysis was conducted separately for the whole of Turkey from January to December. Moreover, the findings were compared with the Standardized Precipitation Index (SPI), a globally accepted and commonly used drought index, to measure the drought detection performance of DEPI. SPI was calculated for periods of 6, 12 and 24 months. Pearson correlation coefficients between drought values of SPI-6, SPI-12 and SPI-24 and DEPI results were calculated. The second part of the study includes possible trend of drought determined by the Mann-Kendall trend analysis method. Both DEPI and SPI results and trend analysis results were mapped and visualized with the help of ArcGIS package program. The highest correlation is between DEPI and SPI-12 with 0.75, while the lowest correlation is between DEPI and SPI-24 with a value of 0.62. SPI monthly drought maps indicated the wettest months were January and February, while the driest months were March and July. Besides the DEPI monthly drought maps, the wettest months were October and November, while the driest months were May and June. The Mann-Kendall trend maps showed a significant increase in drought for summer.
{"title":"Testing of Drought Exceedance Probability Index (DEPI) for Turkey using PERSIANN data for 2000-2021 period","authors":"E. Topçu","doi":"10.36253/ijam-1308","DOIUrl":"https://doi.org/10.36253/ijam-1308","url":null,"abstract":"Drought is a climatic event that threatens the environment and human life with an ambiguity of location and time. Recently, droughts can be analyzed for different periods with the help of different mathematical methods and developing technology. This study aims to perform a drought analysis in 126 designated study points of Turkey. The analyzed data includes monthly total precipitation values between March 2000 and February 2021, obtained from PERSIANN system (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). Monthly precipitation totals of these designated points were used as input parameters in the Drought Exceedance Probability Index (DEPI) which is a new drought analysis method. The analysis was conducted separately for the whole of Turkey from January to December. Moreover, the findings were compared with the Standardized Precipitation Index (SPI), a globally accepted and commonly used drought index, to measure the drought detection performance of DEPI. SPI was calculated for periods of 6, 12 and 24 months. Pearson correlation coefficients between drought values of SPI-6, SPI-12 and SPI-24 and DEPI results were calculated. The second part of the study includes possible trend of drought determined by the Mann-Kendall trend analysis method. Both DEPI and SPI results and trend analysis results were mapped and visualized with the help of ArcGIS package program. The highest correlation is between DEPI and SPI-12 with 0.75, while the lowest correlation is between DEPI and SPI-24 with a value of 0.62. SPI monthly drought maps indicated the wettest months were January and February, while the driest months were March and July. Besides the DEPI monthly drought maps, the wettest months were October and November, while the driest months were May and June. The Mann-Kendall trend maps showed a significant increase in drought for summer.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43513911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Russian-olive (Elaeagnus angustifolia) is a species native to southern Europe and central and eastern Asia. This species plays an important role in urban landscape design because of its rapid growth, resistance in harsh climates and tolerance to human-caused pressure. Understanding its potential dispersal and restricting parameters are the first steps toward the sustainable use of this species. Here, we used Species Distribution Models to predict the potential distribution of Russian-olive in Iran climate and estimate the possible limiting factors for its spread. Our results highlighted the importance of environmental variables including climatic factors, soil, and lithology in the distribution of this species throughout the country. According to these results, suitable habitats for Russian-olive are located in the north of Iran along the Alborz and Koppeh-Dagh mountain ranges. Therefore, the suitable habitats for this species are limited to only nine percent of the country. A habitat suitability map can be used to evaluate future developments in urban areas and predict the dispersal range of Russian-olive in Iran. Our results show that Russian-olive can be used to create new green spaces in urban climates in the northern regions of Iran.
{"title":"Predicting the potential habitat of Russian-Olive (Elaeagnus angustifolia) in urban landscapes","authors":"Z. Karimian, A. Farashi","doi":"10.36253/ijam-1071","DOIUrl":"https://doi.org/10.36253/ijam-1071","url":null,"abstract":"Russian-olive (Elaeagnus angustifolia) is a species native to southern Europe and central and eastern Asia. This species plays an important role in urban landscape design because of its rapid growth, resistance in harsh climates and tolerance to human-caused pressure. Understanding its potential dispersal and restricting parameters are the first steps toward the sustainable use of this species. Here, we used Species Distribution Models to predict the potential distribution of Russian-olive in Iran climate and estimate the possible limiting factors for its spread. Our results highlighted the importance of environmental variables including climatic factors, soil, and lithology in the distribution of this species throughout the country. According to these results, suitable habitats for Russian-olive are located in the north of Iran along the Alborz and Koppeh-Dagh mountain ranges. Therefore, the suitable habitats for this species are limited to only nine percent of the country. A habitat suitability map can be used to evaluate future developments in urban areas and predict the dispersal range of Russian-olive in Iran. Our results show that Russian-olive can be used to create new green spaces in urban climates in the northern regions of Iran.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sérgio Weine Paulino Chaves, Rubens Duarte Coelho, Jéfferson de Oliveira Costa, Sergio André Tapparo
Micrometeorological variables of tabasco pepper cultivated under greenhouse and drip irrigated conditions have not been presented to date in literature, especially the water consumption of these plants, in terms of crop evapotranspiration (ETc) and crop coefficient (Kc). The determination of these variables is extremely important for the application of the correct amount of water to irrigated crops in these environments because PM FAO (56) standard methodology was idealized for outdoor environments. The objective of this work was to develop models of estimation of micrometeorological variables in greenhouse conditions and to determine the water demand, in terms of evapotranspiration (ET) and Kc, of the pepper (Capsicum frutescens L.), cv. Tabasco McIlhenny, drip irrigated using drainage lysimeters. The research was carried out in an experimental area located at the University of Sao Paulo (USP) in Piracicaba, SP, Brazil. The following micrometeorological variables were monitored: air temperature, air relative humidity (digital thermohygrometer) and evaporation (mini-pan) (EMT). Drainage lysimeters were used to obtain the ETc and the reference evapotranspiration (ETo) was estimated outside the greenhouse by the Penman Monteith (EToPM), Hargreaves and Samani (EToHS) methods and the class “A” pan method (ECA). It was concluded that the total value of mini-pan evaporation (EMT) inside the greenhouse was practically equal to EToPM, 5% lower than EToHS and 31% higher than ECA in the outdoor environment. ET values ranged from 0.28 to 2.42 mm day-1 and total crop ET was 446.43 mm. The Kc values for the first pepper production cycle were: 0.17 in the initial phase, 0.76 in the flowering and fruiting phase and 0.39 in the harvest phase, for the second production cycle, the value of Kc was 0.50 at the harvest phase.
温室和滴灌条件下塔巴斯科辣椒的微气象变量，特别是耗水量，如作物蒸散量(ETc)和作物系数(Kc)，迄今尚未见文献报道。这些变量的确定对于在这些环境中为灌溉作物施用正确的水量至关重要，因为PM FAO(56)标准方法是理想的室外环境。本研究的目的是建立估算温室条件下微气象变量的模型，并根据辣椒(Capsicum frutescens L.)的蒸散发(ET)和Kc来确定辣椒(Capsicum frutescens L.)， cv。塔巴斯科麦基尔亨尼，用排水溶渗仪滴灌。这项研究是在巴西圣保罗州皮拉西卡巴的圣保罗大学(USP)的一个实验区进行的。监测了以下微气象变量:气温、空气相对湿度(数字湿度计)和蒸发量(迷你锅)。利用排水渗蒸仪获取ETc，采用Penman Monteith (EToPM)法、Hargreaves and Samani (EToHS)法和A类pan法(ECA)估算温室外参考蒸散量(ETo)。结果表明，在室外环境下，温室内微型蒸发皿蒸发量(EMT)与EToPM基本相等，比EToHS低5%，比ECA高31%。ET值为0.28 ~ 2.42 mm d -1，作物总ET值为446.43 mm。辣椒第一个生产周期的Kc值为:初始期0.17，开花结实期0.76，收获期0.39，第二个生产周期的Kc值为收获期0.50。
{"title":"Micrometeorological modeling and water consumption of tabasco pepper cultivated under greenhouse conditions","authors":"Sérgio Weine Paulino Chaves, Rubens Duarte Coelho, Jéfferson de Oliveira Costa, Sergio André Tapparo","doi":"10.36253/ijam-1221","DOIUrl":"https://doi.org/10.36253/ijam-1221","url":null,"abstract":"Micrometeorological variables of tabasco pepper cultivated under greenhouse and drip irrigated conditions have not been presented to date in literature, especially the water consumption of these plants, in terms of crop evapotranspiration (ETc) and crop coefficient (Kc). The determination of these variables is extremely important for the application of the correct amount of water to irrigated crops in these environments because PM FAO (56) standard methodology was idealized for outdoor environments. The objective of this work was to develop models of estimation of micrometeorological variables in greenhouse conditions and to determine the water demand, in terms of evapotranspiration (ET) and Kc, of the pepper (Capsicum frutescens L.), cv. Tabasco McIlhenny, drip irrigated using drainage lysimeters. The research was carried out in an experimental area located at the University of Sao Paulo (USP) in Piracicaba, SP, Brazil. The following micrometeorological variables were monitored: air temperature, air relative humidity (digital thermohygrometer) and evaporation (mini-pan) (EMT). Drainage lysimeters were used to obtain the ETc and the reference evapotranspiration (ETo) was estimated outside the greenhouse by the Penman Monteith (EToPM), Hargreaves and Samani (EToHS) methods and the class “A” pan method (ECA). It was concluded that the total value of mini-pan evaporation (EMT) inside the greenhouse was practically equal to EToPM, 5% lower than EToHS and 31% higher than ECA in the outdoor environment. ET values ranged from 0.28 to 2.42 mm day-1 and total crop ET was 446.43 mm. The Kc values for the first pepper production cycle were: 0.17 in the initial phase, 0.76 in the flowering and fruiting phase and 0.39 in the harvest phase, for the second production cycle, the value of Kc was 0.50 at the harvest phase.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. E. Başakın, Ö. Ekmekcioğlu, M. Özger, Nilcan Altınbaş, L. Şaylan
Determination of surface energy balance depends on the energy exchange between land and atmosphere. Thus, crop, soil and meteorological factors are crucial, particularly in agricultural fields. Evapotranspiration is derived from latent heat component of surface energy balance and is a key factor to clarify the energy transfer mechanism. Development of the methods and technologies for the aim of determining and measuring of evapotranspiration have been one of the main focus points for researchers. However, the direct measurement systems are not common because of economic reasons. This situation causes that different methods are used to estimate evapotranspiration, particularly in locations where no measurements are made. Thus, in this study, non-linear techniques were applied to make accurate estimations of evapotranspiration over the winter wheat canopy located in the field of Atatürk Soil Water and Agricultural Meteorology Research Institute Directorate, Kırklareli, Turkey. This is the first attempt in the literature which consist of the comparison of different machine learning methods in the evapotranspiration values obtained by the Bowen Ratio Energy Balance system. In order to accomplish this aim, support-vector machine, Adaptive neuro fuzzy inference system and Artificial neural network models have been evaluated for different input combinations. The results revealed that even with only global solar radiation data taken as an input, a high prediction accuracy can be achieved. These results are particularly advantageous in cases where the measurement of meteorological variables is limited. With the results of this study, progress can be made in the efficient use and management of water resources based on the input parameters of evapotranspiration especially for regions with limited data.
{"title":"Estimation of measured evapotranspiration using data-driven methods with limited meteorological variables","authors":"E. E. Başakın, Ö. Ekmekcioğlu, M. Özger, Nilcan Altınbaş, L. Şaylan","doi":"10.36253/ijam-1055","DOIUrl":"https://doi.org/10.36253/ijam-1055","url":null,"abstract":"Determination of surface energy balance depends on the energy exchange between land and atmosphere. Thus, crop, soil and meteorological factors are crucial, particularly in agricultural fields. Evapotranspiration is derived from latent heat component of surface energy balance and is a key factor to clarify the energy transfer mechanism. Development of the methods and technologies for the aim of determining and measuring of evapotranspiration have been one of the main focus points for researchers. However, the direct measurement systems are not common because of economic reasons. This situation causes that different methods are used to estimate evapotranspiration, particularly in locations where no measurements are made. Thus, in this study, non-linear techniques were applied to make accurate estimations of evapotranspiration over the winter wheat canopy located in the field of Atatürk Soil Water and Agricultural Meteorology Research Institute Directorate, Kırklareli, Turkey. This is the first attempt in the literature which consist of the comparison of different machine learning methods in the evapotranspiration values obtained by the Bowen Ratio Energy Balance system. In order to accomplish this aim, support-vector machine, Adaptive neuro fuzzy inference system and Artificial neural network models have been evaluated for different input combinations. The results revealed that even with only global solar radiation data taken as an input, a high prediction accuracy can be achieved. These results are particularly advantageous in cases where the measurement of meteorological variables is limited. With the results of this study, progress can be made in the efficient use and management of water resources based on the input parameters of evapotranspiration especially for regions with limited data.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Orlandi, A. Ranfa, L. Ruga, C. Proietti, M. Fornaciari
Plant phenology, through opportune observing and interpreting techniques can be useful to interpret the eventual plant vegetative and reproductive adaptation to climate changes. Some plants of Salix acutifolia Willd., S. smithiana Willd. and S. viminalis L. were considered in a phenological garden in central Italy for analysing their phenological growth stages according to the International gardens network indications during a 10-year period (2008-2017) which allowed us to realize some preliminary trend analyses. The 3 Salix species showed different behaviours in the same cultivation area. S. acutifolia manifested no trend for spring and autumnal phases, S. viminalis presented low significant trends while S. Smithiana was that with the more evident tendencies for all the considered vegetative phases during the study period. The reproductive phase (BBCH 65) showed no significant trend for any Salix species during the study period not being influenced by the different meteorological variables and suggesting that photoperiod in this case may play an important role. The more evident phenological trends were represented for 2 Salix species by the advance of the leaf development during spring and by the progressive delay of the senescence during the last part of the summer, with the fallen leaves phase that was recorded averagely 2 weeks later during the last years of the study period.
{"title":"Meteorological and Salix species (S. acutifolia, S. smithiana, S. viminalis) phenological trends in central Italy","authors":"F. Orlandi, A. Ranfa, L. Ruga, C. Proietti, M. Fornaciari","doi":"10.36253/ijam-822","DOIUrl":"https://doi.org/10.36253/ijam-822","url":null,"abstract":"Plant phenology, through opportune observing and interpreting techniques can be useful to interpret the eventual plant vegetative and reproductive adaptation to climate changes. Some plants of Salix acutifolia Willd., S. smithiana Willd. and S. viminalis L. were considered in a phenological garden in central Italy for analysing their phenological growth stages according to the International gardens network indications during a 10-year period (2008-2017) which allowed us to realize some preliminary trend analyses. The 3 Salix species showed different behaviours in the same cultivation area. S. acutifolia manifested no trend for spring and autumnal phases, S. viminalis presented low significant trends while S. Smithiana was that with the more evident tendencies for all the considered vegetative phases during the study period. The reproductive phase (BBCH 65) showed no significant trend for any Salix species during the study period not being influenced by the different meteorological variables and suggesting that photoperiod in this case may play an important role. The more evident phenological trends were represented for 2 Salix species by the advance of the leaf development during spring and by the progressive delay of the senescence during the last part of the summer, with the fallen leaves phase that was recorded averagely 2 weeks later during the last years of the study period.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70130212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agriculture sector is most vulnerable to climate change. To predict the crop yield in accordance with the changing climate is a need of hour than choice. To know the climate in advance is crucial for grape growing farmers and grape export agencies for its better planning and security of grape industries from climate change perspective. In the present study, the Agro-Climatic Grape Yield (ACGY) model is developed on monthly scale climatic parameters using correlation, significance and multi-regression analysis approach. The developed model is statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the developed model is obtained as 1.03, 0.19, 0.03% and 0.19 respectively. Sensitivity analysis is carried out for the developed ACGY model using the parametric sensitivity method. In order to know the grape yield for future using developed ACGY model, climate scenarios are generated under Canadian Earth System Model (CanESM2) for three emissions Representative Concentration Pathways (RCP) as RCP2.6, RCP4.5, and RCP8.5. Model response variability is carried out to understand the variation of grape yield. It is observed that grape yield is showing adverse variation with the increase in minimum temperature in January and November months, and precipitation in August and November months. Whereas, minimum temperature in April and sum of monthly mean evapotranspiration showing accordance effect on the grape yield. It is recommended the use of ACGY model for grape yield estimations applicable for the present and future climate of the study area based on the predictive capability of developed model.
{"title":"Development of agro-climatic grape yield model with future prospective","authors":"S. J. Kadbhane, V. Manekar","doi":"10.36253/ijam-406","DOIUrl":"https://doi.org/10.36253/ijam-406","url":null,"abstract":"Agriculture sector is most vulnerable to climate change. To predict the crop yield in accordance with the changing climate is a need of hour than choice. To know the climate in advance is crucial for grape growing farmers and grape export agencies for its better planning and security of grape industries from climate change perspective. In the present study, the Agro-Climatic Grape Yield (ACGY) model is developed on monthly scale climatic parameters using correlation, significance and multi-regression analysis approach. The developed model is statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the developed model is obtained as 1.03, 0.19, 0.03% and 0.19 respectively. Sensitivity analysis is carried out for the developed ACGY model using the parametric sensitivity method. In order to know the grape yield for future using developed ACGY model, climate scenarios are generated under Canadian Earth System Model (CanESM2) for three emissions Representative Concentration Pathways (RCP) as RCP2.6, RCP4.5, and RCP8.5. Model response variability is carried out to understand the variation of grape yield. It is observed that grape yield is showing adverse variation with the increase in minimum temperature in January and November months, and precipitation in August and November months. Whereas, minimum temperature in April and sum of monthly mean evapotranspiration showing accordance effect on the grape yield. It is recommended the use of ACGY model for grape yield estimations applicable for the present and future climate of the study area based on the predictive capability of developed model.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70130196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Western Balkan region, particularly Serbia, is faced with an increased frequency of extreme weather events, as a consequence of global climate change. However, there is still no enough research on how the effects of extreme weather events could be measured on the farm level. More importantly, there is no standard international methodology that is used regularly to address the issue. Therefore, the aim of this research was to evaluate the effects of extreme weather events on business performances of two the most common farm types in Serbia. To achieve this goal, the authors performed a financial loss assessment on a farm level. Panel models and R software environment were used to perform a multiple regression analysis allowing to indicate determinants of financial loss indicator depending on the farm’s production type. The results indicated that performance of both farm types is more influenced by drought than by floods. The regression analysis revealed that for both farm types financial stress is the most important independent variable.
{"title":"The influence of extreme weather events on farm economic performance – a case study from Serbia","authors":"S. Todorović, S. Ivanović, N. Bogdanov","doi":"10.36253/ijam-1073","DOIUrl":"https://doi.org/10.36253/ijam-1073","url":null,"abstract":"Western Balkan region, particularly Serbia, is faced with an increased frequency of extreme weather events, as a consequence of global climate change. However, there is still no enough research on how the effects of extreme weather events could be measured on the farm level. More importantly, there is no standard international methodology that is used regularly to address the issue. Therefore, the aim of this research was to evaluate the effects of extreme weather events on business performances of two the most common farm types in Serbia. To achieve this goal, the authors performed a financial loss assessment on a farm level. Panel models and R software environment were used to perform a multiple regression analysis allowing to indicate determinants of financial loss indicator depending on the farm’s production type. The results indicated that performance of both farm types is more influenced by drought than by floods. The regression analysis revealed that for both farm types financial stress is the most important independent variable.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70129479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-09DOI: 10.5194/egusphere-egu2020-10563
Danilo Rabino, M. Biddoccu, Giorgia Bagagiolo, G. Nigrelli, L. Mercalli, D. Cat Berro, F. Spanna, G. Capello, E. Cavallo
<p>Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.</p><p>A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the “Monferrato” area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962–2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.</p><p>The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using
历史天气数据是农业气象学的一种极其宝贵的资源，用于研究进化动力学和预测目的，以解决具有经济、社会和环境影响的农业经济学和管理选择。气候变异性及其后果的研究始于对随时间变化的观测，以及在历史气象观测系列的基础上确定原因。提供持久、完整和准确的数据集是预测和应对气候变化的基本要求。年间气候变化严重影响葡萄生产周期，决定了对酚期开始和持续时间的直接影响，最终影响了葡萄的收成和产量。事实上，气候变量，如气温和降水，会影响蒸散率、植物需水量以及葡萄藤的生理机能。在这方面，观察到的温暖天数的增加对葡萄质量构成了威胁，因为它在成熟时造成了糖含量、酸度、酚类和芳香成熟度的不平衡。进行了一项研究，调查气候变量和收获开始日期之间的关系，以评估葡萄藤在全球变暖情景下的反应。该研究是在；蒙费拉托”；该地区是意大利西北部一个雨水灌溉的山坡葡萄种植区。特别是，1962年至2019年，记录了在维佐拉诺实验农场（CNR-IMAMOTER）和周围葡萄园（隶属于Terre dei Santi酒窖）种植的不同当地酿酒葡萄品种的收获开始日期，然后通过回归分析与历史系列气候数据相关联。线性回归是基于生长和成熟季节计算的最高和最低日温度的平均值和降水量总和（1962–；2019），以及葡萄园的生物气候热指数Huglin指数进行的。气候数据来自机械气象站在实验农场收集的两个数据系列（1962-2002）和皮埃蒙特地区农业气象网机电站记录的第二个系列（2002-2019）。最后，分析中考虑了意大利气象学会提供的涵盖1962年至2019年的第三个长期连续序列。研究结果强调，年际气候变化具有普遍的积极温度趋势，显著影响葡萄的成熟，并逐渐预测收获开始日期。特别是，除降水量外，所有考虑的变量都与收获开始日期呈负相关，达到高度显著性（P＜0.001）。最高温度和Huglin指数的结果最好，尤其是使用最完整的数据集。使用包括过去15年在内的数据集获得的变化率（按绝对值计算）大于1962-2002年期间的结果，而且相关性具有更高的显著性。结果清楚地表明了温度趋势与收获的逐渐预期之间的关系，以及拥有长期连续的历史天气数据系列的重要性。
{"title":"Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy)","authors":"Danilo Rabino, M. Biddoccu, Giorgia Bagagiolo, G. Nigrelli, L. Mercalli, D. Cat Berro, F. Spanna, G. Capello, E. Cavallo","doi":"10.5194/egusphere-egu2020-10563","DOIUrl":"https://doi.org/10.5194/egusphere-egu2020-10563","url":null,"abstract":"\u0000 <p>Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.</p><p>A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the “Monferrato” area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962–2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.</p><p>The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43181042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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