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  • فاطمه مدیری، محمد معظمی*، کامران الماسیه

    داشتن آمار و اطلاعات به روز از سطح زیر کشت محصولات کشاورزی یک ضرورت در طراحی الگوی کشت، مدیریت منابع آب و ارزیابی اثرات زیست محیطی ناشی از آن است و در این رابطه سنجش از دور یک ابزاری سودمند محسوب می شود. در این تحقیق سطح زیر کشت گندم در شهرستان شوشتر استان خوزستان با استفاده از تصاویر ماهواره ای سنتینل-2 برآورد شد. به منظور شناسایی مزارع گندم، سری زمانی شاخص تفاضل پوشش گیاهی نرمال شده منطبق با فنولوژی گیاه گندم در منطقه، محاسبه شد و مبنای تهیه نمونه های تعلیمی قرار گرفت. در ادامه از دو روش طبقه بندی حداکثر احتمال و ماشین بردار پشتیبان برای طبقه بندی تصاویر و تفکیک مزارع گندم استفاده شد. نتایج ارزیابی صحت نشان داد که روش ماشین بردار پشتیبان با دقت کلی 5/98 و ضریب کاپای 5/96 دقت بالاتری نسبت به روش حداکثر احتمال داشته و به عنوان مبنای محاسبه مساحت مزارع گندم قرار گرفت. نتایج نشان داد که مساحت اراضی زیر کشت گندم 48233 هکتار به دست آمد که تفاوت اندکی با برآوردهای موجود داشت. بر اساس نتایج این تحقیق می توان اظهار کرد که استفاده از روش سری زمانی شاخص تفاضل پوشش گیاهی نرمال شده در ترکیب با روش طبقه بندی ماشین بردار پشتیبان، امکان محاسبه سریع و دقیق سطح زیر کشت گندم را فراهم کرده و می توان از این روش برای سایر محصولات کشاورزی و برنامه ریزی الگوی کشت در منطقه استفاده نمود.

    کلید واژگان: حداکثر احتمال، سری های زمانی، شاخص NDVI، گندم و ماشین بردار پشتیبان
    Fatemeh Modiri, Mohammad Moazami*, Kamran Almasieh
    Introduction

    Up-to-date information on growing area is necessary for cropping pattern, and remote sensing is a useful tool in achieve this goal (Hunt et al., 2019). Among various image classification algorithms and vegetation indices for crop type identification, machine learning methods and normalized difference vegetation index (NDVI) have had better results (Mousavi et al., 2020). In addition, attention to plant phenological stages and using multi-temporal images have led to more accurate crop distinction. The aim of this study was estimation of wheat growing areas in Shushtar county in Khuzestan province in Iran.

    Materials and Methods

    Time series of NDVI index was obtained from eight Sentinel-2 satellite images from November to June, which correspond to the wheat growth stages. Then, maximum likelihood and support vector machine algorithms were used to identify wheat fields. To implement the classification methods, training and test samples were needed, which were obtained by matching the NDVI diagram of the wheat crop growth stages and field surveys.

    Results

    Error matrix was used to evaluate the classification results. Based on this, the support vector machine with overall accuracy and Kappa coefficient of 98.5 and 96.5 percent, respectively, had higher accuracy than the maximum likelihood with overall accuracy and Kappa coefficient of 97.8 and 95 percent, respectively. In addition, considering the wheat crop phenological stages using the time series of NDVI in training samples, selection increased the classifications accuracy. Based on the support vector machine results, the total wheat growing areas was estimated to be 48233 hectares, which showed a small deviation from the available surveys.

    Conclusion

    Using the pattern of NDVI time series to obtain training samples showed the efficiency of plant indices in estimation of wheat growing areas according to the crop phenological stages. In addition, the results revealed that the support vector machine classification was more accurate than the maximum likelihood in the study areas, and it was considered as a base method. One reason for the appropriate performance of the support vector machine was appropriate distribution and adeqaute number of training samples based on wheat phenological stages. It can be concluded that the time series of the normalized difference vegetation index in combination with the support vector machine classification provided the possibility of quick and accurate estimation of the wheat growing areas in Shushtar county in Khuzestan in Iran.

    Keywords: Maximum Likelihood, NDVI, Support Vector Machine, Training Samples, Wheat
  • علیرضا کوچکی، مهدی نصیری محلاتی، سمانه نجیب نیا، بختیار لله گانی، حسن پرسا
    حبوبات، به خاطر دارابودن ویژگی های تغذیه ای و زراعی قابل ملاحظه، جایگاه خاصی در نظام های کشاورزی جهان دارند. این گیاهان در برخی کشورهای درحال توسعه به عنوان یک منبع پروتئینی باارزش محسوب شده و به علت این که گیاهانی کم توقع هستند، در اراضی حاشیه ای و سیستم های زراعی کم نهاده، کشت وکار می شوند. این گیاهان با تثبیت نیتروژن هوا موجب افزایش مقدار نیتروژن خاک می شوند. حبوبات، تاریخچه کشت وکار طولانی در ایران دارند و برخی معتقدند که بعضی از آنها مانند عدس (Lense culinaris Medik) و نخود (Cicer arietinum L.) در این کشور اهلی شده اند. این پژوهش به منظور بررسی تنوع کنونی حبوبات در ایران و پیش بینی روند آینده آن انجام شد. بدین منظور، اطلاعات مربوط به تنوع زیستی، عملکرد، سطح زیرکشت و تولید، جمع آوری شد و با ارزیابی شاخص شانون و سری های زمانی تجزیه و تحلیل انجام گرفت. براساس نتایج به دست آمده، تنوع حبوبات دیم کشور در سال 1382، به میزان 19/1برابر سال 1362 افزایش یافته و پیش بینی های انجام شده تا سال1400 نیز روند افزایش تنوع حبوبات دیم کشور را 22/1برابر نسبت به سال1382 نشان می دهد. این در حالی است که تنوع حبوبات آبی در سال1382 تنها 81/0برابر سال1362 بوده و ادامه این روند نشان داد که تنوع حبوبات آبی در سال1400، معادل 88/0 سال1382 خواهد بود. به این ترتیب، هرچند میانگین تنوع کل حبوبات کشور طی سال های 1362 تا 1382 تقریبا ثابت بوده است (03/1)، اما پیش بینی ها، کاهش10درصدی میانگین تنوع کل حبوبات کشور را در سال1400 نسبت به سال1382 نشان داد. تولید کل لوبیا، نخود و عدس در سال 1382 به ترتیب 29/3، 61/2 و 83/4 برابر سال 1362 افزایش یافته و در مورد سایر حبوبات، تغییر زیادی نداشته است. سطح زیرکشت کل (لوبیا، نخود، عدس) و نیز سایر حبوبات در سال 1382، به ترتیب 64/2، 50/2، 55/5 و 15/1 برابر سال 1362 افزایش یافته است؛ اما به نظر می رسد که عملکرد در طول این سال ها تغییر چندانی نداشته است؛ به جز در مورد لوبیا که به میزان 24/1برابر افزایش یافته است. بنابراین می توان گفت که افزایش تولید در اثر افزایش سطح زیرکشت اتفاق افتاده است.
    کلید واژگان: تنوع زیستی، تولید، حبوبات، سری های زمانی
    Alireza Koocheki, Mahdi Nassiri Mahallati, Samaneh Najib Nia, Bakhtiar Lalehgani, Hassan Porsa
    Introduction
    In recent years due to problems associated with intensive agricultural systems, the role of legumes in the sustainability of cropping systems has been accelerated (Draper, 2006). Currently, one of the challenges of energy intensive agricultural systems is monoculture, which is associated with low biological diversity (Carmine, 2007). Iran has been considered as the area with low agricultural diversity and dominance of few crops particularly cereals in the main cropping systems (Nassiri Mahallati et al., 2003). The most common index of plant diversity is the SHANNON index. In agroecosystems, a Shannon index of 3 is rare (Meng et al., 1999). Researchers evaluated agrobiodiversity of agricultural systems at species, variety and cropping systems in a comprehensive survey for Iran and they found that the diversity at all levels have been declining due to introduction of new agricultural technology (Koocheki et al., 2005). They found that for wheat and rice which are the main cereal crops with high variety richness the SHANNON index ranges from 1.5 to 1.7. The aim of present study was to evaluate biodiversity of pulse crops in Iran and the future trends of yield, acreage and production.
    Materials And Methods
    In this survey, the status of pulse crops in different provinces of the country from 1983 to 2003 was evaluated. These crops were bean (Phaseolus vulgaris), chickpea (Cicer arietinum) and lentil (Lense culinaris). Other species of this category were classified as other pulse crops. The SHANNON index (H) was calculated based on the cultivated area (Smale et al., 2003): For evaluating of the trends in biodiversity, cultivated area, production and yield, time series were used by the following formula: Yt = f (t) et (1)
    Where, Yt is the variable at the time of t, f(t) is a function which describes Y on the bases of time and et is the prediction error of the time of t. Prediction of trend was calculated by direct method (Patchet, 1982) and the first year of data was considered as starting point. In this study, based on the type of time series, double dynamic mean and WINTERS method were used for the future prediction. In the WINTERS method, prediction is made on the bases of harmonized mean from time series data, in such a way that the highest weight is given to the nearest data and the weight of data is decreased with aversions from the present time.
    Results And Discussion
    The diversity index under rainfed condition showed an increasing trend whereas the reverse is true for the diversity index under irrigated condition. The diversity index for the whole pulse crops (rainfed plus irrigated) was inconsistent, but an increasing trend was observed from 1983 to 2003 and a decreasing trend was observed afterwards. The decreasing trend was also true for the prediction towards the year 2021. Similar trend as for the period 1983 to 2003 is expected up to the year 2021.
    The acreage and yield for irrigated has been increased for bean, whereas for chickpea, the rainfed acreage has been increased. The lentil was similar to chickpea. The acreage of the other pulse crops showed a slightly increasing trend from 1983 to 2003. This rise is due, in part, to an increase in the irrigated acreage. This trend is likely to increase slightly over the coming years and is likely it will show a consistent trend afterwards. The yield of other pulse crops decreased from 1983 to 2003. This reduction is due to a reduction under rainfed conditions. The yield of other pulse will decreased until 2021.
    The same trend is shown for production. Although production of bean has shown an increase, this increase is mainly due to an increase under irrigated condition, whereas the rise in lentil and chickpea is due to rainfed production. The same trend is expected up to the year 2021 for these crops. The production of the other pulse crops showed a slight decreasing trend from 1983 to 2003. This decreasing is due to a decreasing under irrigated and rainfed conditions. The trend will show increase for a few time and it will show a consistent trend afterwards.
    Only the yield for bean has an increasing trend and this is associated with this fact that bean has been under irrigated conditions. No increase is shown for other crops which are produced under rainfed condition. Therefore, it may be concluded that the production increase for bean is mainly due to yield and acreage increase, whereas for other pulse crops, the production increase is due to increasing in the acreage, because the yield showed somewhat the decreasing trend during these years. Prediction of diversity index of rainfed pulse crops up to the year 2021 indicates an increase of 1.22 folds compared to the year 2003. However diversity index for irrigated and irrigated plus rainfed showed a reduction of 0.88 and 0.9 folds, respectively. The magnitude of the change of production, yield and acreage for different pulse crops is shown. It is apparent that the prediction of production for the bean up to the year 2021, under rainfed, irrigated and rainfed plus irrigated will be increased by 2.96, 1.60 and 1.95 folds compared with the year 2003. These values for the yield of bean under similar conditions are 1.59, 1.03 and 1.22 folds, respectively and also for the acreage will be 1.88, 1.58 and 1.59 folds, respectively.
    Acreage for the chickpea and lentil for rainfed, irrigated and irrigated plus rainfed will be 2.47, 0.37, 2.41 and 2.63, 1.31, 2.54 folds, respectively. These values for other pulse crops will be 4.96, 1.49 and 2.01 folds, respectively. An increasing trend of the yield has been reported for the future for different crops (Khush, 1999; Rosegrant et al., 2001). Borlog (2000) has stated that yield growth which is associated with genetic improvement and the use of chemical fertilizers, pesticides and irrigation systems, will be continued, in the future.
    We can predict that the rate of acreage and production of pulse crops in Iran, specially three important crops, bean, chickpea and lentil will increase until 2021. However, under rainfed conditions, it is likely that the yields, particularly of chickpea and lentil will stabilize.
    Conclusions
    This study was conducted to evaluate the trends in biodiversity, cultivated area, production and yield of pulse crops in different provinces of Iran from 1983 to 2003. Time series formula was used for such evaluation. The diversity indices studied under irrigated and rainfed conditions. It seems the results can be useful for policy makers, scientists and food industry to improve food security in country.
    Keywords: Biodiversity, Production, Pulse crops, Time series
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