بررسی نقش وضعیت تغذیه‌‌ای برگ بر شاخص‌‌های فتوسنتزی درخت زیتون

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران

2 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران

3 دانشیار، موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

4 دانشیار، بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان زنجان، سازمان تحقیقات، آموزش ‏و ترویج کشاورزی، زنجان، ایران

چکیده

توسعه کشت زیتون مستلزم مطالعات دقیق برای دستیابی به بیشترین عملکرد کمی و کیفی محصول با اعمال راهکارهای مدیریتی از جمله تغذیه است. در این پژوهش روابط متقابل بین متغیر‌‌های تبادلات گازی با عناصر غذایی موجود در برگ درختان زیتون، رقم آربیکن با سیستم کشت فوق متراکم (SHD)، با توجه به میزان هدایت الکتریکی (EC) و کربنات کلسیم  معادل (TNV) خاک‌‌های منطقه طارم سفلی، بررسی و تحلیل گردید. با استفاده از روش تشخیص چندگانه عناصر غذایی (CND)، وضعیت عناصر غذایی و ناهنجاری‌‌های تغذیه‌‌ای مشخص و روابط و اثرات آن بر شاخص‌‌های فتوسنتزی تعیین شد و در نهایت با آنالیز مؤلفه‌های اصلی (PCA) اثرات متغیر‌‌ها بر عملکرد بدست آمد. نتایج نشان داد متغیر‌‌های فتوسنتزی همبستگی مثبت و معنی‌‌داری با عناصر روی، فسفر و مس و همچنین همبستگی بالایی با عملکرد باغ‌‌ها داشتند. میانگین ترتیب نیاز عناصر غذایی باغ‌‌ها به صورت Ca>Mg>N>P>K در عناصر پر مصرف و Zn>B>Mn>Cu در عناصر کم مصرف بود. بر اساس نتایج PCA باغات با عملکرد بالا اثرپذیری مثبت بالاتری با عناصر غذایی فسفر، پتاسیم، روی و مس داشتند که موجب افزایش شدت فتوسنتز و عملکرد شد. همچنین اثرات پنهان کمبود کلسیم و منگنز بر کاهش پارامترهای فتوسنتزی و عملکرد با روشPCA  مشخص گردید. 

کلیدواژه‌ها


عنوان مقاله [English]

Effects of leaf nutrient contents on photosynthetic indices in Olives tree

نویسندگان [English]

  • Samira Vahedi 1
  • Sina Beshsrat 2
  • Naser Davatgar 3
  • Mehdi Taheri 4
1 Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran‎
2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran‎
3 Associate Professor, Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
4 Assistant Professor, Research Department, Zanjan Agriculture and Natural Resources Research and Education Center, Agricultural ‎Research, Education and Extension Organization (AREEO), Zanjan, Iran
چکیده [English]

The development of olive cultivation needs detailed studies to achieve the maximum yield and product quality by applying management plans including nutrition. In this research, the interactions of gas exchange and photosynthetic parameters were investigated with leaf nutrients contents of olive trees (cv. ‘Arbequina’) in super high density (SHD) cultivation under saline and calcareous soils condition in Tarom Sofla, Zanjan, Iran. The nutritional disorders and their effects on photosynthetic indices were revealed using the compositional nutrient diagnosis (CND) index micronutrients and macronutrients status. Finally, principal component analysis (PCA) was used to determine, the effects of all parameters on yield. The results showed that photosynthetic parameters had a significant positive correlation with nutrients such as zinc, phosphorus, and copper. Photosynthesis, transpiration, and stomatal conductance had highly correlated with yield of olive trees, which are important factors in increasing yields. The final CNDr2 index showed nutritional disorders in most orchards in the low yielding group. The average order of nutrient requirement is Ca> Mg> N> P> K in macronutrients and Zn> B> Mn> Cu in micronutrients in the group of low yielding orchards. The principal component analysis showed that highest yield of olive trees had a higher positive correlation than other orchards with P and K in macronutrients and Zn, and Cu in micronutrients; this increased the rate of photosynthesis and yield. PCA also showed the hidden effects of Ca and Mn deficiency on the reduction of photosynthetic parameters and yield.

کلیدواژه‌ها [English]

  • Arbequina
  • CND Index
  • Nutritional disorders
  • Principal Component Analysis
  • Super High Density‎
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