High temperatures and decreased rainfall are detrimental to yield in chickpea (Cicer arietinum L.), particularly during grain filling. This study aimed to (i) assess the individual and combined effects of drought and heat stress on biochemical seed-filling processes, (ii) determine genotypic differences in heat and drought tolerance, and (iii) determine any cross-tolerance. Plants were grown outdoors in the normal growing season when temperatures during seed filling were 32-20°C; heat stress). Half of the pots were kept adequately watered throughout, but water was withheld from the others from the initiation of seed filling until the relative leaf water content reached 50% of the irrigated plants (drought stress); all plants were rewatered thereafter until seed maturit. Water was withheld for 13 days (normal sowing) and 7 days (late sowing), so soil moisture decreased by 54-57%. Tests on leaves and seeds were performed after the stress. Individual and combined stress damaged membranes, and decreased cellular oxidising ability, stomatal conductance, PSII function and leaf chlorophyll content; damage was greater under combined stress. Leaf Rubisco activity increased with heat stress, decreased with drought stress and decreased severely with combined stress. Sucrose and starch concentrations decreased in all seeds through reductions in biosynthetic enzymes; reductions were greater under combined stress. These effects were more severe in heat- and drought-sensitive genotypes compared with drought-tolerant genotypes. Drought stress had a greater effect than heat stress on yield and the biochemical seed-filling mechanisms. Drought- and heat-tolerant genotypes showed partial cross-tolerance.

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Individual and combined effects of transient drought and heat stress on carbon assimilation and seed filling in chickpea.

The effect of high temperature stress on male and female reproduction in plants

Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference Images of the rape field were first classified using K-means method based on Commission Internationale de l’Eclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 089) The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 091 Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 093 and RMSEP = 1618), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 095 and RMSEP = 1413 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape

Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

Spectral considerations for modeling yield of canola

Different techniques utilized for mapping various crops are mainly based on using training dataset. But, due to difficulties of access to a well-represented training data, development of automatic methods for detection of crops is an important need which has not been considered as it deserves. Therefore, main objective of present study was to propose a new automatic method for canola (Brassica napus L.) mapping based on Sentinel 2 satellite time series data. Time series data of three study sites in Iran (Moghan, Gorgan, Qazvin) and one site in USA: (Oklahoma), were used. Then, spectral reflectance values of canola in various spectral bands were compared with those of the other crops during the growing season. NDVI, Red and Green spectral bands were successfully applied for automatic identification of canola flowering date using the threshold values. Examination of the fisher function indicated that multiplication of the near-infrared (NIR) band by the sum of red and green bands during the flowering date is an efficient index to differentiate canola from the other crops. The Kappa and overall accuracy (OA) for the four study sites were more than 0.75 and 88%, respectively. Results of this research demonstrated the potential of the proposed approach for canola mapping using time series of remotely sensed data.

Automatic canola mapping using time series of sentinel 2 images

Seed yield and water use efficiency of canola (Brassica napus L.) as affected by high temperature stress and supplemental irrigation

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Effect of High Temperature Stress and Supplemental Irrigation on Flower and Pod Formation in Two Canola (Brassica napus L.) Cultivars at Mediterranean Climate

Amir Hossain Shirani Rad

Papers

Seed yield and water use efficiency of canola (Brassica napus L.) as affected by high temperature stress and supplemental irrigation

Effect of High Temperature Stress and Supplemental Irrigation on Flower and Pod Formation in Two Canola (Brassica napus L.) Cultivars at Mediterranean Climate