Explorative Frequency Analysis of Leaf Temperature Behavior of Maize (Zea mays subsp. mays) at Water Deficit

Kögler, Friederike; Söffker, Dirk

doi:10.3390/plants8040105

Cited by 8 publications

(5 citation statements)

References 57 publications

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“…The authors conducted an in controlled laboratory conditions studying the leaf temperature under water deficit of the substrate using infrared camera. As a result of the analysis, the authors did not find any correlation between the leaf temperature and water deficit (Kögler and Söffker, 2019). According to our results, a negative correlation was found between ability to germinate under osmotic stress in lines carrying the favorable allele by rspC1090 marker and the leaf surface temperature of studied maize lines.…”

Section: Resultssupporting

confidence: 40%

“…A positive correlation was determined between the leaf temperature and days to flowering, relative injury and chlorophyll content b (Chla/b) (Coskun et al, 2011). Kögler and Söffker (2019) studied the features of explorative frequency analysis for measuring the leaf temperature of maize under water stress. The authors conducted an in controlled laboratory conditions studying the leaf temperature under water deficit of the substrate using infrared camera.…”

Section: Resultsmentioning

confidence: 99%

“…It was assayed 21 maize plant of each line with infrared pyrometer Benetech GM550 (China), sensitivity is 0.1ºС. The leaf temperature was measured at the hottest period of day between 1.30 pm and 2.30 pm (Liu et al, 2011;Kögler and Söffker, 2019). For addition estimation of heat and drought tolerance of maize lines which had the different combination of alleles, the yield of studied lines was assayed.…”

Section: Field Experimentsmentioning

confidence: 99%

“…Also, there are studies which confirm that the determination of leaf surface temperature is used for a quick assessment of the plant water status. The water deficit affects leaf temperature by gradually decreasing transpiration as a result of stomata closure (Kögler and Söffker, 2019). Hence, decreased transpiration leads to an increase a leaf temperature.…”

Section: Introductionmentioning

confidence: 99%

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The Estimation of Heat and Drought Tolerance Maize Lines Using Dna Markers

Prysiazhniuk

Honcharov²,

Shytikova

et al. 2020

AGR

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The main factor which causes to decrease maize grain yield is drought. In most regions where maize is grown, the water stress during the growing period is caused by both lack of soil moisture and high air temperature. The purposes of our study were the estimation and selection of maize lines for drought and heat tolerance based on DNA markers and determination of the correlation between CAPS markers and plant ability to resist the water stress. As the result of study, the significant differences were found between leaf temperature of maize lines which contained favorable alleles by both CAPS markers in 2018 and 2019 (35.72 and 34.41ºC respectively), LSD0.05=1.27. The leaf temperature of maize lines which had SNP (A) by dhn С397 (36.95ºC) differenced significantly with lines contained favorable allele by rspC1090 or lines with no favorable alleles in 2018 (33.68 and 34.35ºC respectively). Based on analysis by seeds germinating in sucrose solution the significant differences were observed between the amount of sprouted seeds in lines contained SNP(G) by rspC1090 and lines without any favorable allele (4% and 2.25% respectively), LSD0.05= 1.70. As the result of correlation analysis, the positive correlation was determined between SNP(A) by dhnС397 marker in maize lines and leaf temperature in 2018-2019 (r=0.16). The positive correlation was observed between SNP(G)byrspC1090 and the percent of sprouted seeds in sucrose solution (r=0.31). Thus, for complex estimation and maize line selection for drought and heat tolerance it could be recommended to use two CAPS markers dhnC397 and rspC1090.

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Section: Resultssupporting

confidence: 40%

Section: Resultsmentioning

confidence: 99%

Section: Field Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Estimation of Heat and Drought Tolerance Maize Lines Using Dna Markers

Prysiazhniuk

Honcharov²,

Shytikova

et al. 2020

AGR

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“…The stomata close in response to soil water depletion, leading to decreased water uptake, gas exchange, and photosynthesis [6]. Thus, water-stressed plants would generally have less transpiration and thus higher temperatures than those not affected by the stress [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands

Kapari,

Sibanda,

Magidi

et al. 2024

Drones

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Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R2 = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R2 = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.

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