Fertilisation strategy and ground sensor measurements to optimise rice yield

Cordero, Eleonora; Moretti, Barbara; Miniotti, Eleonora Francesca; Tenni, Daniele; Gianluca, Beltarre; Romani, Marco; Sacco, Dario

doi:10.1016/j.eja.2018.07.010

Cited by 18 publications

(13 citation statements)

References 27 publications

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“…The NDRE index has the red-edge band that penetrates deep into the canopy of the plant, being more sensitive to the chlorophyll contained in plant biomass that exceeds the intrinsic saturation of NDVI (Li et al, 2014;Miller et al, 2018). Cordero et al (2018) identified that NDRE was efficiently better at determining the N content in rice leaves than indices such as NDVI and SPAD. Li, Zhang, & Huang (2014) found that GNDVI was better than NDVI and similar to or slightly less effective than NDRE for estimating leaf N status in corn.…”

Section: Canonical Correlationsmentioning

confidence: 99%

“…The green normalized difference vegetation index (GNDVI) is efficient in determining N (Wen et al., 2018) and estimating yield data (Wahab, Hall, & Jirström, 2018). Another vegetation index used is the normalized difference red‐edge (NDRE) index, which provides better crop analysis at more advanced stages and with the possibility of accurately measuring the canopy and N content (Cordero et al., 2018)…”

Section: Introductionmentioning

confidence: 99%

“…The green normalized difference vegetation index (GNDVI) is efficient in determining N (Wen et al, 2018) and estimating yield data (Wahab, Hall, & Jirström, 2018). Another vegetation index used is the normalized difference red-edge (NDRE) index, which provides better crop analysis at more advanced stages and with the possibility of accurately measuring the canopy and N content (Cordero et al, 2018) To effectively use vegetation indices in breeding programs, it is necessary to know their relationship with corn agronomic traits. Biometric techniques allow us to know the relationship between groups of traits, such as genotype × trait biplot (GTbiplot) and canonical correlations.…”

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confidence: 99%

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Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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The current challenge of corn (Zea mays L.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super-traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha -1 and high, 180 kg ha -1 ). At full bloom, the spectral variables green (550 nm), red (660 nm), red-edge (735 nm), and near-infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil-adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red-edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield. INTRODUCTIONThe world demand for corn (Zea mays L.) consumption has increased, which has led to the need for increased production.To meet this growing demand, it is necessary to invest in crop

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Section: Canonical Correlationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

et al. 2021

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“…The third lesson concerned the negative correlation between N-tot and yield, which resulted even after absorbing the N-pi in the N-tot. In the experimental plot studies, the correlations were generally positive, and even very high, until the maximum yield was reached 5,8 but results from the real fields are much less optimistic: i.e. Zavattaro et al 10 observed a sparse regression (+0.0125 t kgN-tot -1 ) of the yield (avg.…”

Section: N Correlationsmentioning

confidence: 96%

“…The fourth lesson is related the uncertainty of the reference parcels which identified at zero-input 11 , or at well-fertilized input 5 or by a corrective sufficiency index 8,5 , which are necessary to set up the production models and the PA. As shown in Figure 9, the NDRE response over different N-pre-pi was highly variable, that weakens the right point of reference.…”

Section: A Local Relativitymentioning

confidence: 99%

Rice Yield Advances Under Precision Agriculture: a Farm Lesson

Sarasso¹,

Sarasso²,

Finassi³

et al. 2019

JAR

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In precision agriculture (PA) fertilizing, based on soil testing, production maps and crop nitrogen (N) demand, is the key to maximizing yields and tempering fertilizer costs. A trend study has considered the output / input relationships performed on a farm that has progressively adapted to PA procedures over two decades. The evolutions of the variability parameters of yield, comprising the repeatability coefficient of repeated plots, the vegetative vigour (NDRE) at the panicle initiation (pi) stage, and the nitrogen utilization efficiency (NUE) were monitored and compared by means of mixed linear models over a six-year period, after the variable nitrogen (N) fertilization rate (VNFR) had been enlarged to the whole 230 ha of one farm. At pi key fertilization stage, a corrective dose was applied by tacking the correlation between Npi and the measured NDRE in strong negative mode. The evolution of the yield, for the 2012-2017 interval, based on 1165 ha -1 parcel-data, showed a significant yearly increase of 2.3% more than the regional trend (+0.5%). The variability parameters of the yield, that is, the standard deviation (+7.3%), range (+7.1%), coefficient of variation (+5.4%) and maximum (+2.1%) were enhanced over the years, but the minimum remained stable. The repeatability of the parcel yield generally appeared low (r = +0.31), but it tended to increase by 8.3% year -1 (P = 0.018). At the same time, the vegetational vigour also showed significant increases of the NDRE means (+3.0%) as well as of the maximum (+0.8%), but also large oscillations in the standard deviation and in the coefficient of variation. No significant regression of the NDRE on the coefficient of variation of the yield was established. The favorable increase in yield was found to be independent of the distributed N-total. A strong negative correlation (imposed) between N -pi and NDRE (-0.90) and a negative correlation with production were observed for a sample field (but in the area of maximum production). It is recommended that a partial correlation between Yield and N-tot should be considered in the I /O features for a parity of NDRE, which apparently decreases the negativity of the relationship. In short: with the same total input of N, the PA increased the yield, but also its variability -and it did not reduce the variability as predicted by the theory -by strengthening the repeatability. This is an evidence that in many of the parcels with minimum yield the limiting factors cannot be referred to the N availability.

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Highly active biostimulant Paecilomyces variotii extracts reduced controlled‐release urea application while maintaining rice yield

et al. 2021

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BACKGROUND The high cost of controlled‐release urea (CRU) has prompted this study to explore whether the amount of CRU can be reduced by adding biostimulants while maintaining or increasing rice yield. A 2 year field experiment was conducted with CRU at three levels (60%, 80%, and 100% of the recommended nitrogen (N) fertilizer) and a novel biostimulant Paecilomyces variotii extract (ZNC), to investigate their synergistic effects on yield, nitrogen use efficiency (NUE), and net profitability of rice. RESULTS Controlled‐release urea achieved a significantly higher gain yield and NUE than conventional urea with the same N level, which could be attributed to its N supply. Even if the N level of CRU was reduced by 40%, both rice yield and net profit were still significantly higher than for the full amount of urea. Paecilomyces variotii extract sprayed on the surface of CRU at a dose of only 87.5 mL ha−1 exhibited ultra‐high effectiveness by increasing the panicles, the N accumulation, and the rice yield. Controlled‐release urea enriched by ZNC achieved significantly higher gain yield than CRU alone, increasing the yield by 9.2% and 8.7%, respectively, in 2 years under the full recommended N rate. The combination of 80% CRU and ZNC showed no significant difference in rice yield from treatment with 100% CRU, indicating that the rate of CRU could be reduced by ZNC. The application of ZNC further increased NUE, N partial factor productivity, and net profit. CONCLUSION The CRU and ZNC combination provided a feasible approach for reducing N input while maintaining rice yield and agricultural sustainability. © 2021 Society of Chemical Industry.

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Fertilisation strategy and ground sensor measurements to optimise rice yield

Cited by 18 publications

References 27 publications

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

Genotype × trait biplot and canonical correlations for spectral and agronomic traits in corn

Rice Yield Advances Under Precision Agriculture: a Farm Lesson

Highly active biostimulant Paecilomyces variotii extracts reduced controlled‐release urea application while maintaining rice yield

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