Adopting precision agriculture to improve the cultivation of old wheat varieties in Tuscany (Italy)

Fabbri, Carolina; Napoli, Marco; Mancini, Marco; Brandani, Giada; Vivoli, Roberto; Orlandini, Simone

doi:10.3920/978-90-8686-888-9_57

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Optical indices are subject to variation, because light reflectance is influenced by changes in canopy structure and the N content of the plant during the growing season [55]. Hence, it is important to assess the characteristics of biomass to determine the relationship between optical indices and the N status of plants, and for predicting biomass and seed yield at harvest [56].…”

Section: Discussionmentioning

confidence: 99%

Testing Proximal Optical Sensors on Quinoa Growth and Development

et al. 2020

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Proximal optical sensors (POSs) are effective devices for monitoring the development of crops and the nitrogen (N) status of plants. POSs are both useful and necessary in facilitating the reduction of N losses into the environment and in attaining higher nitrogen use efficiency (NUE). To date, no comparison of these instruments has been made on quinoa. A field experiment conducted in Tuscany, Italy, with different POSs, has assessed the development of quinoa with respect to N status. Three sets of POSs were used (SPAD-502, GreenSeeker, and Canopeo App.) to monitor quinoa development and growth under different types of fertilizers (digestate and urea) and levels of N fertilization (100, 50, and 0 kg N ha−1). The present findings showed that in-season predictions of crop biomass at harvest by SPAD-502 and GreenSeeker optical sensors were successful in terms of the coefficient of determination (R2 = 0.68 and 0.82, respectively) and statistical significance (p < 0.05), while the Canopeo App. was suitable for monitoring the plant´s canopy expansion and senescence. The relative error (RE%) showed a remarkably high performance between observed and predicted values, 5.80% and 4.12% for GreenSeeker and SPAD-502, respectively. Overall, the POSs were effective devices for monitoring quinoa development during the growing season and for predicting dry biomass at harvest. However, abiotic stresses (e.g., heat-stress conditions at flowering) were shown to reduce POSs’ accuracy when estimating seed yields at harvest, and this problem will likely be overcome by advancing the sowing date.

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Section: Discussionmentioning

confidence: 99%

Testing Proximal Optical Sensors on Quinoa Growth and Development

et al. 2020

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“…Field experiments were established in October 2016 under rainfed conditions at the Giuseppe Chiarion farm, located in Monteroni d'Arbia, about 20 km south-east of Siena, Tuscany, Italy (43.2007 • N, 11.4182 • E, 160 m a.s.l.) [19]. Egyptian clover (Trifolium alexandrinum, L.) was the previous crop.…”

Section: Wheat Cultivationmentioning

confidence: 99%

Wheat Grain Composition, Dough Rheology and Bread Quality as Affected by Nitrogen and Sulfur Fertilization and Seeding Density

et al. 2020

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Flour from old varieties are usually considered very weak flours, and thus difficult to use in breadmaking especially when processed as Italian “Tipo 2” flour. Hence, the aim of our study was to understand if agronomic treatments can be used to improve flour processability and the quality of three old wheat varieties. An experimental strip-plot scheme was used: three old wheat varieties (Andriolo, Sieve, Verna), two seeding densities, three levels of nitrogen fertilization (N35, N80, and N135), and two levels of foliar sulfur fertilization. Analyzed parameters related to kernel composition, dough rheology and bread quality. Sulfur and nitrogen treatments significantly affected protein composition and dough alveograph strength, which increased by about 34% with nitrogen fertilization, and by about 14% with the sulfur treatment. However, only nitrogen fertilization affected bread characteristics. Crumb density significantly decreased from N35 to N135, while springiness and cohesiveness increased. On the other hand, sulfur did not improve breads. This highlight the importance of performing breadmaking tests in addition to the rheological determinations. The poor technological performance of old wheat flours can be improved with agronomical treatments designed to obtain higher-quality bread.

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“…Satellite platforms allow for the remote sensing of crops by free image acquisition, and, consequently, estimate crop N status in large-scale plots. Satellites such as Sentinel-2, launched by the European program Copernicus and the European Space Agency (ESA), provide more information on the detection of cultivation parameters, with a high resolution and a short revisit time [107,108]. Currently, Sentinel-2 has a revisit time of 5 days, capturing 13 spectral bands with 10 m of VIS/NIR bands, 30 m of NIR/SWIR bands, and 60 m of atmospheric bands as the ground spatial resolution [109].…”

Section: Satellite Platformsmentioning

confidence: 99%

Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

et al. 2023

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Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.

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Adopting precision agriculture to improve the cultivation of old wheat varieties in Tuscany (Italy)

Cited by 5 publications

References 10 publications

Testing Proximal Optical Sensors on Quinoa Growth and Development

Testing Proximal Optical Sensors on Quinoa Growth and Development

Wheat Grain Composition, Dough Rheology and Bread Quality as Affected by Nitrogen and Sulfur Fertilization and Seeding Density

Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

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