Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

Carreira, Emanuel; Serrano, João; Shahidian, Shakib; Nogales-Bueno, Julio; Rato, Ana Elisa

doi:10.3390/app112210638

Cited by 6 publications

(9 citation statements)

References 44 publications

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“…Additionally, the inclusion of five cereal species extended the range of protein content variability compared to the range found in any of the individual species. Also, both of the instruments tested in this study (FOSS-DS2500, with a spectral range of 400–2498 nm, and HL-EVT5, with a spectral range of 1350–2550 nm) encompassed the spectrum ranges relevant to protein determination in grain samples ( Section 3.2 ) [ 2 , 26 , 28 , 48 , 54 , 65 , 69 , 84 , 91 ], and sufficiently justified the next step, i.e., building the algorithms to infer the protein content from these two types of spectra ( Section 2.5 and 4.3).…”

Section: Discussionmentioning

confidence: 99%

“…In the case of the cereal-based food and feed industry [ 25 , 42 , 43 ], benchtop NIRS systems with calibrations based on single-cereal species are routinely utilized—rice [ 26 , 28 , 30 , 32 , 36 , 40 , 44 , 45 , 46 , 47 , 48 ], sorghum [ 43 , 49 , 50 ], wheat [ 27 , 51 , 52 , 53 , 54 , 55 ], corn [ 31 , 53 , 56 , 57 , 58 , 59 , 60 ], and barley [ 53 , 61 , 62 , 63 ]. These typically use a large number of single-species samples from different environments representing a relevant range of target trait variability [ 3 , 52 , 64 , 65 , 66 , 67 , 68 , 69 ]. With rising global attention on food quality, minor cereals (such as sorghum, fonio, teff, and millets) are being explored and promoted [ 70 , 71 , 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

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NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

Chadalavada

Anbazhagan

Ndour

et al. 2022

Sensors

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Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

Chadalavada

Anbazhagan

Ndour

et al. 2022

Sensors

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“…In the same study by Serrano et al [53], the authors obtained mean CP values under the canopy of trees of 22.9, 22.4, 15.9, 11.2, and 8% for the months of December, February, March, May, and June, respectively; outside the canopy, the values were 21.3, 19.8, 13.5, 9.8, and 6.3% for the months of December, February, March, May, and June, respectively. In a study where pasture samples were collected and CP was determined in Montado, at the peak of spring (30 March to 13 April), average CP values of 13.5%, 12.1%, and 10% were found for the region of Évora, for the Portalegre region, and for the Beja region [66]. In another study carried out on rainfed pastures in Montado, Serrano et al [67] obtained CP values for autumn, winter, and spring, under and outside the tree canopy, and in soils with the application of dolomitic limestone and without this application in the agricultural years 2018/2019 and 2019/2020.…”

Section: Pastures Characteristics and Managementmentioning

confidence: 99%

Montado Mediterranean Ecosystem (Soil–Pasture–Tree and Animals): A Review of Monitoring Technologies and Grazing Systems

et al. 2023

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Montado is an agro-silvo-pastoral ecosystem characteristic of the south of Portugal and called Dehesa in Spain. Its four fundamental components—soil, pasture, trees, and animals—as well as the climate make Montado a highly complex ecosystem. This review article provides an overview of the state of the art of Montado from the point of view of the agro-silvo-pastoral ecosystem and the scientific work carried out in this context. Thus, the aim is: (i) to describe and characterize the Montado ecosystem, as an agro-silvo-pastoral system; (ii) to reveal experimental tests carried out, technologies used or with the potential to be used in the monitoring of Montado; (iii) to address other technologies, carried out in similar and different agro-silvo-pastoral ecosystems from south Portugal. This review consists of three chapters: (a) components of Montado and their interactions; (b) advanced technologies for monitoring Montado; (c) grazing systems. No review article is known to provide an overview of Montado. Thus, it is essential to carry out research on grazing and its effects on the soil and pasture in the Montado ecosystem.

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“…Prolonged natural droughts often impair pasture production. Moreover, the precipitation variability, either in quantity or in seasons, affects pasture productivity and quality [ 6 ]. The spontaneous pastures of the Montado ecosystem generally have low productivity [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Sheep Grazing, Stocking Rates and Dolomitic Limestone Application on the Floristic Composition of a Permanent Dryland Pasture, in the Montado Agroforestry System of Southern Portugal

Carreira

Serrano

Gomes

et al. 2022

Animals

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The Montado is a complex agroforestry–pastoral ecosystem due to the interactions between soil–pasture–trees–animals and climate. The typical Montado soil has an acidic pH and manganese toxicity, which affect the pasture's productivity and pasture floristic composition (PFC). The PFC, on the other hand, can also be influenced by the type and intensity of grazing, which can lead to significant decreases in the amount of biomass produced and the biodiversity of species in the pasture. The objective of this study was to evaluate the effect of grazing type, by sheep, and different stocking rates on the PFC throughout the vegetative pasture cycle in areas with and without dolomitic limestone application. Thus, four treatments (P1UC to P4TC) were constituted: P1UC—without limestone application (U) and continuous grazing (CG); P2UD—U and deferred grazing (DG); P3TD—with the application of limestone (T) and DG; P4TC—T and CG. In DG plots, the placement and removal of the animals were carried out as a function of the average height of the pasture (placement—10 cm; removal—3 to 5 cm). The PFC was characterized in winter, at the peak of spring and in late spring. The PFC data were subjected to a multilevel pattern analysis (ISA). The combination of rainfall and temperature influenced the pasture growth rates and consequently the height of the pasture at different times of the year. Therefore, with the different growth rates of the pasture throughout the year, the sheep remain for different periods of time in the deferred grazing treatments. In the four treatments, 103 plant species were identified. The most representative botanical families in the four treatments were Asteraceae, Fabaceae and Poaceae. ISA identified 14 bioindicator species: eight for the winter period, three for the late spring vegetative period and three for the TC treatment.

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Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer

Cited by 6 publications

References 44 publications

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

Montado Mediterranean Ecosystem (Soil–Pasture–Tree and Animals): A Review of Monitoring Technologies and Grazing Systems

Effect of Sheep Grazing, Stocking Rates and Dolomitic Limestone Application on the Floristic Composition of a Permanent Dryland Pasture, in the Montado Agroforestry System of Southern Portugal

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