Factor-Specific Nutrient Modeling and Management of Agricultural and Forest Crops

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant–Soil Interactions".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 14546

Special Issue Editors


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Guest Editor
1. Department of Soils and Agri-Food Engineering, Laval University, Quebec, Canada
2. Department of Soils, Federal University of Santa Maria, Santa Maria - RS,Brazil
Emeritus professor at Université Laval, Québec, Canada, and visiting professor at Universidade Federal de Santa Maria, Rio Grande do Sul, Brazil
Interests: Compositional Nutrient Diagnosis (CND) and soil indices for phosphorus management in agroecosystems; predict nutrient requirements of agricultural and forest crops at local scale

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Guest Editor
Indian Council of Agricultural Research—Indian Agricultural Research Institute, Gogamukh 787034, Assam, India
Interests: soil fertility; plant nutrition; nutrient diagnosis; nutrient mapping; microbial consortia and rhizosphere engineering; integrated nutrient management; advanced citrus production systems and precision citriculture
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Special Issue Information

Dear Colleagues,

Alexander von Humboldt’s principles of biogeography, elaborated on more than 200 years ago to explain the natural occurrence of living systems based on facts and measures, can now be applied to diagnose the nutrient status of anthropic ecosystems using tools of artificial intelligence and compositional data analysis. In this Special Issue, we welcome articles (original research, reviews, modelling approaches, perspectives, opinions) focusing on the integration of factors affecting yield and quality of agricultural crops and the mineral nutrition of forest crops at local scale. Regional nutrient management recommendations often fail at local scale by not accounting for key factor interactions. Environmental and managerial factors, soil and tissue tests, remote and proximate sensing, ionomes of species down to cultivars or hybrids, biofortification and food contamination by trace metals must be addressed in detail to reach accurate predictions of crop performance and environmental quality at local scale. This warrants uniform data collection protocols, ethical data sharing, and large data sets; hence, close collaboration among stakeholders is required to trustfully collect metadata and the results of field trials, laboratory analyses, mapping, and surveys. Machine learning and compositional models are unprecedented tools to process factor interactions and to reach productive soil–plant systems at local scale.

Prof. Léon Etienne Parent
Dr. Anoop Kumar Srivastava
Guest Editors

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Keywords

  • Plant ionomics
  • Genetic, environmental, and managerial growth factors for nutrient recommendation
  • Yield and quality of agricultural and forest crops
  • Biofortification and nutrient dense crops
  • Contamination by trace metals
  • Data collection protocols
  • Machine learning (ML) predictive models
  • Compositional data analysis (CoDa) models

Published Papers (5 papers)

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Research

22 pages, 1409 KiB  
Article
Seasonal Fe Uptake of Young Citrus Trees and Its Contribution to the Development of New Organs
by Mary-Rus Martínez-Cuenca, Belen Martínez-Alcántara, Jorge Millos, Francisco Legaz and Ana Quiñones
Plants 2021, 10(1), 79; https://doi.org/10.3390/plants10010079 - 02 Jan 2021
Cited by 3 | Viewed by 1980
Abstract
This work quantifies Fe uptake in young citrus trees, its partitioning among plant compartments, and the contribution of the Fe absorbed from fertilizer to the development of new tissues. A soil pot experiment was conducted using 4-year-old clementine trees (Citrus clementina Hort [...] Read more.
This work quantifies Fe uptake in young citrus trees, its partitioning among plant compartments, and the contribution of the Fe absorbed from fertilizer to the development of new tissues. A soil pot experiment was conducted using 4-year-old clementine trees (Citrus clementina Hort ex Tan), and a dose of 240 mg Fe was applied by labeled fertilizer (92% atom 57Fe excess). Plants were uprooted at five different phenologic states: end of flowering (May 15), end of fruit setting and fruit drop (July 1), two fruit growing moments (August 1 and October 15), and at complete fruit maturity (December 10). The Fe accumulated in the root system exceeded 90% of the total Fe content in the plant. All organs progressively enriched with 57Fe (8.5–15.5% and 7.4–9.9% for young and old organs, respectively). Reproductive ones reached the highest increase (111% between May and October). 57Fe enrichment from woody organs reflects an increasing gradient to sink organs. The root system accumulated 80% of the Fe absorbed from the fertilizer, but the young organs accumulated relatively more Fe uptake during flowering and fruit setting (15.6% and 13.8%, respectively) than old organs (around 9.8%). Although iron derived from fertilizer (Fedff) preferably supplied young organs (16.7–31.0%) against old ones (2.5–14.9%), it only represented between 13.8% and 21.4% of its content. The use efficiency of the applied Fe (FeUE) barely exceeded 15%. The lowest FeUE were found in young and old organs of the aerial part (1.1–1.8% and 0.7–1.2%, respectively). Since the pattern of the seasonal absorption of Fe is similar to the monthly distribution curve of the supplied Fe, it is recommended that the application of Fe chelates in calcareous soils should be performed in a similar way to that proposed in this curve. Full article
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21 pages, 2171 KiB  
Article
Nutrient Diagnosis of Fertigated “Prata” and “Cavendish” Banana (Musa spp.) at Plot-Scale
by Antonio João de Lima Neto, José Aridiano Lima de Deus, Vagner Alves Rodrigues Filho, William Natale and Léon E. Parent
Plants 2020, 9(11), 1467; https://doi.org/10.3390/plants9111467 - 30 Oct 2020
Cited by 16 | Viewed by 2945
Abstract
Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the [...] Read more.
Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 “Prata” and “Cavendish” plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models. Full article
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21 pages, 5823 KiB  
Article
Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem
by Serge-Étienne Parent, Jean Lafond, Maxime C. Paré, Léon Etienne Parent and Noura Ziadi
Plants 2020, 9(10), 1401; https://doi.org/10.3390/plants9101401 - 21 Oct 2020
Cited by 11 | Viewed by 2878
Abstract
Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological [...] Read more.
Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale. Full article
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15 pages, 846 KiB  
Article
Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods
by Betania Vahl de Paula, Wagner Squizani Arruda, Léon Etienne Parent, Elias Frank de Araujo and Gustavo Brunetto
Plants 2020, 9(8), 1049; https://doi.org/10.3390/plants9081049 - 18 Aug 2020
Cited by 16 | Viewed by 2790
Abstract
Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors [...] Read more.
Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders. Full article
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14 pages, 2122 KiB  
Article
Classification of Pigeonpea (Cajanus cajan (L.) Millsp.) Genotypes for Zinc Efficiency
by Sanjib Kumar Behera, Arvind Kumar Shukla, Pankaj Kumar Tiwari, Ajay Tripathi, Pooja Singh, Vivek Trivedi, Ashok Kumar Patra and Soumitra Das
Plants 2020, 9(8), 952; https://doi.org/10.3390/plants9080952 - 28 Jul 2020
Cited by 12 | Viewed by 3342
Abstract
Pigeonpea (Cajanus cajan (L.) Millsp.) is grown globally for its protein-rich seed. However, low availability of soil zinc (Zn) adversely affects the seed yield of pigeonpea. The present study was therefore conducted to assess the Zn efficiency of pigeonpea genotypes based on [...] Read more.
Pigeonpea (Cajanus cajan (L.) Millsp.) is grown globally for its protein-rich seed. However, low availability of soil zinc (Zn) adversely affects the seed yield of pigeonpea. The present study was therefore conducted to assess the Zn efficiency of pigeonpea genotypes based on seed yield and seed Zn uptake efficiency. Field experiments were conducted at the Indian Council of Agricultural Research–Indian Institute of Soil Science, Bhopal, India with twenty different pigeonpea genotypes and two levels of Zn application under a split-plot design. The two levels of Zn were low (without application of Zn fertilizer) and high (with application of 20 kg Zn ha−1 (as ZnSO4∙7H2O) as basal soil application, in conjunction with three foliar sprays of 0.50% (w/v) ZnSO4∙7H2O aqueous solution) (with 0.25% lime as neutralizing agent) at flowering, pod formation, and pod filling stages). Application of Zn improved plant height, branches plant−1, pods plant−1, seeds pod−1, and 100 seed weight of pigeonpea genotypes differently. The mean seed yield, seed Zn concentration, and seed Zn uptake of the genotypes increased from 1.71 to 2.12 t ha−1, 32.4 to 43.0 mg kg−1, and 54.9 to 90.6 g ha−1, respectively, with application of Zn. The seed yield efficiency index (SYEI) and Zn uptake efficiency index (ZUEI) of pigeonpea genotypes varied from 67.0 to 92.5 and from 47.0 to 69.9, respectively. Based on SYEI and ZUEI, the genotypes were classified as efficient and responsive (Virsa Arhar-1, GT-1, GT-101, SKNP 05-05, BDN-2, AAUT 2007-04, BSMR 853, T 15-15, DT 23, Pusa 9), efficient and non-responsive (ICPL 87119, PKV Trombay), inefficient and responsive (AKT 8811, Hisar Paras), and inefficient and non-responsive (AAUT 2007-10, JKM 7, Hisar Manak, C 11, Hisar HO2-60, GAUT 93-17). The efficient and responsive genotypes are the most useful as they yield well under low soil Zn conditions and also respond to Zn fertilizer application. The inefficient and responsive genotypes could be utilized for plant breeding programs by plant breeders for identification and utilization of responsive traits. Full article
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