Dataset Information

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

ABSTRACT: Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R (2)~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

SUBMITTER: Vergara-Diaz O

PROVIDER: S-EPMC4870241 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

Vergara-Díaz Omar O Zaman-Allah Mainassara A MA Masuka Benhildah B Hornero Alberto A Zarco-Tejada Pablo P Prasanna Boddupalli M BM Cairns Jill E JE Araus José L JL

Frontiers in plant science 20160518

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They we ...[more]

PMID: 27242867

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Project description:Controlled release urea (CRU) has been widely adopted to increase nitrogen (N) use efficiency and maize production, but the impacts can range widely depending on water availability in the soil. In an experiment using Zhengdan 958 (a popular summer maize hybrid), three levels of water treatments (adequate water condition [W3], which maintained soil moisture at about 75% ± 5% of the soil's field capacity; mild water stress [W2], which maintained moisture content at 55% ± 5% of field capacity; and severe water stress [W1], which had a moisture content of 35% ± 5% of field capacity) and four levels of controlled release urea fertilizer (N0, N1, N2 and N3 were 0, 105, 210 and 315 kg N ha-1, respectively) were compared in a rainout shelter system with soil. The results revealed that CRU had significant effects on maize yields and N use efficiencies under different water conditions. The mean yields increased with increasing water levels and showed significant differences. Under W1, the accumulation of dry matter and N were limited, and N internal efficiency (NIE) and the apparent recovery efficiency of applied N (REN) decreased with N increases; yields of N1, N2, and N3 were similar. Under W2, the dry matter and N accumulation, as well as the yield, showed an increasing trend with an increase in N application, and the NIE and REN of N3 showed no difference from N2. Under W3, yields of N2 and N3 were similar and they were significantly higher than that of N1, but the agronomic N use efficiency (ANUE), REN, and the physiological NUE (PNUE) of N2 were 54.2, 34.9, and 14.4% higher, respectively, than those of N3. N application beyond the optimal N rate did not consistently increase maize yield, and caused a decrease in N use efficiencies. Highest overall dry matter, N accumulation, and yields were observed with N3 under W2, and those showed no differences with N2 and N3 under W3. Under this experimental condition, the CRU of 210 kg ha-1 was optimized when soil moisture content was 75% ± 5% of field capacity, but an N rate of 315 kg ha-1 was superior when soil moisture content during the entire growing season was maintained at 55% ± 5% of field capacity.

Project description:The sustainable use of grasslands in intensive farming systems aims to optimize nitrogen (N) inputs to increase crop yields and decrease harmful losses to the environment at the same time. To achieve this, simple optical sensors may provide a non-destructive, time- and cost-effective tool for estimating plant biomass in the field, considering spatial and temporal variability. However, the plant growth and related N uptake is affected by the available N in the soil, and therefore, N mineralization and N losses. These soil N dynamics and N losses are affected by the N input and environmental conditions, and cannot easily be determined non-destructively. Therefore, the question arises: whether a relationship can be depicted between N fertilizer levels, plant biomass and N dynamics as indicated by nitrous oxide (N₂O) losses and inorganic N levels. We conducted a standardized greenhouse experiment to explore the potential of spectral measurements for analyzing yield response, N mineralization and N₂O emissions in a permanent grassland. Ryegrass was subjected to four mineral fertilizer input levels over 100 days (four harvests) under controlled environmental conditions. The soil temperature and moisture content were automatically monitored, and the emission rates of N₂O and carbon dioxide (CO₂) were detected frequently. Spectral measurements of the swards were performed directly before harvesting. The normalized difference vegetation index (NDVI) and simple ratio (SR) were moderately correlated with an increasing biomass as affected by fertilization level. Furthermore, we found a non-linear response of increasing N₂O emissions to elevated fertilizer levels. Moreover, inorganic N and extractable organic N levels at the end of the experiment tended to increase with the increasing N fertilizer addition. However, microbial biomass C and CO₂ efflux showed no significant differences among fertilizer treatments, reflecting no substantial changes in the soil biological pool size and the extent of the C mineralization. Neither the NDVI nor SR, nor the plant biomass, were related to cumulative N₂O emissions or inorganic N at harvesting. Our results verify the usefulness of optical sensors for biomass detection, and show the difficulty in linking spectral measurements of plant traits to N processes in the soil, despite that the latter affects the former.

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Dataset Information

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

Publications

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization.

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