The physiological response of photosynthesis to nitrogen deficiency.

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype While high-throughput genotyping is feasible at a low cost, high-throughput crop phenotyping methods and data analytical capacities need to be improved

/pdf/high-throughput-estimation-of-crop-traits-a-review-of-ground-4ej5it3p3y.pdf

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Remote Sensing of Grassland Production and Management—A Review

Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

Improving unmanned aerial vehicle remote sensing-based rice nitrogen nutrition index prediction with machine learning

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Effects of nitrogen application rate on grain yield and grain nitrogen concentration in two maize hybrids with contrasting nitrogen remobilization efficiency

The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg·ha−1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5–V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%–90%, 87%–89% and 83%–84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m2·m−2, AGB > 3 t·ha−1 or PNU > 80 kg·ha−1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5–V6 and V7–V8 than the V9–V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI–NDVI) and ratio vegetation index (R2 = 0.56–0.68) performed consistently better than the original VIs (R2 = 0.33–0.55) for estimating NNI. The N status diagnosis accuracy rate using RI–NDVI was 81% and 71% at V7–V8 and V9–V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms.

Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index

Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China

This paper aims to investigate the adaptation of maize root system architecture (RSA) in response to increasing planting densities. A three-year field study was conducted with three planting densities (40,000, 70,000, and 90,000 plants per ha, which are abbreviated as D40000, D70000 and D90000, respectively). The dynamic change of root morphological traits and the 3-dimensional RSA were quantified. The grain yield per ha increased with increasing plant density from D40000 to D70000, and then decreased at D90000. Compared to D70000, high planting density of D90000 did not changed the total root biomass per ha but increased shoot biomass per ha by 4 to 8% in two of the three experimental years. Grain yield per plant and plant NPK concentration decreased with increasing planting density. Total accumulation of P and K per ha also decreased at D90000 compared to D70000. Root to shoot ratio was reduced at high planting density beginning 50 days after emergence. Compared to the control (D70000), total root length (TRL) per plant was reduced by 18 to 30% at D90000 and increased by 43 to 56% at D40000, root biomass per plant was reduced by 23 to 34% at D90000 and increased by 66 to 75% at D40000. High plant density reduced the number of nodal roots, lateral root density (LRD) and the average lateral root (LR) length, but with less effect on the length of axial roots. The RSA is characteristic of “intra-row contraction and inter-row extension”. Vertically, root growth in top soil layer (0- to 36- cm) was enhanced under supra-optimal plant density, but had a negligible effect in deep soil layers (36- to 60- cm). To adapt to the limited photosynthesis capacity in the roots under high planting density, maize plants tend to reduce nodal root number and inhibit lateral root growth. They maintain nodal root length to explore a larger soil space, and adjust root growth in the intra-row and inter-row direction to avoid root-to-root competition.

https://link.springer.com/content/pdf/10.1007%2Fs11104-018-3720-8.pdf

Root growth and root system architecture of field-grown maize in response to high planting density

Vertical Distribution of Photosynthetic Nitrogen Use Efficiency and Its Response to Nitrogen in Field-Grown Maize

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