A Comparative Assessment of Measures of Leaf Nitrogen in Rice Using Two Leaf-Clip Meters

Feasibility study of Aesculus turbinata fruit shell-derived biochar for ammonia removal in wastewater and its subsequent use as nitrogen fertilizer

In the face of increasing nitrogen demand for crop cultivation driven by population growth, this study presents a sustainable solution to address both the heightened demand and the energy-intensive process of nitrogen removal from wastewater. Our approach involves the removal of nitrogen from wastewater and its subsequent return to the soil as a fertilizer. Using biochar derived from Aesculus turbinata fruit shells (ATFS), a by-product of post-medical use, we investigated the effect of pyrolysis temperature on the NH4-N adsorption capacity of ATFS biochar (ATFS-BC). Notably, the ATFS-BC pyrolyzed at 300 °C (ATFS-BC300) exhibited the highest NH4-N adsorption capacity of 15.61 mg/g. The superior performance of ATFS-BC300 was attributed to its higher number of oxygen functional groups and more negatively charged surface, which contributed to the enhanced NH4-N adsorption. The removal of NH4-N by ATFS-BC300 involved both physical diffusion and chemisorption, with NH4-N forming a robust multilayer adsorption on the biochar. Alkaline conditions favored NH4-N adsorption by ATFS-BC300; however, the presence of trivalent and divalent ions hindered this process. Rice plants were cultivated to assess the potential of NH4-N adsorbed ATFS-BC300 (NH4-ATFS-BC300) as a nitrogen fertilizer. Remarkably, medium doses of NH4-ATFS-BC300 (594.5 kg/ha) exhibited key agronomic traits similar to those of the commercial nitrogen fertilizer in rice seedlings. Furthermore, high doses of NH4-ATFS-BC300 demonstrated superior agronomic traits compared to the commercial fertilizer. This study establishes the viability of utilizing ATFS-BC300 as a dual-purpose solution for wastewater treatment and nitrogen fertilizer supply, presenting a promising avenue for addressing environmental challenges.

Improving Tree Seedling Quality Using Humates Combined with Bacteria to Address Decarbonization Challenges through Forest Restoration

Improving the quality of tree planting material for carbon sequestration through reforestation can help solve environmental problems, including the need to reduce the concentration of carbon dioxide in the atmosphere. The purpose of this study was to investigate the possibility of using humic substances in combination with rhizosphere microorganisms Pseudomonas protegens DA1.2 and Pseudomonas sp. 4CH as a means to stimulate the growth of seedlings of pine, poplar, large-leaved linden, red oak, horse chestnut, and rowan. Humic substances stimulated the growth of shoots and roots of pine, large-leaved linden, and horse chestnut seedlings. The effects of bacteria depended on both plant and bacteria species: Pseudomonas protegens DA1.2 showed a higher stimulatory effect than Pseudomonas sp. 4CH on pine and linden, and Pseudomonas sp. 4CH was more effective in the case of chestnut. An additive effect of humates and Pseudomonas protegens DA1.2 on the growth rate of pine and linden saplings was discovered. Poplar, red oak, and rowan seedlings were unresponsive to the treatments. The growth-stimulating effects of the treatments are discussed in connection with the changes in carbon, chlorophyll, and nitrogen contents in plants. The results show the need for further research in bacterial species capable of stimulating the growth of plant species that were unresponsive in the present experiments.

Effect of Mineral-Microbial Deodorizing Preparation on the Value of Poultry Manure as Soil Amendment

Poultry farming involves the production of poultry manures (PMs), which, if properly managed, are excellent organic soil amendments. Poultry farms generally do not have adequate arable land, and therefore, valuable fertilizer becomes a problematic waste. During the production and storage of PMs, odorous VOCs, NH₄, H₂S, and potent greenhouse gases such as CH₄, CO₂ are emitted. It influences the productivity of poultry and negatively affects the working conditions of working staff. In the present study, mineral-microbial deodorizing preparations (MMDP) based on perlite and bentonite as well as the following microorganism strains Lactobacillus plantarum, Leuconostoc mesenteroides, Bacillus megaterium, B. subtilis, and Pseudomonas fluorescens were added to the litter of turkey broilers (TB) and egg-laying hens (LH). PMs were compared with treatments without the addition of MMDP, and maize, sunflower, and rapeseed forage crops were tested. The influence on soil parameters such as pH, EC, HAC, SBC, CEC, BS, N_tot, C_tot, and plant yield and parameter of photosynthesis, i.e., SPAD index, was tested. Soil amending with manure resulted in an increase in pH and a decrease in HAC; in addition, an increase in EC, which was counteracted by the addition of MMDP, was noted. MMDP positively affected parameters such as SBC, CEC, and BS. It was shown that PMs, with the addition of MMDP, improved crops' yield in the first year of the study, whereas this effect was not seen for the after-crop plants (lupine). The main 'added value' related to the usage of MMDP in poultry production is the improvement in the properties of PMs, which mainly had a positive effect on soil indicators.

Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning

Nitrogen balance index (NBI) is an important indicator for scientific diagnostic and quantitative research on crop growth status. The quick and accurate assessment of NBI is necessary for farmers to make timely N management decisions. The objective of the study was to estimate winter wheat NBI based on canopy hyperspectral features between 400–1350 nm combined with machine learning (ML) methods in the individual and whole growth stages. In this study, 3 years of winter wheat plot experiments were conducted. Ground-level canopy hyperspectral reflectance and corresponding plant NBI values were measured during the jointing, booting, flowering and filling stages. Continuous removal spectra (CRS) and logarithmic transformation spectra (LOGS) were derived from the original canopy spectra. Sensitive bands and vegetation indices (VIs) highly correlated with NBI under different spectral transformations were selected as hyperspectral features to construct the NBI estimation models combined with ML algorithms. The study indicated that the spectral transformation significantly improved the correlation between the sensitive bands, VIs and the NBI. The correlation coefficient of the sensitive band in CRS in the booting stage increased by 27.87%, reaching −0.78. The leaf chlorophyll index (LCI) in LOGS had the highest correlation with NBI in the filling stage, reaching a correlation coefficient of −0.96. The NBI prediction accuracies based on the sensitive band combined with VIs were generally better than those based on the univariate hyperspectral feature, and the prediction accuracy of each growth stage was better than that of the whole growth stage. The random forest regression (RFR) method performed better than the support vector regression (SVR) and partial least squares regression (PLS) methods. The NBI estimation model based on the LOGS-RFR method in the filling stage could explain 95% of the NBI variability with relative prediction deviation (RPD) being 3.69. These results will provide a scientific basis for better nitrogen nutrition monitoring, diagnosis, and later for field management of winter wheat.

Co-Application of Biochar Compost and Inorganic Nitrogen Fertilizer Affects the Growth and Nitrogen Uptake by Lowland Rice in Northern Ghana

Inherent low soil fertility status limits productivity of rice in the lowland ecologies in Northern Ghana. Combining organic and inorganic nitrogen fertilizers could help to maintain the fertility of lowland soils for rice production. A screen house pot experiment was carried out to investigate the combined effect of biochar compost and inorganic nitrogen fertilizer on the nitrogen uptake and agronomic performance of rice plants grown on an eutric gleysol lowland soil. Inorganic nitrogen fertilizer alone and its combinations with different types of biochar compost (based on the proportions of biochar and compost) were used as treatment. A control (unamended soil) was also included. The incorporation of biochar compost and inorganic nitrogen fertilizer improved the growth parameters and yield components of rice plants. The combination of biochar compost and inorganic nitrogen fertilizer was also found to improve nitrogen uptake in rice plants. This practice could be the most likely viable option for alleviating lowland soil fertility issues and increasing rice productivity in Northern Ghana.

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

BACKGROUND AND AIMS

The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied.

METHODS

The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation).

RESULTS

In the 0-30 cm soil profile, the models achieved a R² of 0.85 (SOC%) and a R² of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R² of 0.86 (SOC%), and a R² of 0.79 (SOC Mg/ha).

CONCLUSIONS

The used Sentinel-2 variables (FVC, CWC, LCC/C_ab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11104-022-05506-1.

Mineral Materials as a Neutralizing Agent Used on Soil Contaminated with Copper

The aim of the investigation was to evaluate the response of plants, using black mustard (Brassica nigra L. Koch) as a model plant, to soil contamination with copper (0, 200, 400, 600 mg Cu kg⁻¹ of soil), and to determine the effectiveness of the Cu immobilization with mineral neutralizing materials, such as lime, clay and zeolite. The plant yield depended on soil contamination and mineral amendments. In the series without neutralizing materials, the level of 600 mg Cu kg⁻¹ reduced the yield and increased leaf greenness. Lime alleviated the toxicity of Cu in objects with 200 mg Cu kg⁻¹. Zeolite slightly mitigated the harmful effects of Cu at the level of 400 and 600 mg kg⁻¹. Zeolite lowered the SPAD index. In the chemical composition of plants, the content of Cu, K, Mg, Na and Ca in plants increased to 400 mg Cu kg⁻¹, while the content of P decreased to 600 mg Cu kg⁻¹. Among the materials, lime reduced the Cu accumulation in plants the most, followed by clay. Cu narrowed the majority of ratios and widened the Ca:P and K:Ca ratios in plants. The applied mineral materials, except lime, did not significantly affect the formation of these indicators.

The Function of Flavonoids in the Diurnal Rhythm under Rapidly Changing UV Conditions—A Model Study on Okra

Flavonoids are favored compounds in plant responses to UV exposure and act in UV absorption and antioxidant activity. Here, it was investigated, with okra as a model species, how fast plants can react to changing UV conditions and to what extent these reactions take place. Okra (Abelmoschus esculentus) plants were exposed to either full or nearly no UV radiation. The diurnal rhythm of the plants was driven by the UV radiation and showed up to a 50% increase of the flavonoid content (measured optically in the +UV plants). This was reflected only in the trends in UV-absorption and antioxidant activity of the extracts but not in the soluble flavonoid glycosides and hydroxycinnamic acid derivatives. In a second experiment, a transfer from a −UV to a +UV condition at 9:00 CDT showed the immediate start of the diurnal rhythm, while this did not occur if the transfer occurred later in the day; these plants only started a diurnal rhythm the following day. After an adaptation period of seven days, clear differences between the +UV and -UV plants could be found in all parameters, whereas plants transferred to the opposite UV condition settle between the +UV and -UV plants in all parameters. Broadly, it can be seen that the flavonoid contents and associated functions in the plant are subject to considerable changes within one day and within several days due to the UV conditions and that this can have a considerable impact on the quality of plant foods.

Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha⁻¹) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

Application of a Combined Transmittance/Fluorescence Leaf Clip Sensor for the Nondestructive Determination of Nitrogen Status in White Cabbage Plants

The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha⁻¹ in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R² = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.

Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.