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Precision Nutrient Management for Climate-Smart Agriculture

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A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 26916

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Dr. Qiang Cao

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Guest Editor

National Engineering and Technology Center for Information Agriculture, Department of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: precision nitrogen/water management; soil management zone; remote-sensing-based nitrogen status diagnosis; precision crop management; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Syed Tahir Ata-Ul-Karim

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Guest Editor

Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
Interests: nitrogen diagnosis; soil health; plant and soil nutrient management; drought; nitrogen-water interactions; sustainable cropping system; food security; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Farmers apply fertilizers to improve soil fertility for supplying nutrients to their crops. Synthetic fertilizers have greatly boosted crop production, allowing farmers to increase yield per unit area. Nevertheless, this uptick in fertilizer use has led to global warming through greenhouse gas emissions. To address the challenges of global food security, environmental pollution, and climate change, it is imperative to develop precision nutrient management (PNM) strategies, which can sustainably increase the productivity and resilience of cropping systems, while reducing greenhouse gas emissions. PNM is a promising approach for synchronizing soil nutrient supply with crop nutrient demand both in terms of time and rate of nutrient application. However, successful PNM for climate-smart agriculture requires the development of rapid, non-destructive, and economically viable strategies through in-season crop nutrient status monitoring and diagnosis.

This Special Issue on “Precision Nutrient Management for Climate-Smart Agriculture” will mainly focus on topics related to the use of a range of sensor technologies and crop growth models together with classical approaches to extract value-added information to be used for strategic decision making for in-season operational activities. We invite you to submit reviews, case studies, or research articles focusing on scientific methods, technological tools, and innovative statistical analyses, to capture the current advancements and foster an open discussion on the future perspectives on PNM for climate-smart agriculture. Papers are solicited on all areas directly related to these topics, including but not limited to:

Remote sensing-based nutrient status monitoring and diagnosis;
Agricultural decision support systems;
Crop growth modeling-based nutrient recommendation;
Spatial–temporal soil nutrient/crop nutrient variability;
Soil nutrient management zone delineation;
Variable-rate nutrient input technologies;
Integrated crop nutrient management;
Artificial intelligence and machine learning techniques.

We hope you find the topic of this Special Issue interesting, and we look forward to your research contribution.

Dr. Qiang Cao
Dr. Syed Tahir Ata-Ul-Karim
Guest Editors

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Keywords

nutrient monitoring
nutrient diagnosis
decision-support systems
crop growth modeling
remote sensing
spatial-temporal variability
management zone
variable rate
nutrient recommendation
nutrient stress
site-specific management
in-season management
integrated nutrient management
artificial intelligence
machine learning
meta-analysis

Published Papers (10 papers)

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Research

16 pages, 4085 KiB

Open AccessArticle

Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System

by Panuwat Pengphorm, Sukrit Thongrom, Chalongrat Daengngam, Saowapa Duangpan, Tajamul Hussain and Pawita Boonrat

Plants 2024, 13(2), 259; https://doi.org/10.3390/plants13020259 - 16 Jan 2024

Viewed by 878

Abstract

Hyperspectral imaging (HSI) is a promising tool in chlorophyll quantification, providing a non-invasive method to collect important information for effective crop management. HSI contributes to food security solutions by optimising crop yields. In this study, we presented a custom HSI system specifically designed to provide a quantitative analysis of leaf chlorophyll content (LCC). To ensure precise estimation, significant wavelengths were identified using optimal-band analysis. Our research was centred on two sets of 120 leaf samples sourced from Thailand’s unique Chaew Khing rice variant. The samples were subjected to (i) an analytical LCC assessment and (ii) HSI imaging for spectral reflectance data capture. A linear regression comparison of these datasets revealed that the green (575 ± 2 nm) and near-infrared (788 ± 2 nm) bands were the most outstanding performers. Notably, the green normalised difference vegetation index (GNDVI) was the most reliable during cross-validation (

R^{2} = 0.78

and RMSE = 2.4 µg∙cm⁻²), outperforming other examined vegetable indices (VIs), such as the simple ratio (RED/GREEN) and the chlorophyll index. The potential development of a streamlined sensor dependent only on these two wavelengths is a significant outcome of identifying these two optimal bands. This innovation can be seamlessly integrated into farming landscapes or attached to UAVs, allowing real-time monitoring and rapid, targeted N management interventions. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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14 pages, 2380 KiB

Open AccessArticle

Precision Horticulture: Application of Optical Sensor Technology for Nitrogen Monitoring Status in Cocoplum, a Native Landscaping Plant

by Bárbara Nogueira Souza Costa, Daniel A. Tucker and Amir Ali Khoddamzadeh

Plants 2023, 12(4), 760; https://doi.org/10.3390/plants12040760 - 08 Feb 2023

Cited by 1 | Viewed by 1918

Abstract

Cocoplum (Chrysobalanus icaco) is an ecologically significant native species to Southern Florida. Application of precision agriculture technologies such as optical sensors reduces the cost of over-fertilization and nutrient runoff. The aim of this work was to establish a base line sensor value for fertilizer treatment in cocoplum by monitoring chlorophyll content using the Soil Plant Analytical Development (SPAD), atLEAF, and Normalized Difference Vegetation Index (NDVI) sensors. Initial slow-released fertilizer treatment 8N-3P-9K was used at 15 g (control), 15 g (supplemented with +15 g × 2; T1), 15 g (+15 g; T2), 30 g (+15 g × 2; T3), 30 g (+15 g; T4), and 45 g (+15 g × 2; T5). Evaluations were conducted at 0 (base reading), 30, 60, 90, 120, 150, and 180 days after treatment. Growth parameters, optical non-destructive chlorophyll meters, leaf and soil total nitrogen and total carbon, and total nitrogen of leachate were analyzed. The results demonstrated that the treatment using 30 g slow-released fertilizer (8N-3P-9K) supplemented twice with 15 g in November and March after the first fertilization in October provided the least contamination through runoff while still providing adequate nutrients for plant growth compared to higher fertilizer concentrations. These results demonstrate that the highest treatment of nitrogen can cause considerable losses of N, causing extra costs to producers and environmental damage due to the flow of nutrients. Thus, techniques that help in N monitoring to avoid the excessive use of nitrogen fertilization are necessary. This study can serve as a basis for future research and for nurseries and farms, since it demonstrated from the monitoring of the chlorophyll content by optical sensors and by foliar and substrate analysis that lower treatments of nitrogen fertilization are sufficient to provide nutrients suitable for the growth of cocoplum plants. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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15 pages, 3381 KiB

Open AccessArticle

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition

by Anushika L. De Silva, Stephen J. Trueman, Wiebke Kämper, Helen M. Wallace, Joel Nichols and Shahla Hosseini Bai

Plants 2023, 12(3), 558; https://doi.org/10.3390/plants12030558 - 26 Jan 2023

Cited by 6 | Viewed by 1529

Abstract

Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400–1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R²_P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R²_P = 0.55, RPD = 1.52; P: R²_P = 0.77, RPD = 2.11; K: R²_P = 0.77, RPD = 2.12; Ca: R²_P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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18 pages, 4158 KiB

Open AccessArticle

Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms

by Umut Hasan, Kai Jia, Li Wang, Chongyang Wang, Ziqi Shen, Wenjie Yu, Yishan Sun, Hao Jiang, Zhicong Zhang, Jinfeng Guo, Jingzhe Wang and Dan Li

Plants 2023, 12(3), 501; https://doi.org/10.3390/plants12030501 - 21 Jan 2023

Cited by 7 | Viewed by 1634

Abstract

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04

μ g \cdot {cm}^{- 2}

). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R² = 0.88), mean absolute error (MAE = 3.40

μ g \cdot {cm}^{- 2}

), root mean square error (RMSE = 4.23

μ g \cdot {cm}^{- 2}

), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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13 pages, 2793 KiB

Open AccessArticle

Spectral Discrimination of Macronutrient Deficiencies in Greenhouse Grown Flue-Cured Tobacco

by Josh Henry, Patrick Veazie, Marschall Furman, Matthew Vann and Brian Whipker

Plants 2023, 12(2), 280; https://doi.org/10.3390/plants12020280 - 07 Jan 2023

Cited by 4 | Viewed by 2816

Abstract

Remote sensing of nutrient disorders has become more common in recent years. Most research has considered one or two nutrient disorders and few studies have sought to distinguish among multiple macronutrient deficiencies. This study was conducted to provide a baseline spectral characterization of macronutrient deficiencies in flue-cured tobacco (Nicotiana tabacum L.). Reflectance measurements were obtained from greenhouse-grown nutrient-deficient plants at several stages of development. Feature selection methods including information entropy and first and second derivatives were used to identify wavelengths useful for discriminating among these deficiencies. Detected variability was primarily within wavelengths in the visible spectrum, while near-infrared and shortwave-infrared radiation contributed little to the observed variability. Principal component analysis was used to reduce data dimensionality and the selected components were used to develop linear discriminant analysis models to classify the symptoms. Classification models for young, intermediate, and mature plants had overall accuracies of 92%, 82%, and 75%, respectively, when using 10 principal components. Nitrogen, sulfur, and magnesium deficiencies exhibited greater classification accuracies, while phosphorus and potassium deficiencies demonstrated poor or inconsistent results. This study demonstrates that spectral analysis of flue-cured tobacco is a promising methodology to improve current scouting methods. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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19 pages, 3475 KiB

Open AccessArticle

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

by Yifan Yuan, Bo Shi, Russell Yost, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao and Qiang Cao

Plants 2022, 11(19), 2611; https://doi.org/10.3390/plants11192611 - 04 Oct 2022

Cited by 7 | Viewed by 2864

Abstract

Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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21 pages, 2428 KiB

Open AccessArticle

Integrated Nutrient Management Improves the Growth and Yield of Rice and Greengram in a Rice—Greengram Cropping System under the Coastal Plain Agro-Climatic Condition

by Satyabrata Mangaraj, Rabindra Kumar Paikaray, Sagar Maitra, Shriram Ratan Pradhan, Lalita Mohan Garnayak, Manoranjan Satapathy, Barsita Swain, Satyananda Jena, Bijayalaxmi Nayak, Tanmoy Shankar, Mohammed Alorabi, Ahmed Gaber and Akbar Hossain

Plants 2022, 11(1), 142; https://doi.org/10.3390/plants11010142 - 05 Jan 2022

Cited by 9 | Viewed by 3859

Abstract

Continuous mono-cropping of rice has resulted in decline or stagnation of yield output due to the occurrence of multiple nutrient deficiencies and worsening of soil physicochemical properties accompanying increased pressure of insect pests and diseases. The basic concept of integrated nutrient management (INM) is maintenance or adjustment of soil fertility and supply of plant nutrients to an optimum level for sustaining the desired crop productivity through optimisation of benefits from all possible sources of plant nutrients in an integrated way. Augmenting a rice-based cropping system with pulses is a prevalent and indigenous cropping system under rainfed conditions. Considering the above facts, experiments were conducted to evaluate the impacts of integrated nutrient management on productivity of aromatic rice–greengram cropping system and nutrient balance of the post-harvest soil for agricultural sustainability under rainfed conditions in two consecutive years (2017–2018 and 2018–2019) with six main plots and three subplots. The experimental findings revealed that the treatment comprised of 50% recommended dose of fertiliser (RDF) through chemicals + 50% recommended dose of nitrogen (RDN) through farmyard manure (FYM) increased the plant height, tillers, dry matter accumulation, leaf area and leaf area duration, and yield parameters in short grain aromatic rice. Similarly, preceding application of 50% RDF + 50% RDN through FYM to rice and further application 75% RDF + Rhizobium+ phosphate solubilizing bacteria (PSB) to greengram increased the growth characteristics and yield parameters—such as pods/plant, seeds/pod, grain yield, stover yield, and harvest index—in greengram. It was concluded that the treatment consisting of 50% RDF (chemical fertiliser) + 50% RDN (FYM) to rice and 75% RDF + Rhizobium + PSB to greengram increased the productivity of the rice–greengram cropping system. Furthermore, the adoption of INM has positively impacted post-harvest soil nutrient balance. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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20 pages, 3761 KiB

Open AccessArticle

Exploring the Impacts of Genotype-Management-Environment Interactions on Wheat Productivity, Water Use Efficiency, and Nitrogen Use Efficiency under Rainfed Conditions

by Raheel Osman, Muhammad Naveed Tahir, Syed Tahir Ata-Ul-Karim, Wajid Ishaque and Ming Xu

Plants 2021, 10(11), 2310; https://doi.org/10.3390/plants10112310 - 27 Oct 2021

Cited by 7 | Viewed by 2741

Abstract

Wheat production under rainfed conditions is restrained by water scarcity, elevated temperatures, and lower nutrient uptake due to possible drought. The complex genotype, management, and environment (G × M × E) interactions can obstruct the selection of suitable high yielding wheat cultivars and nitrogen (N) management practices prerequisite to ensure food security and environmental sustainability in arid regions. The agronomic traits, water use efficiency (WUE), and N use efficiencies were evaluated under favorable and unfavorable weather conditions to explore the impacts of G × M × E on wheat growth and productivity. The multi-N rate (0, 70, 140, 210, and 280 kg N ha⁻¹) field experiment was conducted under two weather conditions (favorable and unfavorable) using three wheat cultivars (AUR-809, CHK-50, and FSD-2008) in the Pothowar region of Pakistan. The experiments were laid out in randomized complete block design (RCBD), with split plot arrangements having cultivars in the main plot and N levels in the subplot. The results revealed a significant decrease in aboveground biomass, grain yield, crop N-uptake, WUE, and N use efficiency (NUE) by 15%, 22%, 21%, 18%, and 8%, respectively in the unfavorable growing season (2014–2015) as compared to favorable growing season (2013–2014) as a consequence of less rainfall and heat stress during the vegetative and reproductive growth phases, respectively. FSD-2008 showed a significantly higher aboveground biomass, grain yield, crop N-uptake, WUE, and NUE as compared to other wheat cultivars in both years. Besides, N₁₄₀ appeared as the most suitable dose for wheat cultivars during the favorable growing season. However, any further increase in N application rates beyond N₁₄₀ showed a non-significant effect on yield and yield components. Conversely, the wheat yield increased significantly up to 74% from N₀ to N₇₀ during the unfavorable growing season, and there was no substantial difference between N₇₀–N₂₈₀. The findings provide opportunities for maximizing yield while avoiding excessive N loss by selecting suitable cultivars and N application rates for rainfed areas of Pothowar Plateau by using meteorological forecasting, amount of summer rainfall, and initial soil moisture content. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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10 pages, 275 KiB

Open AccessArticle

Application of K and Zn Influences the Mineral Accumulation More in Hybrid Than Inbred Maize Cultivars

by Hafiz Muhammad Ali Raza, Muhammad Amjad Bashir, Abdur Rehim, Qurat-Ul-Ain Raza, Graeme P. Berlyn, Shafeeq Ur Rahman and Yucong Geng

Plants 2021, 10(10), 2206; https://doi.org/10.3390/plants10102206 - 17 Oct 2021

Cited by 4 | Viewed by 2141

Abstract

Maize (Zea mays L.) is an important crop used for feeding humans and cattle globally. Deficiency of potassium (K) and zinc (Zn) adversely impacts the maize crop productivity and quality. However, the application of these nutrients shows variant responses in different maize cultivars. To understand this perspective, the current study aimed at investigating K and Zn’s optimal concentration in different hybrid and inbred maize cultivars. The treatments were based on three zinc levels (0, 6, and 12 mg Zn kg⁻¹) and K levels (0, 30, and 60 mg kg⁻¹), and their respective combinations. The experiment results showed that combined fertilization approaches of Zn and K (Zn12K60) improved the plant biometric, and physiological attributes of maize crop. The results revealed a significant increase in plant height (45%), fresh weight (70%), and dry weight (45%). Similarly, physiological attributes significantly improved the relative water content (76.4%), membrane stability index (77.9%), chlorophyll contents (170%), and photosynthetic rate (130%) in both inbred and hybrid genotypes. Furthermore, Zn and K (Zn12K60) increased transpiration rate (E), stomatal conductance (Ci), and internal CO₂. In conclusion, maize hybrids (Neelam and DK-6142) were observed best compared with inbred (Afghoi and P-1543) cultivars with the combined application of Zn and K (Zn12K60). Thus, these inbred varieties should be preferred for fodder requirement with optimum fertilizer (Zn12K60) application in Zn deficient soils. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

15 pages, 686 KiB

Open AccessArticle

Contributions of Climate and Soil Properties to Wheat and Maize Yield Based on Long-Term Fertilization Experiments

by Shengbao Wei, Anchun Peng, Xiaomin Huang, Aixing Deng, Changqing Chen and Weijian Zhang

Plants 2021, 10(10), 2002; https://doi.org/10.3390/plants10102002 - 24 Sep 2021

Cited by 4 | Viewed by 1869

Abstract

Identifying the contributions of climate factors and soil fertility to crop yield is significant for the assessment of climate change impacts on crop production. Three 20-year field experiments were conducted in major Chinese wheat-maize cropping areas. Over the 20-year period, crop yield and soil properties showed significantly dissimilar variation trends under similar climate changes at each experimental site. The correlation between climatic factors and crop yield varied greatly among the fertilization regimes and experimental sites. Across all the fertilization regimes and the experimental sites, the average contribution rates of soil properties to wheat and maize yield were 45.7% and 53.2%, respectively, without considering climate factors, and 40.4% and 36.6%, respectively, when considering climate factors. The contributions of soil properties to wheat and maize yield variation when considering climate factors were significantly lower than those without considering climate factors. Across all experimental sites and all fertilization regimes, the mean contribution rates of climate factors to wheat and maize yield were 29.5% and 33.0%, respectively. The contribution rates of the interaction of climate and soil to wheat and maize yield were 3.7% and −0.9%, respectively. Under balanced fertilization treatments (NPK and NPKM), the change in the contribution rate of soil properties to wheat or maize yield was not obvious, and the average contribution rates of the interaction of climate and soil to wheat and maize yield were positive, at 14.8% and 9.5%, respectively. In contrast, under unbalanced fertilization treatments (CK and N), the contribution rates of soil properties to wheat or maize yield decreased, and the average contribution rates of the interaction of climate and soil were negative, at −7.4% and −11.2%, respectively. The above results indicate that climate and soil synergistically affected crop yields and that, with the optimization of the fertilization regime, positive interactions gradually emerged. Full article

(This article belongs to the Special Issue Precision Nutrient Management for Climate-Smart Agriculture)

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