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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 25621

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Dr. Qi Jing

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Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Interests: modelling plant–soil systems for annual and perennial crops; climate change effect on agricultural systems; crop eco-physiology; crop husbandry

Special Issue Information

Dear Colleagues,

To cope with the projected increase in food demand and concerns around the environment and climate change, it is becoming increasingly important to maintain cropping systems which are sustainable and to improve crop yield and quality with efficient resource use. Cropping systems consist of numerous complex and interacting biological processes that can be influenced by human management. Quantification of these complex processes helps to increase our understanding of crop growth and facilitates the design of new management strategies aimed at high yield and quality. Crop models are based on existing insights in the underlying chemistry, physics, physiology, and ecology in the cropping system. Information on weather, soil, and management practice is combined and processed to predict crop performance. Crop models increase insight into relevant processes, allow a study of the effects of crop management and exploration of possible consequences of management modifications. Modelling tools have been linked down to scales of functional genomics and up to regional scales of natural resource management. Recent studies have shown that the existing crop models performed discrepantly in simulating crop performances, while crop quality is less simulated. It is necessary to upgrade crop models with newly developed knowledge to precisely project crop performances and explore mitigations under warming climate changes. This Special Issue aims at updating and refreshing the developments, improvements, and applications in modelling cropping system, confronting the emerging challenges in crop production industry. We invite you to share your achievements on but not limited to the following topics: (1) quantifying crop eco-physiological processes; (2) crop model development, improvement, comparison, and adaptation; (3) modelling genotype × environment × management interactions in cropping system; (4) crop model integration with spatial data and other information technology; (5) crop model application to agricultural resource management and climate change; (6) modelling mixed cropping, crop rotations, perennial crops, etc.

Dr. Qi Jing
Guest Editor

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Keywords

Cropping system
Yield
Quality
G×E×M
Management
Climate change
Simulation
Experimentation
Systems approach

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

3 pages, 198 KiB

Open AccessEditorial

Advances in Modelling Cropping Systems to Improve Yield and Quality

by Qi Jing

Agronomy 2023, 13(2), 414; https://doi.org/10.3390/agronomy13020414 - 31 Jan 2023

Viewed by 909

Abstract

Additional food and bio-products are expected to be required to feed the growing world population under the changing climate [...] Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

Research

Jump to: Editorial, Review

13 pages, 2444 KiB

Open AccessArticle

Long-Term Corn–Soybean Rotation and Soil Fertilization: Impacts on Yield and Agronomic Traits

by Ming Yuan, Yingdong Bi, Dongwei Han, Ling Wang, Lianxia Wang, Chao Fan, Di Zhang, Zhen Wang, Wenwei Liang, Zhijia Zhu, Yuehui Liu, Wei Li, Haoyue Sun, Miao Liu, Jianxin Liu, Junqiang Wang, Bo Ma, Shufeng Di, Guang Yang and Yongcai Lai

Agronomy 2022, 12(10), 2554; https://doi.org/10.3390/agronomy12102554 - 18 Oct 2022

Cited by 6 | Viewed by 2165

Abstract

Although crop rotations have been widely shown as an effective approach for improving yield or soil quality in the long term, the relationship between crop rotations and quality traits of crop or biochar-based fertilization is still unclear. To address this, we conducted a long-term field experiment in the Heilongjiang province of China to investigate the effects of crop rotation and biochar-based fertilizer application on the crop yield, soil parameters, crop quality, and agronomic traits in 2014–2020. The effects of rotations on crop production and soil properties were evaluated with the average yield variability during the 7 years of this study. Our results showed that the diversified corn and soybean rotations had a significant positive effect on average crop yield compared with their monocultures. The corn yield was enhanced by 0.6 Mg ha⁻¹ (5.4%) in the corn–soybean–corn (CSC) crop sequence compared with monoculture corn. Similarly, soybean yield was enhanced by 0.21 Mg ha⁻¹ (9.7%) in the soybean–corn–corn (SCC) crop sequence compared with monoculture soybean. However, a negative effect of crop rotations was detected on the protein content of soybean compared with the monoculture soybean, while a positive effect was detected on oil content. Additionally, no differences were detected in crop yield between biochar-based fertilization and mineral fertilization treatments, but a significant positive effect of biochar-based fertilization was observed for any crop on both protein and oil content. A significant effect of crop rotation was found on the percentage of total soil N (TN), available soil N (AN), and available soil K (AK) content. The SSS crop sequence treatment illustrated the highest TN values at 0.18%. The CCC crop sequence treatment increased AN and AK content by 9.1% and 7.8%, respectively, compared with SSS (p < 0.05). We conclude that crop rotations increase crop yield and biochar-based fertilizer application, improving crop quality traits in the long term. Thus, the addition of biochar-based fertilizer could efficiently enhance the yield and quality of crop in the rotation cropping system. The findings of this study may provide useful information for designing sustainable cropping systems based on rotations. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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18 pages, 2637 KiB

Open AccessArticle

Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model

by Gul Roz Khan, Hiba M. Alkharabsheh, Mohammad Akmal, Arwa Abdulkreem AL-Huqail, Nawab Ali, Bushra A. Alhammad, Muhammad Mehran Anjum, Rabia Goher, Fazli Wahid, Mahmoud F. Seleiman and Gerrit Hoogenboom

Agronomy 2022, 12(8), 1766; https://doi.org/10.3390/agronomy12081766 - 27 Jul 2022

Cited by 12 | Viewed by 2358

Abstract

Crop simulation models can be effective tools to assist with optimization of resources for a particular agroecological zone. The goal of this study was to determine the influence of N rates with different timing of application to wheat crop using prominent varieties using the CSM-CERES-Wheat model of the decision support system for agrotechnology transfer (DSSAT). Data were focused for yield traits, i.e., number of tillers, number of grains, grain weight, grain yield, biomass, and grain N content. To test the applicability of the CSM-CERES-Wheat version 4.7.5 model for agroclimatic conditions of Peshawar, Pakistan, experimental data from two years of experiments (2016–17 and 2017–18) were used for model calibration and evaluation. The simulation results of two years agreed well with field measured data for three commercial varieties. The model efficiency (R²) for wheat varieties was above 0.94 for variables tiller number per unit area (m⁻²), number of grains (m⁻²) and number of grains (spike⁻¹), 1000 grain weight (mg), biomass weight (kg ha⁻¹), grain yield (kg ha⁻¹), and harvest N content (kg ha⁻¹). Statistics of cultivars indicated that yield traits, yield, and N can be simulated efficiently for agroecological conditions of Peshawar. Moreover, different N rates and application timings suggested that the application of 140 kg N ha⁻¹ with triple splits timings, i.e., 25% at the sowing, 50% at the tillering, and 25% at the booting stage of the crop, resulted in the maximum yield and N recovery for different commercial wheat varieties. Simulated N losses, according to the model, were highly determined by leaching for experimental conditions where a single N application of 100% or existing double splits timing was applied. The study concluded that 140 kg N ha⁻¹ is most appropriate for wheat crop grown on clay loam soils under a flood irrigation system. However, the N fertilizer has to be given in triple splits of a 1:2:1 ratio at the sowing, tillering, and booting stages of the crop growth. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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18 pages, 3394 KiB

Open AccessEditor’s ChoiceArticle

Simulation Parameter Calibration and Test of Typical Pear Varieties Based on Discrete Element Method

by Guiju Fan, Siyu Wang, Wenjie Shi, Zhenfeng Gong and Ming Gao

Agronomy 2022, 12(7), 1720; https://doi.org/10.3390/agronomy12071720 - 21 Jul 2022

Cited by 9 | Viewed by 1682

Abstract

To improve the accuracy of discrete element simulation parameters for the mechanized picking and collection of pears, the study calibrated the simulation parameters of pears by the method of combining a physical experiment and simulation. Based on the intrinsic parameters of four kinds of pears (Snow pears, Crisp pears, Huangguan pears and Qiuyue pears), their simulation models were constructed by the Hertz-Mindlin with a bonding model. The simulation parameters between pears and the contact material (PVC, EVA foam material) were calibrated by the methods of free fall collision, inclined sliding and rolling, respectively. The experiments of pear accumulation angle were carried out. It was obtained to process the image of pears with Matrix Laboratory software. In order to determine the optimal value interval of influencing factors of the pear accumulation angle, the steepest ascent experiment was carried out. Considering the coefficient of collision recovery, the coefficient of static friction and the coefficient of rolling friction between pears, five-level simulation experiments of the pear accumulation angle were designed for each factor by the method of orthogonal rotation combination. The regression model of the error between the measured value and the simulated value of the pear accumulation angle was established, and the influence of three factors on the pear accumulation angle was analyzed. The results showed that the static friction coefficient and rolling friction coefficient between pears have significant effects on the pear accumulation angle. Therefore, the optimal model of minimum error was constructed according to constraint condition, and the coefficient of collision recovery, coefficient of static friction and coefficient of rolling friction between pears were obtained. The accumulation angle verification experiments were carried out by the method of bottomless barrel lifting. The results showed that the relative error between the simulated and measured accumulation angle of four kinds of pears were 1.42%, 1.68%, 2.19% and 1.83%, respectively, which indicated that the calibrated simulation parameters were reliable. The research can provide a basis for the design and parameters optimization of harvesting machinery of pears. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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17 pages, 4053 KiB

Open AccessArticle

The Quantitative Features Analysis of the Nonlinear Model of Crop Production by Hybrid Soft Computing Paradigm

by Muhammad Sulaiman, Muhammad Umar, Kamsing Nonlaopon and Fahad Sameer Alshammari

Agronomy 2022, 12(4), 799; https://doi.org/10.3390/agronomy12040799 - 26 Mar 2022

Cited by 2 | Viewed by 1696

Abstract

In this study, we provide a discretized system of a continuous dynamical model for enhancing crop production in the presence of insecticides and insects. Crops are assumed to grow logistically but are limited by an insect population that entirely depends on agriculture. To protect crops from insects, farmers use insecticides, and their overmuch use is harmful to human health. We assumed that external efforts are proportional to the gap between actual production and carrying capacity to increase the field’s development potential. We use the Levenberg–Marquardt algorithm (LMA) based on artificial neural networks (NNs) to investigate the approximate solutions for different insecticide spraying rates. “NDSolve” tool in Mathematica generated a data collection for supervised LMA. The NN-LMA approximation’s value is achieved by the training, validation, and testing reference data sets. Regression, error histograms, and complexity analysis help to validate the technique’s robustness and accuracy. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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13 pages, 3291 KiB

Open AccessArticle

Effect of Different Nitrogen Supply on Maize Emergence Dynamics, Evaluation of Yield Parameters of Different Hybrids in Long-Term Field Experiments

by Atala Szabó, Adrienn Széles, Árpád Illés, Csaba Bojtor, Seyed Mohammad Nasir Mousavi, László Radócz and János Nagy

Agronomy 2022, 12(2), 284; https://doi.org/10.3390/agronomy12020284 - 22 Jan 2022

Cited by 7 | Viewed by 2724

Abstract

This paper aims to examine the effect of various nitrogen (N) supply treatments on the date of emergence of maize hybrids classified in different age groups. The study site was at the University of Debrecen’s Látókép Experiment Station in Hungary. The date of emergence of the tested maize hybrids was monitored under control (0 kg N ha⁻¹), 120 kg ha⁻¹ N + PK, and 300 kg ha⁻¹ N + PK nutrient levels in a long-term field experiment. In 2020, maize hybrids (H1 = FAO 490; H2: FAO 420–440; H3 = FAO 420; H4 = 490; H5 = 320–340; H6 = FAO 350–370) growing under natural precipitation supply conditions without irrigation were included in the study. During the days of emergence, different moisture, protein, oil, starch, and yield production levels were observed, according to the variance analysis. In diverse maize hybrids, increasing or decreasing fertilizer treatment resulted in diverse productivity metrics. Regression analysis revealed that the day of emergence had a greater impact on protein, moisture, starch, and oil content than N fertilizer; however, yield production was influenced by N fertilization, rather than day of emergence. Regarding productivity parameters, this study suggests that H1 has the best productivity until the fourth day of emergence. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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15 pages, 1280 KiB

Open AccessEditor’s ChoiceArticle

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

by Dragana Rajković, Ana Marjanović Jeromela, Lato Pezo, Biljana Lončar, Federica Zanetti, Andrea Monti and Ankica Kondić Špika

Agronomy 2022, 12(1), 58; https://doi.org/10.3390/agronomy12010058 - 27 Dec 2021

Cited by 31 | Viewed by 3597

Abstract

As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r² values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively). Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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19 pages, 1461 KiB

Open AccessArticle

Using DNDC and WHCNS_Veg to Optimize Management Strategies for Improving Potato Yield and Nitrogen Use Efficiency in Northwest China

by Lingling Jiang, Wentian He, Rong Jiang, Jun Zhang, Yu Duan and Ping He

Agronomy 2021, 11(9), 1858; https://doi.org/10.3390/agronomy11091858 - 16 Sep 2021

Cited by 5 | Viewed by 2001

Abstract

Excessive nitrogen (N) application rate led to low N use efficiency and environmental risks in a potato (Solanum tuberosum L.) production system in northwest China. Process-based models are effective tools in agroecosystems that can be used to optimize integrated management practices for improving potato yield and N use efficiency. The objectives of this study were (1) to calibrate and evaluate the DeNitrification-DeComposition (DNDC) and soil Water Heat Carbon Nitrogen Simulator of Vegetable (WHCNS_Veg) models using the measurements of potato yield, above-ground biomass, N uptake, soil moisture and temperature, and soil inorganic N based on a field experiment in northwest China (2017–2020) and (2) to explore optimal management practices for improving yield and N use efficiency under long-term climate variability (1981–2020). Both models overall performed well in simulating potato tuber yield (normalized root mean square error (NRMSE) = 5.4–14.9%), above-ground biomass (NRMSE = 6.0–14.7%), N uptake (NRMSE = 18.1–25.6%), daily soil temperature (index of agreement (d) > 0.9 and Nash–Sutcliffe efficiency (EF) > 0.8), and acceptable in-soil moisture and inorganic N content (d > 0.6 and EF > ‒1) for N-applied treatments. However, the two models underestimated tuber yield and soil N content for no N fertilization treatment which was partially attributed to the underestimated soil N mineralization rate under N stress conditions. The sensitivity analysis showed that the greatest tuber yield and N use efficiency were achieved at the N rate of 150–180 kg ha⁻¹ with 2–3 splits, fertilization depth of 15–25 cm, and planting date of 25 April to 10 May in both models. This study highlights the importance of integrated management strategies in obtaining high N use efficiency and crop yield in potato production systems. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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19 pages, 5917 KiB

Open AccessArticle

Detection and Dynamic Variation Characteristics of Rice Nitrogen Status after Anthesis Based on the RGB Color Index

by Kaocheng Zhao, Ying Ye, Jun Ma, Lifen Huang and Hengyang Zhuang

Agronomy 2021, 11(9), 1739; https://doi.org/10.3390/agronomy11091739 - 29 Aug 2021

Cited by 2 | Viewed by 2156

Abstract

We aimed to elucidate the color changes of rice leaves after anthesis and create an algorithm for monitoring the nitrogen contents of rice leaves and of the whole plant. Hence, we aimed to provide a theoretical basis for the precise management of rice nitrogen fertilizer and the research and development of digital image nutrition monitoring equipment and reference. We selected the leaf colors of the main stems of four major rice varieties promoted in production, including Huaidao 5 (late-maturing medium japonica rice), Yangjing 4227 (early maturing late japonica rice), Changyou 5 (late japonica hybrid rice), and Yongyou 8 (late japonica hybrid rice). Under different nitrogen levels, the leaf R, G, and B values of the four rice varieties at different stages after anthesis, the dynamic changes in RGB normalized values, the correlations between RGB normalized values and leaf SPAD values, the leaf nitrogen content and whole plant nitrogen content, and the nitrogen prediction model were studied. The research results demonstrate the following: (1) regardless of nitrogen levels, the leaf of R, G, B, NRI, NGI and NBI of different rice varieties after anthesis followed the order, G > R > B. R, G, NRI, NGI, and days after heading could be fitted according to a logarithmic equation, y = ae^bx (0.726 ≤ R² ≤ 0.992); B, NBI, and days after heading could be fitted using a linear equation, y = a + bx (0.863 ≤ R² ≤ 0.992). Both fitting effects were significant (except NGI). (2) A quadratic function (Y = −1296.192x² + 539.419x − 10.914; Y = −1173.104x² + 527.073x − 12.993) was adopted to construct a monitoring model for the NBI and SPAD values of japonica rice and hybrid japonica rice leaves after anthesis and the R² values were 0.902 and 0.838, respectively. Exponential functions (Y = 5.698e^7.261x; Y = 3.371e^9.326x) were employed to construct monitoring models of leaf nitrogen content, and the R² values were 0.833 and 0.706, respectively. Exponential functions (Y = 5.145e^4.9143x; Y = 3.966e^5.364x) were also used to construct a monitoring model for the nitrogen content of the whole plant, and the R² values were 0.737 and 0.511, respectively. The results obtained from prediction tests by using Determination Coefficient (R²), Relative Percent Deviation (RPD), and Root Mean Square Error (RMSE) showed that it was feasible, accurate, and efficient to use a scanner for measuring the nitrogen content of rice. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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17 pages, 2623 KiB

Open AccessArticle

Spatial and Temporal Assessment of Nitrate-N under Rice-Wheat System in Riparian Wetlands of Punjab, North-Western India

by Bhupinder S. Farmaha, Pritpal-Singh and Bijay-Singh

Agronomy 2021, 11(7), 1284; https://doi.org/10.3390/agronomy11071284 - 24 Jun 2021

Cited by 8 | Viewed by 2227

Abstract

The nitrate (NO₃⁻) leaching assessment from extensive fertilizer nitrogen (N) applications to croplands is crucial to optimize fertilizer-N recommendations that do not threaten the quality of drinking groundwater. SWAP (Soil Water Atmosphere Plant), a water balance model, was linked with ANIMO (Agricultural NItrogen MOdel), a nitrate leaching model and the Geographical Information System (GIS) to assess the spatial and temporal leaching of NO₃⁻-N from fields under rice-wheat cropping system in the riparian wetlands in the Punjab in north-western India. The results revealed that NO₃⁻-N concentration in the groundwater exceeded the 10 mg NO₃⁻-N L⁻¹ limit set by the World Health Organization (WHO) for drinking water only during December–January. The verification of these results using measured values indicated that the SWAP-ANIMO model satisfactorily predicted NO₃⁻-N concentrations in the leachate in the vadose zone. A low value of the mean absolute error (0.5–1.4) and a root mean square error (0.6–1.5) was observed between the measured and the predicted NO₃⁻-N concentration across the soil profile during the validation at five sampling sites. The NO₃⁻-N predictions revealed that in the long-term, the ongoing fertilizer-N management practices in the riparian wetlands will not significantly change the average NO₃⁻-N concentration in the groundwater. The modeling approach was satisfactory for an efficient quantitative assessment of NO₃⁻-N pollution in groundwater while accounting for the spatial and temporal variability. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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Review

Jump to: Editorial, Research

8 pages, 413 KiB

Open AccessReview

Ecophysiological Crop Modelling Combined with Genetic Analysis Is a Powerful Tool for Ideotype Design

by Junfei Gu

Agronomy 2022, 12(1), 215; https://doi.org/10.3390/agronomy12010215 - 16 Jan 2022

Cited by 3 | Viewed by 1727

Abstract

Improving the grain yield of crops in both favourable and stressful environments is the main breeding objective required to ensure food security. In this review, I outline a genotype-to-phenotype approach that exploits the potential values of quantitative genetics and process-based crop modelling in developing new plant types with high yields. The effects of quantitative trait locus (QTL), for traits typically at the single-organ level over a short time scale, were projected for their impact on crop growth during the whole growing season in the field. This approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for ideotyping under contrasting conditions, i.e., use of single-environment information for predicting phenotypes under different environments. Full article

(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)

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