Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Nabiollahi, Kamal; Heshmat, Eskandari; Mosavi, Amir; Kerry, Ruth; Zeraatpisheh, Mojtaba; Taghizadeh‐Mehrjardi, Ruhollah

doi:10.3390/agriculture10100469

Cited by 13 publications

(10 citation statements)

References 63 publications

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“…The lack of a direct correlation in a specific study may be influenced by the specific conditions and the timing of data collection. Similarly, ( Nabiollahi et al., 2020 ) observed that the lack of significant correlations for some soil properties (CCE, pH, Silt, and EC) with the measured WY may not directly impact the WY in their study area.…”

Section: Discussionmentioning

confidence: 93%

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

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This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R2 of 0.75, RMSE of 400 (kg ha−1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha−1)] was very low compared to the mean value [4937 (kg ha−1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

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Section: Discussionmentioning

confidence: 93%

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

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“…Silt ranges from 4.00 to 61.30% with a mean value of 20.10 and CV 56.26%, from 0.50 to 54.50% with a mean value of 15.00 and CV 94.93% and from 0.20 to 54.00% with a mean value of 17.06 and CV 72.09%% in high, medium and low productivity zones respectively. Soil texture is a permanent physical property of soil that influences many functions of soil like nutrient and water uptake [13]. Variations in the soil texture might be due to the difference in nature and composition of the parent material.…”

Section: Texturementioning

confidence: 99%

Assessment of Soil Variables under Different Sesame Productivities of Northern Telangana Zone

Deepthi,

Kumar,

Ravi

et al. 2023

IJECC

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A survey was carried out in major sesame-growing areas of Northern Telangana Zone in high, medium and low productivity zones of districts namely Nizamabad, Jagtial, Nirmal and Kamareddy. 50 surface soil samples (0-15 cm) were collected from each zone and a total of 150 surface soil samples were analyzed for various physical, physicochemical and chemical properties the results showed that the bulk density ranged from 1.10 to 1.56 Mg m-3, from 1.20 to 1.60 Mg m-3 and from 1.20 to 1.63 Mg m-3 in high, medium and low productivity zones respectively. Water holding capacity ranged from 30.00 to 56.80%, from 20.80 to 56.50% and from 19.60 to 51.70% in high, medium and low productivity zones respectively. pH ranged from 7.11 to 7.82, from 6.45 to 8.51 and from 6.81 to 7.46 in high, medium and low productivity zones respectively. EC ranged from 0.32 to 0.46 d Sm-1, from 0.32 to 0.56 d Sm-1 and from 0.12 to 0.56 d Sm-1 in high, medium and low productivity zones respectively. Organic carbon ranged from 0.48 to 1.08%, from 0.23 to 0.72% and from 0.21 to 0.64% in high, medium and low productivity zones respectively. Available nitrogen ranged from 176.20 to 279.70 kg ha-1, from 116.50 to 261.50 kg ha-1 and from 76.40 to 139.80 kg ha-1 in high, medium and low productivity zones respectively. Available phosphorus ranged from 13.41 to 82.33 kg ha-1, from 11.81 to 64.71 kg ha-1 and from 19.98 to 49.06 kg ha-1 in high, medium and low productivity zones respectively. Available potassium ranged from 213.00 to 562.00 kg ha-1, from 126.25 to 585.00 kg ha-1 and from 182.50 to 562.50 kg ha-1 in high, medium and low productivity zones respectively.

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“…There is an inseparable relationship between SQ and crop production (Bünemann et al, 2018). A preceding investigation underscored a remarkable 270% increase in crop yields when associated with robust SQ, in contrast to those linked with poorer SQ (Nabiollahi et al, 2020). Furthermore, the enhancement of SQ through the application of both organic and inorganic fertilizers has been validated as a means to secure sustainable crop yields (Ren et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Effects of different tillage and fertilizer on soil quality under wheat‐maize rotation in the North China Plain

Zhang,

Chen,

Guo

et al. 2024

Land Degrad Dev

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The North China Plain plays an important role in China's crop cultivation regions, with the wheat‐maize rotation serving as the predominant cropping system. Nonetheless, a dearth of research refers to the impact of tillage and fertilizer employment on soil quality (SQ) in this region. In this study, the primary objective centered on utilizing the soil quality index (SQI) derived from both minimum and total datasets (MDS, TDS) by the linear and nonlinear scoring criteria to assess SQ. The results indicated that there was a significant correlation between wheat‐maize yield and SQI, revealing a significant association between yield and key soil parameters such as bulk density, soil porosity, total phosphorus, alkali‐hydrolyzed nitrogen, available phosphorus and cation exchange capacity at 0–60 cm soil layer. The study identifies that substantially elevated SQI mean values were completely under T8 whose values were 0.980 (linear), 0.672 (nonlinear) under MDS and 0.908 (linear), 0.701 (nonlinear) under TDS at 20–40 cm. Importantly, the annual yields were the highest when the SQI values were the maximum. Furthermore, SQ levels within this locale demonstrate a moderate to highly favorable status, ultimately underscoring the area's propitious conditions for crop cultivation. This research provides indispensable theoretical guidance for agricultural practitioners and offers a foundational framework supporting enhanced yields in local grain crops.

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Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Cited by 13 publications

References 63 publications

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Assessment of Soil Variables under Different Sesame Productivities of Northern Telangana Zone

Effects of different tillage and fertilizer on soil quality under wheat‐maize rotation in the North China Plain

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