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Mallarino, Antonio P.; Oyarzabal, E. S.; Hinz, Paul N.

doi:10.1023/a:1009940700478

Cited by 75 publications

(13 citation statements)

References 13 publications

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“…However, soil available phosphorus outside this range should be considered for phosphorus deficiency or excess, which may lead to deficits of micronutrients such as Zn or Fe [37] . We recommend the lower limit of 81 mg• kg −1 K for soil available potassium which is comparable to the lower limit of 90 mg• kg −1 K according to other studies [38] .…”

Section: Thresholds Of Yield-limiting Factors For Field Managementsupporting

confidence: 70%

Plant Density, Irrigation and Nitrogen Management: Three Major Practices in Closing Yield Gaps for Agricultural Sustainability in North-West China

Guo

Shukla

et al. 2021

Front. Agr. Sci. Eng.

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• A relative yield of 70% was obtained under both border and drip irrigation. • Drip irrigation saved water and lowered yield variability compared to border irrigation. • Drip irrigation led to accumulation of soil nitrogen and phosphorus in the root zone. • Relative yield may increase 8% to 10% by optimizing field management. • Plant density, irrigation and nitrogen are major factors closing yield gap in NW China.

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Section: Thresholds Of Yield-limiting Factors For Field Managementsupporting

confidence: 70%

Plant Density, Irrigation and Nitrogen Management: Three Major Practices in Closing Yield Gaps for Agricultural Sustainability in North-West China

Guo

Shukla

et al. 2021

Front. Agr. Sci. Eng.

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“…Principal component analysis (PCA) and fuzzy means classification were frequently used to delineate soil MZs in different agriculture ecosystems with different crops [26,36,37]. A related set of data could be summarized into a few expressive components by performing principal component analysis (PCA) [38]. Kriging methods could be used in mapping principal component values [39].…”

Section: Introductionmentioning

confidence: 99%

Soil Properties Spatial Variability and Delineation of Site-Specific Management Zones Based on Soil Fertility Using Fuzzy Clustering in a Hilly Field in Jianyang, Sichuan, China

et al. 2019

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Avoiding soil degradation and improving crop productivity could be achieved by performing sustainable soil nutrient management with an appropriate understanding of soil properties' spatial variability. The present fertilizer recommendations for the region where the study area is located are typically symmetric for large regions. This leads to the under-application of fertilizers in zones with low nutrient contents and over-application in zones with high nutrient contents. Therefore, this study was conducted to assess soil management zones (MZs) in the study area for effective soil nutrient management and to evaluate soil properties' spatial variability. A total of 100 geo-referenced soil samples were collected at a depth of 0-20 cm, processed and analyzed for pH, available nitrogen (AN), available phosphorus (AP), available potassium (AK), soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP), while C:N, C:P and N:P ratios were calculated. Soil properties' coefficients of variation (CVs) widely varied from low (1.132%) to moderate (45.748%). Ordinary kriging and semi-variogram analysis showed differed spatial variability patterns for the studied soil properties with spatial dependence ranged from weak to strong. MZs were delineated by performing principal component analysis (PCA) and fuzzy K-means clustering. Four PCs with eigen values more than 1 dominated 84.44% of the total variance, so they were retained for clustering analysis. Three MZs were delineated based on the two criteria modified partition entropy (MPE) and fuzzy performance index (FPI). The studied soil properties differed significantly among MZs. Thus, the methodology used for MZ delineation could be used effectively for soil site-specific nutrient management for avoiding soil degradation concurrently with maximizing crop production in the study area.

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“…Th e use of corn extends from food products for human consumption, to animal feed, to a constituent of drugs and construction materials. Additionally, the biofuel industry considers corn grain and stover as important inconsistent correlation between plant height at early stages and grain yield, among diff erent sites (Mallarino et al, 1999), while others have reported inconsistent correlations of plant height and grain yield in dry years (Katsvairo et al, 2003). It seems that the use of plant height as the only explanatory variable cannot contribute toward a large amount of the yield variability consistently.…”

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confidence: 99%

Corn Grain and Stover Yield Prediction at R1 Growth Stage

Mourtzinis

Arriaga

Balkcom³

et al. 2013

Agronomy Journal

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Estimating corn (Zea mays L.) grain and stover yield during the growing season is an appealing idea. An accurate yield estimation could benefit farmers, as well as corn‐related industries. The objective of this study was to develop regression equations to estimate corn grain and stover yield using easily accessible information (N fertilization rate and cumulative precipitation), and simple plant morphological measurements such as height, stem diameter at various heights, height of the first ear, and plants per hectare. Measurements made at silking (R1) were used since the maximum explainable variability would not exceed 58% at early stages of plant development (V2–V10; between 2‐leaf and 10‐leaf stages). The experiment was conducted from 2009 until 2011 in two locations, in north and central Alabama, under no‐tillage and non‐irrigated conditions. Treatments were assigned to a 3 × 4 × 2 complete factorial design arranged in a split‐split‐plot with three replications. Factors were; winter rye (Secale cereale L.) cover crop (main plot), N fertilization rates (subplot), and stover residue harvest (sub‐subplot). All measurements from this study, across years and locations, were used in the final regression equations to create robust prediction models. Equations, with and without intercept, were developed and compared according to several statistical criteria. The final grain yield equation at R1 growth stage included an intercept with a R2 of 0.7705. The final stover equation also included an intercept (R2 = 0.8473). This study suggests that N rate, total precipitation amount from planting until silking, and simple plant morphological measurements can be used to predict corn yield.

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Cited by 75 publications

References 13 publications

Plant Density, Irrigation and Nitrogen Management: Three Major Practices in Closing Yield Gaps for Agricultural Sustainability in North-West China

Plant Density, Irrigation and Nitrogen Management: Three Major Practices in Closing Yield Gaps for Agricultural Sustainability in North-West China

Soil Properties Spatial Variability and Delineation of Site-Specific Management Zones Based on Soil Fertility Using Fuzzy Clustering in a Hilly Field in Jianyang, Sichuan, China

Corn Grain and Stover Yield Prediction at R1 Growth Stage

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