On farm verification of soil test-based phosphorus fertilizer recommendations for bread wheat (<i>Triticum aestivum</i> L.) on the vertisols of central highlands of Ethiopia

Shewangizaw, Beza; Yassin, Kenzemed Kassie; Assefa, Shawl; Feyisa, Tesfaye

doi:10.1080/23311932.2020.1807811

Cited by 2 publications

(2 citation statements)

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“…Farmers can choose to use a comprehensive mix of individual fertilizers based on recommendations, or they can purchase specific fertilizers produced by companies that obtain soil tests (with regional variations). STFFT achieves the two most obvious advantages of scientific quantification and symptomatic treatment by mastering the nutrient demand of crops and the nutrient supply of the soil [ 20 , 21 , 22 , 23 ]. This technology is rooted in the basic theories of the three laws of fertilizer, the doctrine of nutrient restitution and the law of diminishing returns, which effectively reduces waste of resources and improves the efficiency of fertilizer utilization, thus ensuring the ecological balance and structural integrity of the soil [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

What Influences Farmers’ Adoption of Soil Testing and Formulated Fertilization Technology in Black Soil Areas? An Empirical Analysis Based on Logistic-ISM Model

Liu

Xue

2022

IJERPH

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Along with the increasing prominence of environmental risks such as soil surface source pollution and declining quality grade of arable land, the issues of how to address irrational fertilizer application and enhance the safety of agricultural products have attracted widespread attention. In this context, clarifying the main factors affecting farmers’ use of soil testing and formulated fertilization technology (STFFT) can further improve the technology adoption rate and fertilizer utilization efficiency, promote standardized agricultural production and maintain the health and stability of soil ecology in black soil areas. This is of great significance to the construction of green agriculture, national dietary health and national food security. This study builds an “external environmental stimuli-perceived characteristics-adoption behavior” theoretical framework to investigate the decision-making and the dynamic influence mechanisms of farmers’ adoption behavior of STFFT. Based on farmer survey data, the logistic-ISM model has been applied. The main findings are as follows. First, five types of influencing factors, namely individual characteristics, family characteristics, business characteristics, cognitive characteristics and external environmental characteristics, had significant “push” effects on farmers’ STFFT adoption behavior. Among them, planting scale and technical training are the key factors influencing farmers’ adoption of scientific fertilizer application technology. Second, both farmers’ perceived ease of use and perceived usefulness play a significant role in farmers’ decision-making process, and the easier farmers perceive STFFT to be to master and the greater the benefits it brings, the more pronounced the tendency to adopt the technology, all other influencing conditions being equal. Third, the main influencing factors of farmers’ STFFT adoption behavior are intrinsically related and divided into four categories based on the magnitude of influence: deep-rooted, medium indirect, shallow indirect and superficial direct. In order to reduce further degradation of black soil caused by farmers’ irrational production habits and to improve resource utilization efficiency, this study recommends the government to further regulate the land transfer market, strengthen the propagation of soil-conservation-type technologies in black soil areas, expand the breadth of agricultural technology training and enhance farmers’ understanding and trust in STFFT. Thus, the maintenance of soil ecosystem in black soil areas, effective guarantee of food security and sustainable development of agriculture can be achieved.

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Section: Introductionmentioning

confidence: 99%

What Influences Farmers’ Adoption of Soil Testing and Formulated Fertilization Technology in Black Soil Areas? An Empirical Analysis Based on Logistic-ISM Model

Liu

Xue

2022

IJERPH

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“…Relative yield was calculated as the ratio of yield in the control treatment (Yo) to the maximum yield (Ymax) observed in the other P-involved treatments. The Cate-Nelson procedure sets all the relative yield values against the STP values on a scatter diagram upon which vertical and horizontal lines are superimposed to maximize the number of points in the first and third quadrants [17,25,69]. The performance criteria of the Cate-Nelson partitioning models were calculated using the number of points in the first, second, third, and fourth quadrants, which corresponds to the false-positive (FP), true-negative (TN), false-negative (FN), and true-positive (TP) quadrants.…”

Section: Determination Of Critical Stp Concentration and Delineation ...mentioning

confidence: 99%

Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning

Chipatela,

Khiari,

Jouichat

et al. 2024

Agronomy

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The traditional approach of prescribing phosphate fertilizer solely based on soil test P (STP) has faced criticism from scientists and agriculturists pushing farmers to seek phosphate fertilization models that incorporate additional factors. By embracing integrated approaches, farmers can receive more precise recommendations that align with their specific conditions and fertilization techniques. This study aimed to utilize artificial intelligence prediction to replicate soybean response curves to fertilizer by integrating edaphic and climatic factors. Literature data on soybean response to P fertilization were collected, and the Random Forest (RF) algorithm was applied to predict response curves. The predictions utilized seven predictors: P dose, STP, soil pH, texture, % OM, precipitation, and P application methods. These predictions were compared to the traditional STP-based approach. The STP-based P prescription models exhibited extremely low robustness values (R2) of 1.53% and 0.88% for the PBray-1 and POlsen diagnostic systems, respectively. In contrast, implementing the RF algorithm allowed for more accurate prediction of yield gains at various P doses, achieving robustness values of 87.4% for the training set and 60.9% for the testing set. The prediction errors remained below 10% throughout the analysis. Implementing artificial intelligence modeling enabled the study to achieve precise predictions of the optimal P dose and customized fertilization recommendations tailored to farmers’ specific soil conditions, climate, and individual fertilization practices.

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On farm verification of soil test-based phosphorus fertilizer recommendations for bread wheat (Triticum aestivum L.) on the vertisols of central highlands of Ethiopia

Abstract: To cite this article: Beza Shewangizaw, Kenzemed Kassie, Shawl Assefa & Tesfaye Feyisa | (2020) On farm verification of soil test-based phosphorus fertilizer recommendations for bread wheat (Triticumaestivum L.

Cited by 2 publications

References 10 publications

What Influences Farmers’ Adoption of Soil Testing and Formulated Fertilization Technology in Black Soil Areas? An Empirical Analysis Based on Logistic-ISM Model

What Influences Farmers’ Adoption of Soil Testing and Formulated Fertilization Technology in Black Soil Areas? An Empirical Analysis Based on Logistic-ISM Model

Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning

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