Fertilizer Use Optimization: Principles and Approach

Kaizzi, Kayuki C.; Mohammed, Belay; Nouri, Maman

doi:10.1079/9781786392046.0009

Cited by 34 publications

(27 citation statements)

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“…Profit maximization is expected with the affordable fertilizer allocated to the highest PCR opportunities. Consideration of all potential permutations, especially with more than three crops in the system requires advanced calculations such was through use of linear optimization (Jansen et al, 2013;Kaizzi et al, 2017). Under OFRA, a dataset of >5950 geo-referenced nutrient functions was developed from results of past and OFRA-supported field research.…”

Section: Discussionmentioning

confidence: 99%

Cowpea nutrient responses for Malawi and Tanzania

Munthali¹,

Senkoro²,

Nalivata³

et al. 2018

Afr. J. Agric. Res.

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Research was conducted in Malawi and Tanzania to determine cowpea (Vigna unguiculata L. Walp) grain yield responses to applied P and K, the agronomic and economic efficiency of nutrient application, and the importance of other nutrient deficiencies. Nine site-years of research were conducted. Cowpea did not respond to fertilizer P and K in Malawi. In Tanzania, the yield response to applied P was linear with 21 kg of grain yield increase per kg of P applied. Overall, the effect of P applied at the economically optimal rate (EOR) were mean cowpea grain yield increases and profit to cost ratios (PCR), respectively, of 0.47 Mg ha -1 and 3.2 in Tanzania. Similar effects for K application in Tanzania with an EOR of 17 kg ha -1 were 0.264 Mg ha -1 yield gain and a PCR of 2.3. There were no responses to application of Mg, S, Zn and B. Financially constrained farmers are often not able to apply fertilizer at EOR for all of their cropland. The mean effect of applying K in Tanzania at 50% compared with 100% of EOR to twice as much land was 35% more production increase and 52% more PCR. The results indicate the importance of adequate availability to farmers of straight P and K fertilizers for farmer profitability. Use of multi-nutrient fertilizers implies paying for nutrients that will not give a yield response, thereby reducing the profit potential.

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Section: Discussionmentioning

confidence: 99%

Cowpea nutrient responses for Malawi and Tanzania

Munthali¹,

Senkoro²,

Nalivata³

et al. 2018

Afr. J. Agric. Res.

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“…OFRA, a project led by CABI (Kaizzi et al 2017), contains 7954 legacy rows from over 600 trials collected in the period 1960-2010. Field trials include crop yields and field conditions for majority of crops including maize, cowpea, sorghum, (lowland, upland) rice, groundnut, bean, millet, soybean, wheat, cassava, pea, climbing bean, barley, sunflower, (sweet, irish and common) potato, cotton, and similar.…”

Section: Importance Of Soil Nutrient Maps For Crop Yield Datamentioning

confidence: 99%

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Hengl

Leenaars

Shepherd

et al. 2017

Nutr Cycl Agroecosyst

252

153

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Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

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“…Crop‐nutrient‐rate choices in fertilizer use decisions are important to maximize profit (Cyamweshi et al., 2017; Wortmann & Kaizzi, 2015). Such optimization of fertilizer use requires robust information of responses to different nutrients for barley, wheat, and other crops in different recommendation domains (Kaizzi, Mohammed, & Maman, 2017). Although nutrient response trials have been conducted for wheat and barley in Ethiopia, responses have been variable, and no such information is available from the highly productive western Shewa.…”

Section: Introductionmentioning

confidence: 99%

Barley and wheat nutrient responses for Shewa, Ethiopia

et al. 2020

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Hulless barley (Hordeum vulgare L. var. nudum Hook. f.) and wheat (Triticum aestivum L.) are important crops for the highlands of western Shewa in Ethiopia. Information is scarce regarding the determination of fertilizer recommendations for barley and wheat with location specificity. On-farm trials were conducted to determine yield response to N and P and to diagnose K-Mg-S-Zn-B deficiencies in western Shewa. Grain yields were higher and responses to N and P were greater for these trials compared with results from earlier trials. Grain yield increases occurred with N application for all trials, ranging from 0.42 to 2.8 Mg ha −1 for barley and from 1.4 to 3.8 Mg ha −1 for wheat, fitting curvilinear to plateau or quadratic functions. Barley yield was maximized with 60 kg ha −1 N, and 98% of maximum yield was achieved with 50 kg ha −1 N, but wheat yield was projected to maximize with >100 kg ha −1 N. About 50% of the trials had yield increases with P application. Many farmers in this area are financially constrained regarding fertilizer use and can achieve a mean of 48% greater net return on their investment with the application at 50% rather than 100% of the rate to maximize profit per hectare. The yields and response to applied nutrients were generally less with earlier results compared with the current results, whereas the economically optimal rate of nutrient application or the rate expected to maximize net return per hectare to nutrient application was similar or slightly less for the earlier results. The profit opportunity with fertilizer use in western Shewa is great for N, less for P, and negligible for K-Mg-S-Zn-B.Abbreviations: CP, kilograms of barley or wheat required to equal the cost of 1 kg of nutrient applied; EOR, economically optimal nutrient rate; PCR, profit to cost ratio.

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Fertilizer Use Optimization: Principles and Approach

Cited by 34 publications

References 0 publications

Cowpea nutrient responses for Malawi and Tanzania

Cowpea nutrient responses for Malawi and Tanzania

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Barley and wheat nutrient responses for Shewa, Ethiopia

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