Applicability of Ground-based Remote Sensors for Crop N Management in Sub Saharan Africa

Teboh, Jasper M.; Tubaña, Brenda; Udeigwe, Theophilus K.; Emendack, Yves; Lofton, Josh

doi:10.5539/jas.v4n3p175

Cited by 10 publications

(7 citation statements)

References 71 publications

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“…African research is starting to adapt PA technology to both large-scale farming (Maine et al, 2007(Maine et al, , 2010 and smaller scale agriculture (Teboh et al, 2012). One of the main uses of GNSS in developing countries is mapping of farm and field boundaries to establish land tenure.…”

Section: Africamentioning

confidence: 99%

Setting the Record Straight on Precision Agriculture Adoption

2019

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Core Ideas There is a perception that adoption of precision agriculture has been slow.Precision agriculture is not one technology but a toolkit from which farmers choose what they need.Global Navigation Satellite Systems guidance is being adopted rapidly.Variable rate technology adoption rarely exceeds 20% of farms.Use of precision agriculture technology on non‐mechanized farms is almost nonexistent. There is a perception that adoption of precision agriculture (PA) has been slow. This study reviews the public data on farm level use of PA in crop production worldwide. It examines adoption estimates for PA from completed surveys that utilized random sampling procedures, as well as estimates of adoption using other survey methods, with an objective to document the national or regional level adoption patterns of PA using existing data. The analysis indicates that Global Navigation Satellite Systems (GNSS) guidance and associated automated technologies like sprayer boom control and planter row or section shutoffs have been adopted as fast as any major agricultural technology in history. The main reason for the perception that PA adoption is slow is because PA is often associated with variable rate technology (VRT)—just one of many PA technologies, one of the first adopted by many farmers, but that now rarely exceeds 20% of farms. This level of adoption suggests that farmers like the idea of VRT, but are not convinced of its value. VRT adoption estimates for niche groups of farmers may exceed 50%. The biggest gap in PA adoption is for medium and small farms in the developing world that do not use motorized mechanization.

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Section: Africamentioning

confidence: 99%

Setting the Record Straight on Precision Agriculture Adoption

2019

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“…Similar results were communicated by Sultana et al (2014) from a study in Pakistan where NDVI was positively correlated with grain yield at stem elongation (r = 0.888), booting (r = 0.950), and maturity stage (r = 0.927). Although many studies reported a good correlation between Green-Seeker measured NDVI and crop yield, such studies have not been done widely in tropical SSA (Teboh et al 2012). It is obvious from the results of this study that wide testing and evaluation of the GreenSeeker Handheld Optical NDVI sensor is of paramount importance.…”

Section: Correlation Coefficient Between Grain Yield and Ndvi Score A...mentioning

confidence: 86%

Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania

et al. 2023

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Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (r) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (P < 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (P < 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.

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“…It can be used to track rainfall, drought conditions and in identifying water resources, thereby alerting farmers beforehand on availability of water and on weather so that capital and crop planning can be done in advance [46]. Normalized Difference Vegetation Index (NVDI) values, which are one of the most important parameters to quantify the crop cultivation process, are used to notify yield prediction and plant growth [47]. Remote sensing instruments on the farm field are used for monitoring abiotic stress agents with the best possible spatial resolution [48].…”

Section: Remote Sensingmentioning

confidence: 99%

Everything You wanted to Know about Smart Agriculture

Mitra¹,

Vangipuram²,

Bapatla³

et al. 2022

Preprint

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The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups -from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.

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Applicability of Ground-based Remote Sensors for Crop N Management in Sub Saharan Africa

Cited by 10 publications

References 71 publications

Setting the Record Straight on Precision Agriculture Adoption

Setting the Record Straight on Precision Agriculture Adoption

Handheld NDVI sensor-based rice productivity assessment under combinations of fertilizer soil amendment and irrigation water management in lower Moshi irrigation scheme, North Tanzania

Everything You wanted to Know about Smart Agriculture

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