Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT

Lundström, Christina; Lindblom, Jessica

doi:10.1016/j.agsy.2017.10.004

Cited by 74 publications

(60 citation statements)

References 39 publications

(70 reference statements)

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“…Other factors were as in Exp. 1 fertilization rates (0 (N0), 120 (N5), 180 (N7), and 240 (N8) kg•ha −1 ) were applied in the form of urea at a rate of 50% at pre-planting and 50% at tillering, and four topdressing N fertilizer rates (0 (T0), 80 (T3), 120 (T6), and 160 (T7) kg•ha −1 ) were also applied in the form of urea at a rate of 50 at stem elongation and 50% at booting. A no-topdressing treatment (T0) was also examined at each N gradient.…”

Section: Experiments Designmentioning

confidence: 99%

“…Rice (Oryza sativa L.) is a staple food for billions of people worldwide. China is the largest rice producer in the world, with approximately 31 million hectares of paddy rice cultivation [1]. To ensure sustainable food production, agricultural producers invest in a significant amount of nitrogen (N) fertilizer [1].…”

Section: Introductionmentioning

confidence: 99%

“…China is the largest rice producer in the world, with approximately 31 million hectares of paddy rice cultivation [1]. To ensure sustainable food production, agricultural producers invest in a significant amount of nitrogen (N) fertilizer [1]. Zhang et al reported that the N use efficiency (NUE) of China's major food crops was less than 30% [2], representing 33% of the global NUE, and 40% of that in developed countries in Europe and America [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Zhang

Shen

et al. 2019

Remote Sensing

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Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.

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Section: Experiments Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Zhang

Shen

et al. 2019

Remote Sensing

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“…They found that the use of farmers' experience could be an efficient and simple tool that could reduce costs in comparison with the traditional method of using stable soil variables and relief. Another interesting experience, although not specifically addressed to the delineation of management zones, analysed how to integrate farmers' experience-based knowledge in decision support systems, in order to calculate variable N rate application from satellite images [29]. They found that PA technology must fit doubly the technical needs of farmers and their usual practices to be adopted.…”

Section: Introductionmentioning

confidence: 99%

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

2018

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Abstract:One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer's expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers' expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.

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“…Bovendien moet er rekening gehouden wordt met de voorkennis van de eindgebruiker. Een mogelijkheid is dat bij toenemende complexiteit een concept voor scholing en ondersteuning in het gebruik en interpretatie gelijktijdig ontwikkeld wordt om zeker te stellen dat het hulpmiddel bij de gebruiker komt (Rose & Bruce, 2017) en (Lundström & Lindblom, 2018…”

Section: Van Behoeftes Naar Conceptontwikkelingunclassified

Beslisondersteunende hulpmiddelen voor de ruwvoerproductie in Nederland : Inventarisatie van online beslisondersteunende hulpmiddelen en analyse van het gebruik in de praktijk

Bufe¹,

Wesselink²,

Verhoeven³

et al. 2018

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Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT

Cited by 74 publications

References 39 publications

Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

Beslisondersteunende hulpmiddelen voor de ruwvoerproductie in Nederland : Inventarisatie van online beslisondersteunende hulpmiddelen en analyse van het gebruik in de praktijk

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