Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

Chivasa, Walter; Mutanga, Onisimo; Biradar, Chandrashekhar

doi:10.1080/01431161.2017.1365390

Cited by 59 publications

(48 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, farmers in developing countries typically have only limited access to agricultural expertise and technologies and often lack the ability to accurately estimate yields and plot sizes. While there are a few reviews on applications of satellite data to identify cropping practices (Bégué et al 2018), tillage (Zheng et al 2014), land cover (Gómez et al 2016), and yield estimation (Chivasa et al 2017), they focused on the technical aspects of remote sensing rather than on the possibilities of integrating satellite data into adoption and impact studies. Here we specifically discuss how typical estimation approaches and data types used by economists, such as econometric estimation using farm-household survey data, could be enhanced further through integrating satellite data.…”

Section: Introductionmentioning

confidence: 99%

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Kubitza

Krishna

Schulthess

et al. 2020

Agron. Sustain. Dev.

View full text Add to dashboard Cite

Development and dissemination of sustainable practices are key to enhance agricultural productivity in developing countries and to curtail potential negative externalities. Rigorous adoption/impact evaluations provide valuable lessons to enhance the capacity of agricultural research-for-development (R4D) systems in this context. Conventional evaluation studies rely solely on farmhousehold surveys for data. Generation of survey data however requires considerable financial and human capital, and the process often misses several important explanatory variables, ignores the longer-term impacts, and suffers from measurement errors. Complementary data sources are explored to make the evaluations more robust and rigorous. Here we review 54 studies that used satellite data to estimate adoption and impact of agricultural practices in developing countries. Some evidence on successful application of satellite data in high-income countries is also provided. The main findings of the paper are threefold: (1) satellite data have been successfully used to detect agricultural practices, such as cropping intensity, tillage, crop residue cover, irrigation, and soil and water conservation; (2) only a few studies have estimated the yield impacts of agricultural practices, although the estimation of crop yields with satellite data is fairly developed; and (3) only a small number of studies have explored impact estimation beyond the biophysical sphere. Estimation of certain environmental impacts of agricultural practices is possible through satellite data, although only a few studies have carried it out. Not many have assessed the economic impacts of interventions. We conclude that satellite data analysis allows information access with little delay and over longer periods, provide a unique set of variables over wide geographies, and reduce measurement error in certain variables. However, more interdisciplinary research is necessary to speed up the uptake of this alternative data source in R4D evaluations.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Kubitza

Krishna

Schulthess

et al. 2020

Agron. Sustain. Dev.

View full text Add to dashboard Cite

show abstract

“…For example, remotely sensed imagery provides timely information to assist operations involving nutrient concentration modeling, disease detection, and water stress assessment [3][4][5][6]. Information derived from remote sensing imagery has benefitted growth monitoring and yield modeling [7][8][9]. Breeding programs benefit from diverse, low cost, and high throughput remote sensing technologies capable of measuring plant traits [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

et al. 2020

View full text Add to dashboard Cite

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

show abstract

“…The actual grain crop yield is affected to different degrees by the physical environment and social factors [1][2][3][4], such as irregular topography [1,3], irrigation [5,6], soil nutrients [1,7], and the application of pesticides and chemical fertilizers [8,9]. These factors can generate grain crop yield differences, even at a fine scale [10][11][12][13][14]. Understanding the spatial distribution of the grain crop yield of specific regions plays a crucial role in policy-making and market management [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Identifying the major obstacles or opportunities at a grid scale can help in maximizing the productivity of cultivated land, optimizing the allocation of various physical resources, and decreasing production losses that are caused by inappropriate farming practices or land use patterns [15,17]. Some achievements have been made for single grain crop productivity spatialization, including paddy rice [15,18], maize [12,18], wheat [6,19,20], and barley [21], and multiple grain crops at global [17,18,22,23], national [24][25][26], and regional [5,15,27,28] scales because of the advantages of raster data. The estimation of the grain crop yield can be divided into five productivity levels, including photosynthetic productivity, light and temperature productivity, climatic productivity, soil productivity and land productivity [16].…”

Section: Introductionmentioning

confidence: 99%

Spatialization of Actual Grain Crop Yield Coupled with Cultivation Systems and Multiple Factors: From Survey Data to Grid

Zhang

2020

Agronomy

View full text Add to dashboard Cite

The spatialization of actual grain crop yield helps to understand the spatial heterogeneity of yield and support for the precise farming and targeted farmland management. However, inadequate consideration and quantification of anthropogenic factors affecting the estimation of actual yield distribution easily cause uncertainties in recent researches. Here, we developed a new grain crop yield spatialization (GCYS) model in order to downscale the yield from county to grid scale. The GCYS model is composed of four modules: (a) cultivated land Net Primary Productivity (NPP) calculation module, (b) comprehensive agricultural system construction module, (c) key factors establishment module, and (d) integration and validation module. Its novelty is to realize the actual grain crop yield spatialization from county scale to grid scale by quantifying and spatializing the comprehensive agricultural system when considering the diversity of cultivated structure and field management factors. Taking Guizhou and Guangxi Karst Mountains Region as a study-area, the GCYS model is developed and tested. The determination coefficients of regression analysis between agricultural survey data and spatialization results of paddy rice yield calculated by our model reach 0.72 and 0.76 in 2000 and 2015, respectively (p < 0.01). The results visualize the spatial pattern of actual grain crop yield at the grid scale, which show a gradually decreasing trend from southeast to northwest. With an increase in potential yield and improvement of field management technologies, the actual average yield of grain crops per unit increased form 4745.10 kg/ha of 2000 to 5592.89 kg/ha of 2015. Especially in high-yield zones in southeast area, mechanized cultivation became the dominated factor, rather than chemical fertilizer application. This demonstrates that our model can provide a reference for agricultural resource utilization and policy-making.

show abstract

Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

Cited by 59 publications

References 112 publications

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

Spatialization of Actual Grain Crop Yield Coupled with Cultivation Systems and Multiple Factors: From Survey Data to Grid

Contact Info

Product

Resources

About