Crop climate suitability mapping on the cloud: a geovisualization application for sustainable agriculture

Peter, Brad G.; Messina, Joseph P.; Lin, Zihan; Snapp, Sieglinde S.

doi:10.1038/s41598-020-72384-x

Cited by 41 publications

(33 citation statements)

References 81 publications

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“…First, geovizualisation tools using remote sensing data to support weather and agricultural information more readily lend themselves to vast subcontinents, such as large parts of Africa [65], India, and North America. In complex terrains, such as in Southeast Asia, microclimates and farming methods are poorly represented as 0.5-1 km pixel information [66].…”

Section: Initial Observations On Overall Search Resultsmentioning

confidence: 99%

Do Digital Climate Services for Farmers Encourage Resilient Farming Practices? Pinpointing Gaps through the Responsible Research and Innovation Framework

Simelton¹,

McCampbell

2021

Agriculture

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Digital climate services can support agricultural management decisions under uncertain climatological conditions and may contribute to achieving the ambitions of the fourth agricultural revolution. However, do they encourage social and environmental aspects? Our analysis builds on the four dimensions of the Responsible Research and Innovation framework and evaluates, among other things, which production systems are promoted in climate service apps; how the services contribute to or challenge (inter)national targets for sustainable development, ecosystem restoration, and climate resilience. From a longlist of apps, we present the best documented ones as case studies: nine weather-based and two non-weather-based digital services. We target apps of relevance for Southeast Asian smallholder farming systems, where both supply of and demand for such apps have this far been limited in contrast to the access to phones, and where particularly the supply of apps is poorly documented. The key findings point out several gaps. First, digitalization in Southeast Asia’s farming system is driven by foreign investments, while partnerships with public agencies, in particular national Met Offices, were rare. Services were developed for farmers but not necessarily with farmers, thereby overlooking needs and social factors such as (digital) literacy and trust. While some of the weather-based apps included more than one crop, they primarily support single solutions and none of them targeted mixed or integrated farming systems. This calls for developers of digital climate services to innovate applications in an inclusive manner, and to support governments in achieving their commitments to global climate, biodiversity, and sustainability goals. Difficulties in generating comparable information about the reviewed apps regardless of the study’s geographical focus demonstrates a need for more transparent means and protocols for users to assess and compare digital climate services.

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Section: Initial Observations On Overall Search Resultsmentioning

confidence: 99%

Do Digital Climate Services for Farmers Encourage Resilient Farming Practices? Pinpointing Gaps through the Responsible Research and Innovation Framework

Simelton¹,

McCampbell

2021

Agriculture

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“…It is a hybrid method between phytosociology and edaphology, which is of interest to environmental and agricultural agencies, because it enhances sustainable agricultural development [50], avoiding impacts [51] and mitigating climate change, which is causing an irregularity in production and food security. While Peter et al [52] propose the use of Google Earth to select the best biotopes for cultivation, this tool is useful but not satisfactory; the use of bioclimatic maps and the phytosociological study of plant covers is necessary to obtain the amount and type of nutrients ground. Cover crops are necessary to maintain the richness of rhizobacteria, which intervene in the assimilation of nutrients and in plant growth [53,54].…”

Section: Discussionmentioning

confidence: 99%

Indicative Value of the Dominant Plant Species for a Rapid Evaluation of the Nutritional Value of Soils

et al. 2020

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A study was conducted on 14 grassland communities located in the south of the Iberian Peninsula and their edaphology, which is identified as specific plant associations. The edaphic study of each association allows a rapid evaluation of the nutrient content in the soil without the need for laboratory edaphic analysis. For each phytosociological relevé and soil, samplings were carried out. The field data were subjected to various statistical analysis—canonical correspondence analysis (CCA), Bayesian networks, and decision trees—to establish nutrient content. When the abundance value of the species is 9 in the Van der Maarel scale, there is an increase in the values of several soil parameters. In the case of Hordeum leporinum, when the Van der Maarel index is 9, the Kc (exchangeable potassium in cmol/kg) undergoes the greatest variation, to a value of up to 0.729 cmol/kg. The application of the decision tree to this species reveals that the soil attributes with the greatest influence in the classification are conductivity, %_si (silt texture), pH, and pF 15 atm (pressure at 15 atmospheres (water retention capacity) in %). Indeed, this interlaced edaphic and phytosociological study provides us with a high-value tool to obtain quick information on the content of nutrients in the soil.

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“…The impact of climate change on cropland suitability is very important to sustainable agriculture (Worqlul et al 2019). Based on their research, Peter & Messina (2020) proposed the factors that affect cropland suitability are temperature and rainfall. In addition, Tesfay et al (2017) found the slope profile, soil type, and moisture are also important factor of cropland suitability.…”

Section: Research Variablementioning

confidence: 99%

Iot Based Smart Agro-Industrial Technology With Spatial Analysis

et al. 2020

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Smart teknologi berkembang pesat di sektor agroindustri. Tujuan dari penelitian ini adalah merancang dan mengembangkan sistem agroindustri kentang yang optimal dan adaptif. Dalam penelitian ini ditambahkan Internet of Things (IoT) yaitu penginderaan jauh untuk memprediksi jumlah panen dan kapasitas produksi. Sebelum menerapkan IoT, perspektif spasial dianalisis menggunakan spasial dan geoprocessing. Sampel penelitian menggunakan random grid di Wonosobo, Jawa Tengah, Indonesia. Titik optimal diperoleh di Kejajar (N1), Garung (N2), Kalikajar (N3), Kepil (N4) dan Mojotengah (N5). Pada penelitian ini dipasang sensor suhu dan kelembaban SHT15 serta curah hujan Rain Gauge di 5 titik. Sensor memberikan data secara rutin selama 30 hari. Berdasarkan analisis kesesuaian lahan, lokasi yang paling sesuai adalah Kejajar (N1) di koordinat 7°14'11.8"LS, 109°56'29.7"BT. Hasil penelitian menunjukkan bahwa berdasarkan 24 titik sampel dengan luas rata-rata 2,5m x 2,5m dihasilkan rata-rata total panen 8,62 kg/m 2 dan prediksi produktivitas panen 13,79 ton/ha. Sistem ini dapat memprediksi jumlah panen dan kapasitas produksi yang baik dengan tingkat akurasi 89,35%. Dengan demikian, metode ini dapat digunakan dan merepresentasikan pertanian melalui inovasi digital menggunakan smart teknologi agroindustri. Untuk penelitian selanjutnya, metode ini dapat dilanjutkan untuk penanganan pascapanen dengan menggunakan Sistem Operasi berbasis Android.

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Crop climate suitability mapping on the cloud: a geovisualization application for sustainable agriculture

Cited by 41 publications

References 81 publications

Do Digital Climate Services for Farmers Encourage Resilient Farming Practices? Pinpointing Gaps through the Responsible Research and Innovation Framework

Do Digital Climate Services for Farmers Encourage Resilient Farming Practices? Pinpointing Gaps through the Responsible Research and Innovation Framework

Indicative Value of the Dominant Plant Species for a Rapid Evaluation of the Nutritional Value of Soils

Iot Based Smart Agro-Industrial Technology With Spatial Analysis

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