Erratum: Corrigendum: Climate-smart agriculture for food security

Lipper, Leslie; Thornton, Philip K.; Campbell, Bruce M.; Baedeker, Tobías; Braimoh, Ademola K.; Bwalya, Martin; Caron, Patrick; Cattaneo, Andrea; Garrity, D. P.; Henry, Kevin; Hottle, Ryan Darrell; Jackson, Louise E.; Jarvis, Andy; Kossam, Fred; Mann, Wendy; McCarthy, Nancy; Meybeck, A.; Neufeldt, Henry; Remington, Thomas F.; Sen, Pham Thi; Sessa, Reuben; Shula, Reynolds; Tibu, Austin; Torquebiau, Emmanuel

doi:10.1038/nclimate2586

Cited by 8 publications

(2 citation statements)

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“…The healthy development of agriculture directly affects the economic stability and social prosperity. Therefore, it is of great significance to carry out accurate and timely evaluation of regional grain crops to regulate the grain market, and guide the adjustment of planting structure [1,2] .…”

Section: Introductionmentioning

confidence: 99%

Regional winter wheat yield estimation based on CASA model and improved harvest index using remote sensing images

Li,

Zhao,

Yang

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R 2 ) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha.

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Section: Introductionmentioning

confidence: 99%

Regional winter wheat yield estimation based on CASA model and improved harvest index using remote sensing images

Li,

Zhao,

Yang

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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“…Climate change/variability brought about by human activity and natural processes is expected to have a signi cant impact on world food supply [1]. Low-income countries in tropical regions with low adaptation capacities are particularly sensitive to the negative e ects of climatic variability and change [2].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Local Climate and Its Impact on Faba Bean (Vicia faba L.) Yield in Central Ethiopia

Bogale

Maja

Hadgu

2022

Advances in Meteorology

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Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (1988–2017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970 mm with SD of 145.6 and coefficient of variation (CV %) of 15%. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV% of 35.2%. The probability of dry spell less than 7 days was high (>80%) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0% on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7 mm and 2.32 mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r = −0.407 and −0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmer’s adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield.

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