Agricultural R&amp;D is on the move

Pardey, Philip G.; Chan‐Kang, Connie; Dehmer, Steven P.; Beddow, Jason M.

doi:10.1038/537301a

Cited by 130 publications

(122 citation statements)

References 4 publications

(3 reference statements)

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“…Additionally, one of the main driving factors for the agricultural Green Revolution was the economic input. Gross domestic expenditures on food and agricultural research and development worldwide have increased from 27.4 to 65.5 billion of 2009 purchasing power parity (PPP) dollars from 1980 to 2010 (Pardey et al, 2016). The middle-income countries' research and development investment share increased from 29 % in 1980 to 43 % in 2011.…”

Section: Discussionmentioning

confidence: 99%

Estimating global cropland production from 1961 to 2010

et al. 2017

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Abstract. Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17-45 % to the increase in global atmospheric CO 2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation-Global Atmosphere-Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr −1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr −1 (∼ 45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history.

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Section: Discussionmentioning

confidence: 99%

Estimating global cropland production from 1961 to 2010

et al. 2017

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“…Given the paucity of privately performed food and agricultural R&D in the region (Pardey et al, 2016a) most of the evidence (390 of a total of 456 evaluations) explicitly report social returns to R&D, with the social versus private dimensions of the benefits being unspecified in the remaining studies.…”

Section: Randd Evaluation Evidencementioning

confidence: 99%

“…Roughly one-third of the evaluations refer to research done in or impacting Eastern Africa (with studies for Uganda and Zambia accounting for 89 of that region’s 150 internal rates of return, IRR , evaluations). One notable aspect of the regional split is the surprisingly small number of evaluation studies for Southern Africa compared with Eastern or Western Africa, even though that part of the continent accounted for 22.5% of the region’s public food and agricultural R&D spending for the period 1975–2011 (Pardey et al, 2016a). A large share of the SSA evaluation evidence (specifically 39.0% of the evidence reported on a by-performer basis, and 29.4% on a by-user basis) consists of African IRR results obtained in the context of evaluations that encompassed more than one country (i.e., multi-country studies) or more than one region of the world, one of which was SSA (i.e., multi-regional studies).…”

Section: Randd Evaluations Characterizedmentioning

confidence: 99%

“…Notwithstanding this consensus, investments in research and development (R&D) targeted to many national food and agricultural economies throughout Africa are fragile and faltering. Pardey et al (2016a) reported that in 2011, national investments in food and agricultural R&D (public and private combined ) throughout SSA constituted just 3.9% ($2.7 billion, 2009 PPP prices) of the corresponding world total investment of $69.3 billion; a decline on the 6.1% SSA share three decades earlier in 1980 1. Moreover, 39% (i.e., 17 of a total of 44) of the region’s countries spent less on public food and agricultural R&D in 2011 than they did in 1980, after adjusting for the rising costs of research.…”

Section: Introductionmentioning

confidence: 99%

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Returns to food and agricultural R&D investments in Sub-Saharan Africa, 1975–2014

et al. 2016

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Research-enabled growth in agricultural productivity is pivotal to sub-Saharan Africa’s overall economic growth prospects. Yet, investments in research and development (R&D) targeted to many national food and agricultural economies throughout Africa are fragile and faltering. To gain insight into what could be driving this trend, this article updates, summarizes and reassesses the published evidence on the returns to African agricultural R&D. Based on a compilation of 113 studies published between 1975 and 2014 spanning 25 countries, the reported internal rates of return (IRRs) to food and agricultural research conducted in or of direct consequence for sub-Saharan Africa averaged 42.3%py. In addition to the 376 IRR estimates, the corresponding 129 benefit-cost ratios (BCRs) averaged 30.1. Most (96.5%) of the returns-to-research evaluations are of publicly performed R&D, and the majority (87.6%) of the studies were published in the period 1990–2009. The large dispersion in the reported IRRs and BCRs makes it difficult to discern meaningful patterns in the evidence. Moreover, the distribution of IRRs is heavily (positively) skewed, such that the median value (35.0%py) is well below the mean, like it is for research done elsewhere in the world (mean 62.4%py; median 38.0%py). Around 78.5% of the evaluations relate to the commodity-specific consequences of agricultural research, while 5.5% report on the returns to an “all agriculture” aggregate. The weight of commodity-specific evaluation evidence is not especially congruent with the composition of agricultural production throughout Africa, nor, to the best that can be determined, the commodity orientation of public African agricultural R&D.

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“…Governments around the world are increasingly investing in agricultural technology development, and private sector spending is catching up with public sector spending. Achieving higher levels of productivity to feed the wealthier and more urbanized population of the future will require significant investment in agriculture research and development [3].…”

Section: Introductionmentioning

confidence: 99%

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

et al. 2018

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Abstract:In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk's points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.

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Agricultural R&D is on the move

Cited by 130 publications

References 4 publications

Estimating global cropland production from 1961 to 2010

Estimating global cropland production from 1961 to 2010

Returns to food and agricultural R&D investments in Sub-Saharan Africa, 1975–2014

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

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