scholarly journals LOADING THE DICE IN FAVOUR OF THE FARMER: REDUCING THE RISK OF ADOPTING AGRONOMIC INNOVATIONS

2016 ◽  
Vol 55 (S1) ◽  
pp. 67-83 ◽  
Author(s):  
RIC COE ◽  
JOYCE NJOLOMA ◽  
FERGUS SINCLAIR
Keyword(s):  
Crop Production ◽  
Agricultural Development ◽  
Maize Yield ◽  
Scaling Up ◽  
Development Projects ◽  
Pilot Studies ◽  
Fertilizer Use ◽  
Production Technologies ◽  
New Crop ◽  
Agroforestry Technologies

SUMMARYAgricultural development projects frequently promote new crop production technologies for adoption at scale on the basis of research and pilot studies in a limited number of contexts. The performance of these production technologies is often variable and dependent on context. Using an example from the Agroforestry for Food Security Project in Malawi, that promoted agroforestry technologies for soil fertility enhancement, we explore the nature and implications of variation in performance across farmers. Mean effects of these technologies, measured by differences in maize yield between agroforestry and sole maize plots, were modest but positive. However, there was large variation in those differences, some explained by altitude, plot management and fertilizer use but with much unexplained. This represents risk to farmers. Those communicating with farmers need to be honest and clear about this risk. It can be reduced by explanation in terms of contextual factors. This should be an aim of research that can often be embedded in scaling up the promotion of agronomic innovations.

ON-FARM TRIALS AS ‘INFECTION POINTS’? A RESPONSE TO WALL ET AL.

2019 ◽  
Vol 55 (2) ◽  
pp. 195-199 ◽  
Author(s):  
J. A. ANDERSSON ◽  
T. J. KRUPNIK ◽  
N. DE ROO
Keyword(s):  
Case Studies ◽  
Southern Africa ◽  
Site Selection ◽  
Crop Production ◽  
Agricultural Research ◽  
Smallholder Farmers ◽  
Production Technologies ◽  
On Farm ◽  
New Crop

In their response to our paper on the problems of using on-farm trials in efforts to scale-out new crop production technologies and practices among smallholder farmers, Wall et al. (2019) focus on our descriptions of on-farm trials in just one of the three case studies of Agricultural Research for Development (AR4D) projects that were presented. They argue we did not understand the projects’ philosophy and that the biases in farmer and site selection we discussed, do not exist in the southern Africa case study.

scholarly journals Conservation agriculture: new crop production technologies

2019 ◽  
Vol 350 ◽  
pp. 012039
Author(s):  
G E Larina ◽  
L G Seraya ◽  
I O Ivanova ◽  
I N Kalembet ◽  
L M Poddymkina
Keyword(s):  
Crop Production ◽  
Conservation Agriculture ◽  
Production Technologies ◽  
New Crop

scholarly journals Long-run Spatial and Temporal Yield Variability Analysis of Three Major Crops Affected by Fertilizer Use and Rainfall in Ethiopia over the Past 15 Years (2004/05–2018/19)

2021 ◽  
Vol 10 (3) ◽  
pp. 1
Author(s):  
Mulugeta Demiss ◽  
Joaquin Sanabira ◽  
Upendra Singh
Keyword(s):  
Crop Production ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Fertilizer Use ◽  
The Past ◽  
Long Run ◽  
Sub Saharan ◽  
The Government

Ethiopia is one of the major producers of maize and wheat and the only producer of teff at a larger scale for grain in sub-Saharan Africa (SSA). Various efforts have been made by the government of Ethiopia to increase productivity over the past 15 years. Here we analyze a dataset with more than 1,260 yield observations from 2004/05 to 2018/19 for three crops (teff, maize, and wheat) in the 28 zones of the two major cereal-growing regions of the country. These two regions, Amhara, and Oromia represent around 81% of cropped area, 75% of fertilizer use, and 82% of cereal production of Ethiopia annually. Zonal level crop production data were used to analyze spatial and temporal patterns of teff, wheat, and maize yield. Zones were categorized as wet and dry based on annual rainfall. We dissect the evolution of yield trends over time and space, analyze yield variation, and evaluate whether growth of yields has increased, decreased, or stalled in recent years. We found that productivity of teff, wheat, and maize continued to increase from 0.95 to 1.76 metric tons per hectare (mt ha -1), 1.56 to 2.76 mt ha -1, and 1.72 to 4.0 mt ha -1 respectively, between 2004/05 and 2018/19. There was an average annual increase of 5%–8% during this time. The data also show a strong correlation of yield with rainfall and fertilizer use patterns; therefore, we recommend that the fertilizer advisory service should also make use of the rainfall conditions of the different locations to fine-tune fertilizer recommendations.

scholarly journals Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1551
Author(s):  
Tamoor Khan ◽  
Jiangtao Qiu ◽  
Hafiz Husnain Raza Sherazi ◽  
Mubashir Ali ◽  
Sukumar Letchmunan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Production ◽  
Agricultural Development ◽  
Production Efficiency ◽  
Fruit Production ◽  
National Bureau ◽  
System A ◽  
Future Production ◽  
Learning Techniques ◽  
Agricultural Yields

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

scholarly journals Impacts of participatory variety selection in wheat on agro-economic changes of wheat farmers in Bangladesh

2008 ◽  
Vol 32 (3) ◽  
pp. 335-347 ◽  
Author(s):  
DB Pandit ◽  
ME Baksh ◽  
MA Sufian ◽  
M Harun-ur-Rashid ◽  
MM Islam
Keyword(s):  
Attitude Change ◽  
Agricultural Development ◽  
Household Survey ◽  
Base Line ◽  
Wheat Varieties ◽  
Economic Changes ◽  
Variety Selection ◽  
New Varieties ◽  
Production Technologies ◽  
Participatory Variety Selection

Impacts of participatory variety selection in wheat on agro-economic changes like adoption of new wheat varieties and production technologies, income and attitude change of the wheat farmers, etc. are presented in the paper. Participatory variety selection was conducted at 12 villages of four districts in Bangladesh. Base line information from the villages was collected through participatory rural appraisal and household survey in 2002. Data on agro-economic changes were collected through household survey in 2005. Impacts were assessed from the difference of the data of two surveys. The area of the check variety Kanchan came down from 97.8% (covered in 2002) to 57% in the working villages in 2005. Varietal diversity was increased remarkably and seven varieties were found to cultivate in 2004-05. The new varieties occupied 43% of the wheat areas. Seed preservation by farmers was increased remarkably and 208 tons seeds of new varieties were preserved by them in 2004-05. When 60% seeds of their total requirements were collected from Bangladesh Agricultural Development Corporation in 2002, then, 100% seeds were used from farmers’ own source in 2004-05. There were remarkable changes in production technology adoption, sources of agricultural knowledge, attitude and income changes. Farmers’ income was increased to Tk. 11148/ha due to cultivation of new varieties and use of recommended production technologies. Participatory variety selection approach in wheat was found very useful to increase wheat production in the working villages. Widespread use of this approach may be useful throughout the county in other crops also.DOI: http://dx.doi.org/10.3329/bjar.v32i3.462Bangladesh J. Agril. Res. 32(3) : 335-347, September 2007

scholarly journals Environmental Life-Cycle Assessment of Arable Crop Production Technologies Compared to Different Harvesting Work Systems in Short Rotation Energy Plantations

2019 ◽  
Vol 15 (2) ◽  
pp. 55-68
Author(s):  
András Polgár ◽  
Zoltán Kovács ◽  
Veronika Elekné Fodor ◽  
András Bidló
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Crop Production ◽  
Short Rotation ◽  
Environmental Life Cycle Assessment ◽  
Arable Crop ◽  
Production Technologies ◽  
Energy Plantations ◽  
The Impact ◽  
Rotation Energy

Abstract Environmental life cycle assessment (LCA) was developed as a tool for sustainable, decision-supporting environmental management. Applying agricultural sector-LCA in order to achieve both internal (comparative) and external (efficiency enhancing) benefits is a priority. Since the life-cycle assessment of products and processes attracts great interest, applying the method in agriculture is relevant. Our study undertakes a comparative environmental life-cycle assessment (LCA) of local arable crop production technologies used for the main cultivated plants: maize, sunflower, lucerne, cereals, and canola (environmental data in the territorial approach calculated on a 1 ha unit and in the quantitative approach calculated on 1 t of produce). We prepared an environmental inventory of the arable crop production technologies, constructed the life-cycle models, and executed the impact assessment. We also compiled an environmental ranking of technologies. In the impact interpretation, we compared the results with the values of short rotation energy plantations in each impact category. We analysed carbon footprints closely. The obtained results help better assess environmental impacts, climate risks, and climate change as they pertain to arable crop production technologies, which advances the selection of appropriate technologies adjusted to environmental sensitivities.

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