scholarly journals Effect of climate variations on maize yields across Abeshge district in Ethiopia

2021 ◽  
Vol 23 (3) ◽  
pp. 299-305
Author(s):  
SOLOMON ABIRDEW YIRGA
Keyword(s):  
Maize Yield ◽  
Maximum Temperature ◽  
Onset Date ◽  
Total Rainfall ◽  
Growing Period ◽  
Strong Positive Relationship ◽  
Rain Season ◽  
Dry Spell ◽  
Maize Yields ◽  
Rainy Days

The mean onset date, cessation date and length of growing period of the main rain season remained May 5, September 14 and 133 days, respectively across Abeshge district in Ethiopia. The dry spell is minimum during the peak rainy season (June to August or DOY 153-244) and switch upward once more around DOY 247 (September 4), indicating end of the season). Rainy days have a strong positive relationship (r=0.72) with maize yield, whereas total rainfall and rainfall cessation have moderately negative (r=-0.56) and positive (r=0.58) correlation, respectively. Increase in total rainfall caused a decrease in maize yield. However, increased rainy days, length of growing period and maximum temperature will result to increase in maize yield. Therefore, to minimize the effects of total rainfall, cutoff drain should be considered along the farmland.

scholarly journals 11-Year Rainfall and Maize Yield Variation (2003-2013) in Four Northern Districts of Malawi

2016 ◽  
Vol 8 (5) ◽  
pp. 95
Author(s):  
Naohiro Matsui
Keyword(s):  
Sweet Potato ◽  
Crop Yields ◽  
Smallholder Farmers ◽  
Seasonal Fluctuation ◽  
Critical Factor ◽  
Maize Yield ◽  
Total Rainfall ◽  
Yield Variation ◽  
Cropping Season ◽  
Maize Yields

<p>Rainfall in the maize cropping season (Oct-Apr) in the four northern districts of Malawi was examined in terms of seasonal fluctuation and spatial distribution, and data spanning 11 years were analyzed. Rainfall fluctuations in the 11-year period differed considerably among the four districts and the Extension Planning Areas (EPAs) showed high coefficients of variance (CVs) (16.9-93.7). The equation with the three-month rainfall (October, February, and April), i.e., Maize yield (kg/ha) in SH = 2.29 + 0.0042 × Oct rainfall – 0.0009 × Feb rainfall + 0.00045 × Apr rainfall (r<sup>2</sup> = 0.41), better explained maize yield in the 2013/14 season than the equation with total rainfall in the cropping season. Rainfall accounted for more than 41% of the total variation in maize yields of smallholder farmers (SHs). Rainfall in April was the most critical factor influencing maize and other crop yields. After the Farm Input Subsidy Programme (FISP) was implemented in 2005/06, maize yield became more dependent on rainfall. CV was higher in maize than in groundnut and sweet potato, indicating that maize is susceptible to rainfall fluctuations, and groundnut and sweet potato should be incorporated in farming as a countermeasure against unpredictable rainfall.</p>

scholarly journals Effects of Recent Climate Change on Maize Yield in Southwest Ecuador

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 299
Author(s):  
Gina Lopez ◽  
Thomas Gaiser ◽  
Frank Ewert ◽  
Amit Srivastava
Keyword(s):  
Climate Change ◽  
Negative Impact ◽  
Kendall Test ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Precipitation Trend ◽  
Growing Period ◽  
Recent Climate Change ◽  
Recent Climate ◽  
Semi Arid

In recent years, evidence of recent climate change has been identified in South America, affecting agricultural production negatively. In response to this, our study employs a crop modelling approach to estimate the effects of recent climate change on maize yield in four provinces of Ecuador. One of them belongs to a semi-arid area. The trend analysis of maximum temperature, minimum temperature, precipitation, wind speed, and solar radiation was done for 36 years (from 1984 to 2019) using the Mann–Kendall test. Furthermore, we simulated (using the LINTUL5 model) the counterfactual maize yield under current crop management in the same time-span. During the crop growing period, results show an increasing trend in the temperature in all the four studied provinces. Los Rios and Manabi showed a decreasing trend in radiation, whereas the semi-arid Loja depicted a decreasing precipitation trend. Regarding the effects of climate change on maize yield, the semi-arid province Loja showed a more significant negative impact, followed by Manabi. The yield losses were roughly 40 kg ha−1 and 10 kg ha−1 per year, respectively, when 250 kg N ha−1 is applied. The simulation results showed no effect in Guayas and Los Rios. The length of the crop growing period was significantly different in the period before and after 2002 in all provinces. In conclusion, the recent climate change impact on maize yield differs spatially and is more significant in the semi-arid regions.

scholarly journals Identifying maize yield and precipitation gaps in Uganda

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Terence Epule Epule ◽  
Driss Dhiba ◽  
Daniel Etongo ◽  
Changhui Peng ◽  
Laurent Lepage
Keyword(s):  
Cropping Systems ◽  
Growing Season ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
World Bank Group ◽  
Precipitation Data ◽  
Yield Data ◽  
Yield Gaps ◽  
Maize Yields ◽  
Growing Season Precipitation

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

scholarly journals Impact of Climate Change on Production and Productivity: A Case Study of Maize Research and Development In Nepal

2010 ◽  
Vol 11 ◽  
pp. 59-69 ◽  
Author(s):  
Janak Lal Nayava ◽  
Dil Bahadur Gurung
Keyword(s):  
Climate Change ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Rainfall Distribution ◽  
Maize Production ◽  
Growing Seasons ◽  
Agriculture And Environment ◽  
Present Rate ◽  
Annual Maximum Temperature

The relation between climate and maize production in Nepal was studied for the period 1970/71-2007/08. Due to the topographical differences within north-south span of the country, Nepal has wide variety of climatic condition. About 70 to 90% of the rainfall occurs during summer monsoon (June to September) and the rest of the months are almost dry. Maize is cultivated from March to May depending on the rainfall distribution. Due to the availability of improved seeds, the maize yield has been steadily increasing after 1987/1988. The national area and yield of maize is estimated to be 870,166ha and 2159kg/ha respectively in 2007/08. The present rate of annual increase of temperature is 0.04°C in Nepal. Trends of temperature rise are not uniform throughout Nepal. An increase of annual temperature at Rampur during 1968-2008 was only 0.039°C. However, at Rampur during the maize growing seasons, March/April - May, the trend of annual maximum temperature had not been changed, but during the month of June and July, the trend of increase of maximum temperature was 0.03°C to 0.04°C /year.Key words: Climate-change; Global-warming; Hill; Mountain; Nepal; TaraiThe Journal of AGRICULTURE AND ENVIRONMENT Vol. 11, 2010Page: 59-69Uploaded Date: 15 September, 2010

Relative Sowing Time and Density of Component Crops in a Maize/Cowpea Intercrop System

1987 ◽  
Vol 23 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Francis Ofori ◽  
W. R. Stern
Keyword(s):  
Seed Protein ◽  
Total Yield ◽  
Maize Yield ◽  
Protein Yield ◽  
Sowing Time ◽  
Maize Yields ◽  
Total Seed

SUMMARYThe effect of variations in the relative sowing time and density of component crops in a maize/cowpea intercrop were examined in two experiments. In the first experiment, maize and cowpea were sown together, and either 10 or 21 days before or after each other. Maize yield was reduced when sown at the same time or after cowpea; intercrop cowpea yields were less than sole cowpea yields at all sowings. In the second experiment, maize densities of 35, 50 and 70 × 103plants ha−1were combined with cowpea densities of 70, 100 and 140 × 103plants ha−1. Increasing the density of either crop in the mixture resulted in increases in total yield. Maize reduced cowpea yields more than the effect of cowpea on maize yields. In terms of LER and total seed protein yield, there was no advantage of either staggered sowings over simultaneous sowing or of the various intercrop density combinations, except between the lowest and the highest densities of either maize or cowpea. The LERs appeared to follow the trends in cowpea yields and total seed protein yields the trends in maize yields.

scholarly journals Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Food Security ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 83-103 ◽  
Author(s):  
Banchayehu Tessema Assefa ◽  
Jordan Chamberlin ◽  
Pytrik Reidsma ◽  
João Vasco Silva ◽  
Martin K. van Ittersum
Keyword(s):  
Stochastic Frontier Analysis ◽  
National Level ◽  
Total Yield ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
Yield Gap ◽  
Potential Yield ◽  
Frontier Analysis ◽  
Yield Gaps ◽  
Maize Yields

AbstractEthiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers’ maize yields are still much lower than on-farm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

Maize/Weed Competition Experiments: Implications for Tropical Small-Farm Weed Control Research

1983 ◽  
Vol 19 (4) ◽  
pp. 341-347 ◽  
Author(s):  
R. Vernon ◽  
J. M. H. Parker
Keyword(s):  
Weed Control ◽  
Critical Period ◽  
Maize Yield ◽  
Weed Competition ◽  
Labour Demand ◽  
Small Farms ◽  
Yield Losses ◽  
Small Farm ◽  
Control Research ◽  
Maize Yields

SUMMARYTwo sets of experiments examined the effects of weeds on maize yields using weeding methods typical of small farms in Zambia where oxen are used for cultivation. Maize yield losses of 30% due to weeds were evident with common weeding practices. A critical period of competition, during which the crop should be kept clean, was demonstrated from 10 to 30 days after emergence. This is a period of peak labour demand and the prospect of using chemical weed control to ease the situation is considered. The value of weed competition data, given its variability between sites, is discussed.

Increased Maize Yields through the Transdisciplinary Process with Site Specific Fertilizer Recommendation in Small Holder Farms in Uasin Gishu County, Kenya

2019 ◽  
pp. 1-10
Author(s):  
Arusey Chebet ◽  
Otinga A. Nekesa ◽  
Wilson Ng’etich ◽  
Ruth Njoroge ◽  
Roland W. Scholz ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Grain Yield ◽  
Maize Yield ◽  
Soil Testing ◽  
Site Specific ◽  
Maize Grain Yield ◽  
Significant Difference ◽  
Fertilizer Recommendations ◽  
Maize Yields ◽  
Maize Grain

The objective of this study was to evaluate the effects of site-specific fertilizer recommendations on maize yield using the transdisciplinary (TD) process. 144 farmers participated in the study for the two seasons. Experiments were laid on the farmers’ fields at four sites (Kapyemit, Kipsomba, Ngenyilel and Ziwa, in Uasin Gishu County) using Randomized Complete Block Design in a 3 x 2 factorial arrangement. Treatments included farmers who participated in the TD process (TD2) and those who did not (TD1) in using the interventions for soil fertility improvement which were farmer own practices (ST1); farmers who applied government recommendations (ST2), and site-specific fertilizer recommendations (ST3) which was based on soil testing results. The Data collected was the dry weights of maize which were measured at the end of the seasons and subjected to Analysis of Variance using Genstat 14th edition. Means separation was done using Fischer’s unprotected Least Significant Difference.. There was a significant effect on maize yields by soil testing and participation in TD process p = 0.01. The mean maize grain yield for season one was 5.43 ton ha-1 while for season two was 5.73 ton ha-1. Control farmers (TD1) maize grain yield of 5.27 ton ha-1, had a significant difference (p = 0.05) from the yield of participating farmers (TD2) who had 5.96 ton ha-1. Maize grain yield was increased by the application of site specific fertilizer recommendations which gave an overall mean of 6.57 ton ha-1 for season one and 6.56 ton ha-1 for season two. Following (ST3) recommendations and participation in the TD process, improved soil nutrient content thus maize yield increased. We recommend soil testing and consequent site-specific fertilizer recommendations for any initiative in managing soil fertility.

Irrigated pasture productivity and its variability in the Shepparton region of northern Victoria

1983 ◽  
Vol 23 (121) ◽  
pp. 131 ◽  
Author(s):  
CR Stockdale
Keyword(s):  
Environmental Factors ◽  
Growth Curves ◽  
Subterranean Clover ◽  
Trifolium Subterraneum ◽  
Maximum Temperature ◽  
Lolium Rigidum ◽  
Growing Period ◽  
Wimmera Ryegrass ◽  
Maximum Temperatures ◽  
Pasture Productivity

The seasonal distribution and variability of growth of three types of irrigated pastures were measured at Kyabram over a period of up to seven years. The pasture types studied were (1) paspalum (Paspalum dilatatum)-dominant perennial pasture, (2) ryegrass (Lolium perenne)/clover (Trifolium repens) perennial pasture, and (3) annual pasture based on subterranean clover (Trifolium subterraneum) and Wimmera ryegrass (Lolium rigidum). The influence of environmental factors on the year-to-year variability in monthly growth rates was also examined. Annual growth curves were constructed for each pasture type, and examination of the variability about each monthly mean indicated that the spring months, and October in particular, were the most variable months for pasture growth. Environmental factors were found to account for part of the year-to-year variation in pasture growth of paspalum pastures in August, September, October, November and April. Higher mean maximum temperatures significantly increased growth in September, October and April, with the greatest response occurring in October; hours of sunshine was the significant factor influencing growth in August and November. Annual pasture growth also responded to changes in mean maximum temperature or hours of sunshine in September and October. The comparative mean annual production of paspalum pasture, ryegrass/clover pasture and annual pasture was 18.3, 18.3 and 11.0 t DM/ha, respectively. These levels of production represented 1.1, 1.2 and 1.6% conversion of photosynthetically active radiation during the growing period of the three pasture types, respectively. These levels of productivity and the animal production that should result, suggest that the pasture productivity on many irrigated dairy farms is either very low or the pasture that is grown is inefficiently utilized. Because animal productivity depends on pasture productivity more than any other single factor, farmers should make improvement of pasture growth their major aim while having regard for the variability in growth that can result from variations in environmental factors.

scholarly journals Spatial Coherence of Monsoon Onset over Western and Central Sahel (1950–2000)

2009 ◽  
Vol 22 (5) ◽  
pp. 1313-1324 ◽  
Author(s):  
Romain Marteau ◽  
Vincent Moron ◽  
Nathalie Philippon
Keyword(s):  
Burkina Faso ◽  
Interannual Variability ◽  
Large Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Local Scale ◽  
Monsoon Onset ◽  
Onset Date ◽  
Dry Spell

Abstract The spatial coherence of boreal monsoon onset over the western and central Sahel (Senegal, Mali, Burkina Faso) is studied through the analysis of daily rainfall data for 103 stations from 1950 to 2000. Onset date is defined using a local agronomic definition, that is, the first wet day (&gt;1 mm) of 1 or 2 consecutive days receiving at least 20 mm without a 7-day dry spell receiving less than 5 mm in the following 20 days. Changing either the length or the amplitude of the initial wet spell, or both, or the length of the following dry spell modifies the long-term mean of local-scale onset date but has only a weak impact either on its interannual variability or its spatial coherence. Onset date exhibits a seasonal progression from southern Burkina Faso (mid-May) to northwestern Senegal and Saharian edges (early August). Interannual variability of the local-scale onset date does not seem to be strongly spatially coherent. The amount of common or covariant signal across the stations is far weaker than the interstation noise at the interannual time scale. In particular, a systematic spatially consistent advance or delay of the onset is hardly observed across the whole western and central Sahel. In consequence, the seasonal predictability of local-scale onset over the western and central Sahel associated, for example, with large-scale sea surface temperatures, is, at best, weak.

Sign in / Sign up

Export Citation Format

Share Document