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1
Ologeh, I., and F. Adesina. "Evaluation of climate change as a major determinant of crop yield improvement in Nigeria." IOP Conference Series: Earth and Environmental Science 1077, no. 1 (September 1, 2022): 012002. http://dx.doi.org/10.1088/1755-1315/1077/1/012002.
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Abstract Climate change has adversely affected agricultural productivity leading to decline in food production. The influence of climate change on crops and livestock persists despite irrigation, improved plant and animal hybrids. The continued dependence of agricultural production on climatic factors and the relative dependence of human existence on agricultural products create the need for a comprehensive consideration of the relationship between climate and crop production. This study measured the relationship between annual maize/yam yield as dependent variable and seasonal rainfall as independent variables in four states in Nigeria. It has been proven in the past that yearly rainfall value has no influence on annual crop yield, but seasonal or monthly rainfall does. There is a positive and significant relationship between summed up rainfalls of June/July/August and annual maize yields for the thirty-five years under study. The bi-monthly rainfall values did not influence a major part of total annual maize yield, as it records weak relationship with annual maize yield. On the other hand, bi-monthly rainfall values (May/June and July/August) have positive and significant relationship with annual yam yield. The first quarter- March/April/May rainfall values for each of the states have a positive and significant relationship with annual yam yield. This implies that the rainfall value for this quarter is very essential for annual yam yield for each of the states.
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Stephens, D. J., and T. J. Lyons. "Rainfall-yield relationships across the Australian wheatbelt." Australian Journal of Agricultural Research 49, no. 2 (1998): 211. http://dx.doi.org/10.1071/a96139.
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A network of rainfall stations was selected across the Australian wheatbelt and monthly rainfall regressed with wheat yields from the surrounding shires for the period 1976-87. Yields were found to be strongly related to fluctuations in total rainfall amount and the seasonal distribution of rainfall through the year. These temporal relationships vary spatially and appear to be regulated by the water-holding capacity of regional soils. Sixteen agrometeorological zones were defined with similar rainfall-yield relationships. In all these, autumn rains that permit an early sowing, and finishing rains after July, are most important for higher yields. As the rainfall distribution becomes more winter-dominant, both crop yield variability and the usefulness of high winter rainfall decreases. Heavy rainfall in the month after sowing can have a negative effect in southern Australia, as plants are more prone to suffer potential yield losses from a wet soil profile. Waterlogging has a large negative effect in the south-west of Western Australia, such that the rainfall distribution can be more important than the rainfall amount. Rainfall-yield correlations are generally more positive in drier regions, and are enhanced by persistent rainfall anomalies between April and November during El Niño Southern Oscillation years.
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CHOWDHURY, A., and H. P. DAS. "Effect of rainfall on growth and yield of rice in Konkan coast." MAUSAM 44, no. 2 (January 1, 2022): 143–46. http://dx.doi.org/10.54302/mausam.v44i2.3811.
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In this Study, relationship between rainfall and rice yield has been investigated in Ratnagiri and Thane districts of Maharashtra using 37 year's (1951.1987) data. Weekly water balance has been worked out in developing a yield index and its association with yield examined. Planting rates have been calculated assuming two categories of empirical rainfall accumulations of 450 and 500 mm. The yields were correlated with rainfall during selected growth phases, total rainfall, the yield index and the technological trend. Effects of late or early onset on yield has also been examined.
It appears that 450 mm rainfall accumulation from Ist June gives a ~reasonably accurate indication of transplanting paddy in Konkan. Technology seems.. to have much influence on the paddy yield. Results also reveal that rainfall during early growth phases is significantly related to the yield the date of planting was not found to/have a determining influence on the yield.
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Van Oosterom, E. J., S. Ceccarelli, and J. M. Peacock. "Yield response of barley to rainfall and temperature in Mediterranean environments." Journal of Agricultural Science 121, no. 3 (December 1993): 307–13. http://dx.doi.org/10.1017/s0021859600085488.
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SUMMARYGrain yield of barley (Hordeum vulgare L.) in northern Syria is limited by water stress and extremes of temperature. The present study compared the grain yield of two barley cultivars, Harmal (spring type, cold-sensitive, early heading) and Arabi Aswad (winter type, cold-tolerant, medium early heading), under varying rainfall and temperature. Grain yield was obtained from three sites in northern Syria for seven seasons (1984/85 to 1990/91), resulting in 18 site × season combinations, here called environments. Multiple regression models, containing one rainfall and one temperature variable, were used to quantify yield responses to environmental fluctuations.Total seasonal rainfall was the variable most strongly correlated with the grain yield of Harmal, accounting for 62·8% of the variance. For Arabi Aswad, rainfall from November to January gave the best fit, accounting for 61·8% of the variance. December and January rainfall had the highest contribution to the yield of both cultivars; the contribution of March rainfall tended to be negative. The overall yield response to seasonal rainfall was 11·89 kg/ha/mm for Harmal and 8·57 kg/ha/mm for Arabi Aswad; the expected grain yield at the driest site was c. 1270 kg/ha for both cultivars. The addition of a temperature variable gave a better fit, accounting for c. 80% of the variance in grain yield for both cultivars if winter rainfall was combined with number of night frosts in spring. It reduced the expected yields at the driest site to c. 986 kg/ha. Arabi Aswad had a lesser response to both rainfall and frost than Harmal.In environments where low yields are due to both water and temperature stress, farmers are advised to grow Arabi Aswad because its lesser sensitivity to environmental fluctuations will ensure a better yield stability.
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MacColl, D. "Studies on Maize (Zea mays) at Bunda, Malawi. I. Yield in Relation to Rainfall." Experimental Agriculture 25, no. 3 (July 1989): 357–66. http://dx.doi.org/10.1017/s0014479700014873.
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SUMMARYYields of maize were determined in six years, on two soils, at up to four nitrogen levels following early and late planting. Without fertilizer nitrogen, high rainfall at the beginning of the growing season reduced yield on one soil but not on the other, while high rainfall at late silking and high total rainfall in the growing season reduced yields on both soils. As the level of fertilizer nitrogen increased, the negative effects of rainfall on yield tended to disappear. A three week delay in planting sometimes increased and sometimes decreased yield. The probable reasons for the observed effects of rainfall on yield are discussed.
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Hannah, MC, and GJ O'Leary. "Wheat yield response to rainfall in a long-term multi-rotation experiment in the Victorian Wimmera." Australian Journal of Experimental Agriculture 35, no. 7 (1995): 951. http://dx.doi.org/10.1071/ea9950951.
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Seventy-six years of wheat yield data from a long-term rotation experiment at Dooen in the Victorian Wimmera were analysed to describe the response of wheat yield to seasonal rainfall, crop sequence, and time. Wheat yields from 7 different 1- to 4-course rotations involving wheat, barley, oat, field pea, grass pasture and fallow were compared as a function of growing-season (May-November) rainfall. The field layout had no within-year replication, but each phase of each rotation was represented once in each year. An approximate quadratic response of wheat yield to both current year and previous year May-November rainfall was observed for each rotation. Previous year May-November rainfall boosted wheat yields grown on fallow, but decreased the yield of wheat grown on field pea or wheat stubble. Highest wheat yields followed fallow preceded by pasture, high yields followed fallow preceded by a cereal, moderate yields followed field pea, and low yields occurred for continuous wheat. Long-term trends in wheat yields adjusted for rainfall depended on crop sequence and fluctuated more in the non-fallow, 3-course rotations. Over the 76 years, average yield declined in all rotations except the continuous wheat, which was always low, but there was evidence that yield of all continuous cropped rotations had increased during the last 2 decades.
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Vittal, K. P. R., K. Vijayalakshmi, and U. M. B. Rao. "The Effect of Cumulative Erosion and Rainfall on Sorghum, Pearl Millet and Castor Bean Yields Under Dry Farming Conditions in Andhra Pradesh, India." Experimental Agriculture 26, no. 4 (October 1990): 429–39. http://dx.doi.org/10.1017/s0014479700001356.
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SUMMARYTopsoil depth and yields of sorghum, pearl millet and castor bean were found to be positively correlated when monitored over thirteen seasons under widely varying rainfall conditions on an Alfisol in Andhra Pradesh, India. Yields responded up to 2.5 times more to topsoil depth when rainfall in the critical period exceeded evapotranspiration than under drier conditions. Yield variations were explained by topsoil depth in cereals, and by the interaction between rainfall during the critical period and topsoil depth in cereals and castor bean (R2<0.72). The expected yield losses as a result of erosion, based on 56 years of rainfall data, were 138, 84 and 51 kg ha−1cm−1for sorghum, pearl millet and castor bean, respectively. Quadratic models explained the yield losses due to cumulative erosion under conditions of both poor and good rainfall during the critical period. Sorghum and pearl millet tended to produce no yield when erosion exceeded 34 cm and castor bean when it exceeded 40 cm. There was less reduction in yield when rainfall was poor, up to about 10 cm of erosion, than when rainfall was good. The results indicate that pearl millet and castor bean are more suitable than sorghum for low rainfall and eroded conditions.
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Vittal, K. P. R., K. Vijayalakshmi, and U. M. B. Rao. "The Effect of Cumulative Erosion and Rainfall on Sorghum, Pearl Millet and Castor Bean Yields Under Dry Farming Conditions in Andhra Pradesh, India." Experimental Agriculture 26, no. 4 (October 1990): 429–39. http://dx.doi.org/10.1017/s001447970000363x.
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SUMMARYTopsoil depth and yields of sorghum, pearl millet and castor bean were found to be positively correlated when monitored over thirteen seasons under widely varying rainfall conditions on an Alfisol in Andhra Pradesh, India. Yields responded up to 2.5 times more to topsoil depth when rainfall in the critical period exceeded evapotranspiration than under drier conditions. Yield variations were explained by topsoil depth in cereals, and by the interaction between rainfall during the critical period and topsoil depth in cereals and castor bean (R2<0.72). The expected yield losses as a result of erosion, based on 56 years of rainfall data, were 138, 84 and 51 kg ha−1cm−1for sorghum, pearl millet and castor bean, respectively. Quadratic models explained the yield losses due to cumulative erosion under conditions of both poor and good rainfall during the critical period. Sorghum and pearl millet tended to produce no yield when erosion exceeded 34 cm and castor bean when it exceeded 40 cm. There was less reduction in yield when rainfall was poor, up to about 10 cm of erosion, than when rainfall was good. The results indicate that pearl millet and castor bean are more suitable than sorghum for low rainfall and eroded conditions.
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A, SIVA SANKAR, RANGA REDDY M, NAGARAJA RAO M, MADHAVA SWAMY G, and BRAMHANANDA REDDY P. "INFLUENCE OF RAINFALL AND ITS DISTRIBUTION ON CROP YIELDS OF RAINFED COTTON AND SORGHUM." Madras Agricultural Journal 75, May June (1988): 173–75. http://dx.doi.org/10.29321/maj.10.a02106.
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A Study was carried out to determine the influence of rainfall during the crop: season on the yields of rainfed cotton and sorghum, Rainfall during the crop growth had a significant negative association with Kapas yield (r-0.56%, in an early ameri can cotton var. NA 247 (Gossypium hirsutum L.) while it had little influence on late variety Laxmi. Among different months, rainfall and number of rainy days in September month alone had a strong negative effect on kapas yields in American cotton var. NA 247. There was no clear response to rainfall for kapas yield in rainfed desi cotton (Gossypium arboreum L.) varieties viz, Pandaripur Mungari and Srisailam. In Sorghum no distinct relationship exists between rainfall and grain yield of CSH-1, CSH-5, and N-13. However, stover yields were significantly correlated with rainfall during crop growth in hybrids CSH-1 and CSH-5.
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Westcott, Nancy E., Steven E. Hollinger, and Kenneth E. Kunkel. "Use of Real-Time Multisensor Data to Assess the Relationship of Normalized Corn Yield with Monthly Rainfall and Heat Stress across the Central United States." Journal of Applied Meteorology 44, no. 11 (November 1, 2005): 1667–76. http://dx.doi.org/10.1175/jam2303.1.
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Abstract This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997–99 and 2001–02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50–100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days > 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.
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Jlibene, Mohammed. "Partition of rainfall variation effect on crop productivity into amount and distribution effects." E3S Web of Conferences 183 (2020): 03005. http://dx.doi.org/10.1051/e3sconf/202018303005.
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Rainfall is the most influential factor of rainfed agricultural production in semi-arid Mediterranean and Sub-Saharan environments. While effects of rainfall amount and distribution are confounded, their partition will help developing strategies to manage green water in these environments. A series of 39 years of country average cereal yield and rainfall data was used (1981-2019). Cumulated rainfall over months of November, Rainfall of the month of January, February and March of Central location of Marchouch (33.5613° N, 6.6919° W) taken as a representative of agricultural land, were used because of high correlations with yield. Regression of cereal yields on rainfall showed scattered points below a top frontier line representing highest water productivity (yield/rainfall), accounting for effect of amount independent from distribution. Regression of all 39 yields on rainfall accounts for both confounded effects of amount and distribution. Difference indicates effectofdistribution of rainfall. Cumulated rainfall over the 4 months, varied from 64.20 mm to 447 mm with an average of 219 mm. Yields ranged from 3.59 q/ha to 22.88 q/ha with an average of 12.29 q/ha. Top water productivity indicating effect of amount was 3.98 kg/mm. Distribution of rainfall reduced actual yields by 13% to50% depending on season and cereal crop. The model can be adapted to rainfed crops in semiarid environments.
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Njouenwet, Ibrahim, Derbetini Appolinaire Vondou, Stephanie Vanessa Ngono Ashu, and Robert Nouayou. "Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields." Sustainability 13, no. 21 (November 2, 2021): 12086. http://dx.doi.org/10.3390/su132112086.
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Cotton yields in the Sudano-Sahelian region contribute to food security through their role in agricultural productivity. Daily precipitation data and cotton yield data were synthesized from nine agricultural regions obtained from the “Société de Développement du Coton (SODECOTON)”. The following seasonal rainfall indices—from Cameroon’s cotton zone—were mapped with geographic information systems for spatial analysis: wet season onset and retreat date, rainfall amount, number of rainy days, rainfall intensity (SDII), heavy-rainfall events (R95p), consecutive dry days (CDD), annual highest daily precipitation (Rx1day) and number of very heavy precipitation days (R20mm). Linear regressions were used as statistical tools for analysis. The strongest relationships were observed between cotton yields and the heavy-rainfall events, closely followed by seasonal rainfall amount. An increase in consecutive dry days (CDD) and heavy events, and a decreased seasonal rainfall amount, have a negative impact on cotton yield trends. Overall, the critical breakpoint analysis between cotton yields and all rainfall indices showed that the cotton yield was particularly negatively impacted before a 251 retreat date, 591 mm seasonal rainfall amount and 33 rainy days. By contrast, an onset date, rainfall intensity, heavy rainfall, CDD, Rx1day and R20mm of 127, 12.5 mm·day−1, 405 mm, 27 days, 67 mm and 22 days, respectively, were identified for an optimum cotton yield. These results can be used as information for agricultural activity and management, civil planning of economic activities and can also contribute to furthering our understanding of the management impacts on future food security.
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Oliver, Y. M., M. J. Robertson, P. J. Stone, and A. Whitbread. "Improving estimates of water-limited yield of wheat by accounting for soil type and within-season rainfall." Crop and Pasture Science 60, no. 12 (2009): 1137. http://dx.doi.org/10.1071/cp09122.
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Rainfall is the main driver of potential yield in the dryland cropping environment of Australia. Rainfall-based empirically derived models such as that proposed by French and Schultz (1984) (F&S) can be used to determine an upper limit of water-limited potential yield, but F&S often overestimates actual yield as it does not account for rainfall distribution, runoff, drainage, or stored soil water. More complex models are available to predict potential yield more accurately; however, farmers and advisors favour easy-to-use-methods to estimate potential yield. To derive a simple yet accurate method for estimating potential yield, several adjustments to F&S were evaluated: (1) accounting for stored soil water at sowing, (2) varying the value of the intercept between yield and growing-season rainfall (GSR), (3) varying the water-use efficiency of the crops (WUE) according to soil type, and (4) adjustments to GSR depending on soil plant-available water capacity (PAWC). The water-limited potential yields predicted from these methods were compared with simulations from the daily time-step simulation model APSIM and observed wheat yields from 146 dryland wheat crops, managed to water-limited potential yield, covering the 1996–2006 seasons in the Mediterranean-type growing environments of Australia. The original F&S method overestimated actual yields, particularly at high rainfall (GSR > 220 mm) when PAWC was low, and underestimated yields at low rainfall (GSR < 220 mm). Significant improvements to the F&S were achieved with a few simple adjustments. With the addition of a variable intercept (dependent upon GSR), accounting for stored soil water at the start of the season and placing a cap on GSR that is a function of the soil PAWC, the predictive performance (RMSE 624 kg/ha) was similar to that gained with the daily time-step model APSIM (RMSE 419 kg/ha). The improved method gave more realistic estimates of water-limited potential yield, particularly at low and high rainfall and on soils of low PAWC.
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Márton, L. "Fertilisation, rainfall and crop yield." Acta Agronomica Hungarica 52, no. 2 (August 1, 2004): 165–72. http://dx.doi.org/10.1556/aagr.52.2004.2.7.
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The effect of rainfall quantity and distribution and of N, P, K, Ca and Mg fertilisation on the yields of rye, potato, winter wheat and triticale were evaluated in the 42 years of a long-term mineral fertilisation experiment [soil (acidic, sandy, brown forest) × fertilisation (N, P, K, Ca, Mg) × rainfall (quantity, distribution) × crop (rye, potato, winter wheat, triticale)] set up in 1962 under fragile agro-ecological conditions in the Nyírlugos-Nyírség region of Eastern Hungary. The soil had the following agrochemical characteristics: pH (H2O) 5.9, pH (KCl) 4.7, hydrolytic acidity 8.4, hy1 0.3, humus 0.7%, total N 34 mg kg-1, ammonium lactate (AL)-soluble P2O5 43 mg kg-1, AL-K2O 60 mg kg-1 in the ploughed layer. From 1962 to 1980 the experiment consisted of 2×16×4×4=512 plots and from 1980 of 32×4=128 plots in split-split-plot and factorial random block designs. The gross plot size was 10×5=50 m2. The average fertiliser rates in kg ha-1 year-1 were nitrogen 45, phosphorus 24 (P2O5), potassium 40 (K2O), magnesium 7.5 (MgO) until 1980 and nitrogen 75, phosphorus 90 (P2O5), potassium 90 (K2O), magnesium 140 (MgCO3) after 1980. The main results and conclusions were as follows: The rainfall quantities averaged over many years and in the experimental years, and during the growing season, averaged over many years and in the experimental years, were 567, 497, 509, 452 mm for rye and 586, 509, 518 and 467 mm for winter wheat. Rainfall deviations from the many years' average -3% and -13% in the experimental years and during the growing season for potato and 2% and -3% for triticale. During the vegetation period the relationships between rainfall quantity, NPKCaMg nutrition and yield could be characterised primarily by quadratic correlations. Maximum yields of 4.0 t ha-1 for rye, 21.0 t ha-1 for potato, 3.4 t ha-1 for winter wheat and 5.0-6.0 t ha-1 for triticale were recorded when the natural rainfall amounted to 430-500, 280-330, 449-495 and 550-600 mm, respectively. At values above and below these figures there was a considerable reduction in the yield. The results showed that the crop yields were strongly influenced (quadratic correlation) by interactions between N, P, K, Ca and Mg fertilisation and rainfall quantity and distribution.
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Matsui, Naohiro. "11-Year Rainfall and Maize Yield Variation (2003-2013) in Four Northern Districts of Malawi." Journal of Agricultural Science 8, no. 5 (April 13, 2016): 95. http://dx.doi.org/10.5539/jas.v8n5p95.
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<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>
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Peterson, Todd Andrews, Charles A. Shapiro, and A. Dale Flowerday. "Rainfall and previous crop effects on crop yields." American Journal of Alternative Agriculture 5, no. 1 (March 1990): 33–37. http://dx.doi.org/10.1017/s0889189300003209.
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AbstractAfield study was conducted between 1972 and 1982 to compare the effects of previous crop on row crop yields under rainfed conditions in eastern Nebraska. The objectives were to determine the effects of fallow and three previous crops: corn (Lea. maysLJ, soybeans /Glycine max (L.) Mem], and grain sorghum /Sorghum bicolor (L.) Moench], on the growth and grain yield of the same crops. The study was conducted on a Sharpsburg silty clay loam (fine, montmorillonitic, mesicf Typic Argiudoll). Corn grain yield was most variable (C. V. 23.4percent) compared to soybean (C. V. 13.6percent) or grain sorghum (C. V. 9.5 percent) yields. Corn was also the most sensitive crop to previous crop effects. The range of treatment yields for each crop was 47 percent, 22 percent, and 11 percent of the overall means for corn, soybean, and sorghum, respectively. Previous crop affected yields for all crops, but the effects were not consistent across years. All crops produced highest yield following fallow. Yields of corn, soybean, and grain sorghum following fallow were 74, 25, and 10 percent higher than their respective monoculture yields. In years of average precipitation, a corn-soybean sequence produced the greatest yield. In years having above- or below-normal precipitation, a grain sorghum-soybean sequence produced the highest yield.
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Anwar, M. R., D. Rodriguez, D. L. Liu, S. Power, and G. J. O'Leary. "Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia." Australian Journal of Agricultural Research 59, no. 2 (2008): 112. http://dx.doi.org/10.1071/ar07061.
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Reliable seasonal climate forecasts are needed to aid tactical crop management decisions in south-eastern Australia (SEA). In this study we assessed the quality of two existing forecasting systems, i.e. the five phases of the Southern Oscillation Index (SOI) and a three phase Pacific Ocean sea-surface temperatures (SSTs), to predict spring rainfall (i.e. rainfall from 1 September to 31 November), and simulated wheat yield. The quality of the forecasts was evaluated by analysing four attributes of their performance: their reliability, the relative degree of shift and dispersion of the distributions, and measure of forecast consistency or skill. Available data included 117 years of spring rainfall and 104 years of grain yield simulated using the Agricultural Production Systems Simulator (APSIM) model, from four locations in SEA. Average values of spring rainfall were 102–174 mm with a coefficient of variation (CV) of 47%. Average simulated wheat yields were highest (5609 kg/ha) in Albury (New South Wales) and lowest (1668 kg/ha) in Birchip (Victoria). The average CV for simulated grain yields was 36%. Griffith (NSW) had the highest yield variability (CV = 50%). Some of this year-to-year variation was related to the El Niño Southern Oscillation (ENSO). Spring rainfall and simulated wheat yields showed a clear association with the SOI and SST phases at the end of July. Important variations in shift and dispersion in spring rainfall and simulated wheat yields were observed across the studied locations. The forecasts showed good reliability, indicating that both forecasting systems could be used with confidence to forecast spring rainfall or wheat yield as early as the end of July. The consistency of the forecast of spring rainfall and simulated wheat yield was 60–83%. We concluded that adequate forecasts of spring rainfall and grain yield could be produced at the end of July, using both the SOI and SST phase systems. These results are discussed in relation to the potential benefit of making tactical top-dress applications of nitrogen fertilisers during early August.
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Awala, Simon Kamwele, Kudakwashe Hove, Johanna Shekupe Valombola, Helena Nalitende Nafuka, Evans Kamwi Simasiku, Barthlomew Chataika, Lydia Ndinelao Horn, Simon Angombe, Levi S. M. Akundabweni, and Osmund D. Mwandemele. "Co-Cultivation and Matching of Early- and Late-Maturing Pearl Millet Varieties to Sowing Windows Can Enhance Climate-Change Adaptation in Semi-Arid Sub-Saharan Agroecosystems." Climate 11, no. 11 (November 10, 2023): 227. http://dx.doi.org/10.3390/cli11110227.
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In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia’s semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia—Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain-yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR to be from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers’ climate adaptation. Policymakers for semi-arid agricultural regions could utilize this information to adjust local seed systems and extension strategies.
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Atar, Bekir. "Determination and Assessments the Yield Gap Between the Wheat Yield and Potential Yield in Turkey." Turkish Journal of Agriculture - Food Science and Technology 6, no. 10 (October 1, 2018): 1339. http://dx.doi.org/10.24925/turjaf.v6i10.1339-1346.1825.
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Knowing the current and the potential production amount of wheat is essential to meet the growing needs. Yield is determined by many factors. The main factors that limit the potential yield are genetic characteristics, and climatic factors such as rainfall and radiation, and management. Wheat is mostly grown in non-irrigated areas in Turkey. The most significant factors that limit dry agriculture wheat production yield in the Mediterranean climatic type are rainfall and its distribution in the growing season. A steady increase in yield is observed in Turkey in recent years. Average annual wheat production is 20.6 million tons. The potential production in this work is determined as 54 million tons. The gap is about 33 million ton. The actual production is 39% of the potential production. The average yield of the Growing Season Rainfall (GSRF) 500 mm areas is 2.2 t ha-1, and the potential yield is 8.8 t ha-1. The gap between the actual yield and potential yield is quite large. The current yield between the areas (GSRF 500 mm) is very small. So it is difficult to explain the gap just because of the rainfall.
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Cetin, Oner, Mehmet Yildirim, Cuma Akinci, and Anna Yarosh. "Critical Threshold Temperatures and Rainfall in Declining Grain Yield of Durum Wheat (Triticum Durum Desf.) During Crop Development Stages." Romanian Agricultural Research 39 (2022): 247–57. http://dx.doi.org/10.59665/rar3924.
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The effects of maximum temperature, rainfall and growing degree day on grain yield of durum wheat (Triticum durum Desf.) were studied during the period of available years (2005-2017) in the study sites. A polynomial equation was described the relationship between grain yield, maximum temperature and rainfall for four growing stages of winter wheat. The nonlinear relationships were used from time-series variations in temperatures, rainfall and yields. The maximum positive effects of rainfall (R2=0.72*) on grain yield was in the mid-season stage (heading, anthesis and grain filling) of the crop. However, there was a negative effect of temperature more than 30°C on grain yield. The highest negative effects (R2=0.31 through 0.86*) of maximum temperatures were in the crop development stages (vernalization and tillering). The yield might decrease about 2.5% for every 1°C increase in the growth period based on the daily mean temperature of 12.4°C for all the study locations. The critical maximum temperatures on threshold values declining yield and positive effects of rainfall on grain yield varied according to the altitudes and longitudes.
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Tiamiyu, Saliu Akinlabi, Jude N. Eze, Taibat Moji Yusuf, Alhassan Tsako Maji, and Samuel Oladele Bakare. "Rainfall Variability and its Effect on Yield of Rice in Nigeria." International Letters of Natural Sciences 49 (November 2015): 63–68. http://dx.doi.org/10.18052/www.scipress.com/ilns.49.63.
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Nigerian rice production is mainly rain-fed and naturally prone to vagaries of rainfall variability. The objective of this study was to determine the probable effect of rainfall variability on average yield of rice in Nigeria during a 22-year period (1992-2013). The mean annual rainfall data from major rice producing states and national average rice yields were analyzed using descriptive statistics and regression models. Results of the analysis revealed a significant variation in mean annual rainfall across vegetation grouping in a descending order from swamp forest to Sudan savanna. Mean rainfall was adequate for rice production in all vegetation grouping except Sudan savanna where rainfall was less than the prescribed minimum quantity for rice production. Rainfall was positively related to yield of rice in all vegetation grouping except Sudan savanna but relationship was not statistically significant at 5% level. It was concluded that annual rainfall variability had statistically negligible effect on national average rice yield. It was recommended that the cultivation of rice should go along with farming practices that aimed at mitigating adverse effects of rainfall variability on the yield of rice especially in the Sudan savanna.
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Tiamiyu, Saliu Akinlabi, Jude N. Eze, Taibat Moji Yusuf, Alhassan Tsako Maji, and Samuel Oladele Bakare. "Rainfall Variability and its Effect on Yield of Rice in Nigeria." International Letters of Natural Sciences 49 (November 24, 2015): 63–68. http://dx.doi.org/10.56431/p-yk8v53.
Full textAbstract:
Nigerian rice production is mainly rain-fed and naturally prone to vagaries of rainfall variability. The objective of this study was to determine the probable effect of rainfall variability on average yield of rice in Nigeria during a 22-year period (1992-2013). The mean annual rainfall data from major rice producing states and national average rice yields were analyzed using descriptive statistics and regression models. Results of the analysis revealed a significant variation in mean annual rainfall across vegetation grouping in a descending order from swamp forest to Sudan savanna. Mean rainfall was adequate for rice production in all vegetation grouping except Sudan savanna where rainfall was less than the prescribed minimum quantity for rice production. Rainfall was positively related to yield of rice in all vegetation grouping except Sudan savanna but relationship was not statistically significant at 5% level. It was concluded that annual rainfall variability had statistically negligible effect on national average rice yield. It was recommended that the cultivation of rice should go along with farming practices that aimed at mitigating adverse effects of rainfall variability on the yield of rice especially in the Sudan savanna.
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Erskine, W., and F. El Ashkar. "Rainfall and temperature effects on lentil (Lens culinaris) seed yield in Mediterranean environments." Journal of Agricultural Science 121, no. 3 (December 1993): 347–54. http://dx.doi.org/10.1017/s0021859600085543.
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SUMMARYLentil (Lens culinaris Medikus) is a rain-fed crop in North Africa and West Asia whose yields are limited by the amount and distribution of rainfall and winter cold. This study aimed to quantify the effects of climatic variables on lentil seed yields through the fitting of simple empirical models to trial data of two cultivars sown at six sites, 1983–89, representing 31 environments in Syria. The ranges over environments were 152–527 mm for total season rainfall and 1–52 for the number of frost nights. Overall, the total seasonal rainfall accounted for 40·8% of the variance in mean seed yield (1·27 t//ha, S.D. 0·82) with a response of 5·68 kg/ha/mm. A multiple regression model with monthly rainfall from November to May explained 67·6;0% of the variance in mean seed yield. From November to February the response of seed yield to rain was < 10 kg/ha/mm; rain in March, the period of late vegetative growth, made the most important contribution to seed yield. The response to April rain was negative. At Tel Hadya (the most frequently used site), the total seasonal rainfall accounted for 79·8% of the variance in mean seed yield, and the addition of the number of frost nights to the model improved the fit to 92·7%. Winter cold had a smaller effect on yield than rainfall, with no consistent overall effect, but differences over regions. The cultivars contrasted in their responses to drought (78S26002 was superior to ILL4400 at seasonal rainfall levels down to 134 mm) and the number of frost nights at Breda and Tel Hadya (78S26002 was more susceptible to cold than ILL4400). Thus, despite the predominant influence of rainfall on yield, the genetic variability in response to moisture and cold shows the scope for selection under rainfed Mediterranean environments.
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McDonald, GK. "Grain yields of field pea (Pisum sativum L.) in South Australia." Australian Journal of Experimental Agriculture 35, no. 4 (1995): 515. http://dx.doi.org/10.1071/ea9950515.
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The grain yield of field pea (Pisum sativum L.) between 1959-60 and 1991-92 was examined in selected Hundreds in important peagrowing regions of South Australia. Over the 33 years, the rates of increase in grain yield have been substantial, ranging from 20 to 48 kg/ha.year. The rate of increase in the State average for the same period was 22 kg/ha. year. The largest rates of increase have occurred mainly in the Hundreds in the higher rainfall areas. Yields have increased irregularly. During the 1960s grain yields rose relatively slowly, but from the mid 1970s to the mid 1980s, large increases occurred. Since then, yields have increased relatively little or, in some Hundreds, declined. With one exception, grain yield was positively and significantly correlated with seasonal (April-October) rainfall in each Hundred, but there were few significant correlations with rainfall in individual months. Yield was often correlated with winter and autumn rainfall but not with spring rainfall. The efficiencies of seasonal water use in the Hundreds ranged from 2.7 to 4.8 kg/ha.mm; these were lower than the maximum values recorded for other winter grain legumes, suggesting that water use efficiencies can improve substantially.
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Jones, M. J. "Plant Population, Rainfall and Sorghum Production in Botswana. I. Results of Experiment Station Trials." Experimental Agriculture 23, no. 3 (July 1987): 335–47. http://dx.doi.org/10.1017/s0014479700017221.
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SUMMARYGeneral mathematical relations between yield parameters, plant populations and rainfall were developed for an indigenous sorghum from the results of 28 population/row spacing trials conducted at four sites over five seasons. Populations maximizing yield increased from 25 000 to 69 000 plants ha−1 over the rainfall range 200–700 mm (pre-planting to harvest total). Tillering partly compensated for low populations but yields from 10000 plants ha−1 at 300 and 600 mm rainfall were only 80 and 61% of potential maximum, respectively. Row spacing at constant population affected tiller numbers and eventual panicle weights but not panicle numbers, and any yield differences were unrelated to rainfall.
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Reddy, B. Sahadeva, K. Ashok Kumar, K. V. S. Sudheer, Anuhya Pola, P.Radhika, Y. Pavan Kumar Reddy, and G. R. Maruthi Sankar. "Sustainability of Pod Yields of Groundnut through Crop Seasonal Rainfall, Length of Growing Period and Cultivated Area under Arid Alfisols of Anantapur in South India." Asian Journal of Applied Science and Technology 07, no. 01 (2023): 80–104. http://dx.doi.org/10.38177/ajast.2023.7108.
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A study was conducted with the objective of assessing the effect of crop seasonal rainfall and length of growing period on the sustainability of pod yields of groundnut attained in 31 mandals under arid Alfisols of Anantapur in Andhra Pradesh. We have considered the variability of mandals with regard to (i) crop seasonal rainfall (mm) and (ii) pod yield of groundnut (kg/ha) during 2001 to 2020; (iii) extent of crop area (ha) during 2009 to 2020; and (iv) length of growing period (days). Based on the mean and standard deviation (SD) of each parameter, the mandals were classified into 5 groups viz., (i) G1: Less than (Mean–2SD); (ii) G2: (Mean–2SD) to (Mean–SD); (iii) G3: (Mean–SD) to (Mean+SD); (iv) G4: (Mean+SD) to (Mean+2SD); and (v) G5: More than (Mean+2SD). Out of 31 mandals, 22 mandals for area and crop seasonal rainfall, 20 mandals for LGP and 18 mandals for yield have fallen in G3. Estimates of correlation were derived between groundnut area, crop seasonal rainfall and yield for each mandal over years and tested for significance to assess the superiority of mandals. Significant correlation of yield and crop seasonal rainfall was observed which ranged from 0.433 at Kalyandurg to 0.765 at Putlur. Similarly, significant correlation between yield and area of groundnut was observed in Kalyandurg (-0.764), Brahmasamudram (-0.674) and Rapthadu (-0.584) mandals. The predictability of yield and prediction error were derived based on a regression model of yield calibrated through the crop seasonal rainfall, LGP and crop area in different mandals. The model gave significant predictability (R2 ) value of 0.46 with prediction error of 90.9 kg/ha and indicated negative effect of area, positive effect of crop seasonal rainfall and LGP on yield. The sustainability yield index ranged from 26.6% (Kambadur) to 87.5% (Peddavadagur) with mean of 53.9% (CV of 30.1%) over years. Ranks were assigned to the mean and variation of area, crop seasonal rainfall, yield, LGP and SYI of each mandal and rank sums were derived. Guntakal, Gooty and Vidapanakal were superior with rank sums of 30, 38 and 70 respectively. Guntakal was superior with an area of 16570 ha (CV of 17.3%), crop seasonal rainfall of 436.1 mm (CV of 33.4%), LGP of 140 days, yield of 644 kg/ha (CV of 70.9%) and SYI of 76.5%, while Gooty was superior with area of 14146 ha (CV of 14.6%), crop seasonal rainfall of 429.6 mm (CV of 42.4%), LGP of 140 days, yield of 663 kg/ha (CV of 69.1%) and SYI of 79.1%. Similarly, Vidapanakal was superior with area of 5077 ha (CV of 31.1%), crop seasonal rainfall of 403.2 mm (CV of 47.4%), LGP of 140 days, yield of 654 kg/ha (CV of 49.5%) and SYI of 77.9%. Due to maximum LGP and crop seasonal rainfall, we recommend that the farmers of these mandals could enhance the area of groundnut and attain maximum sustainable yields under arid Alfisols.
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Tafoughalti, K., E. M. El Faleh, Y. Moujahid, and F. Ouargaga. "Climate Change Impact on Rainfall: How will Threaten Wheat Yield?" E3S Web of Conferences 37 (2018): 03001. http://dx.doi.org/10.1051/e3sconf/20183703001.
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Climate change has a significant impact on the environmental condition of the agricultural region. Meknes has an agrarian economy and wheat production is of paramount importance. As most arable area are under rainfed system, Meknes is one of the sensitive regions to rainfall variability and consequently to climate change. Therefore, the use of changes in rainfall is vital for detecting the influence of climate system on agricultural productivity. This article identifies rainfall temporal variability and its impact on wheat yields. We used monthly rainfall records for three decades and wheat yields records of fifteen years. Rainfall variability is assessed utilizing the precipitation concentration index and the variation coefficient. The association between wheat yields and cumulative rainfall amounts of different scales was calculated based on a regression model. The analysis shown moderate seasonal and irregular annual rainfall distribution. Yields fluctuated from 210 to 4500 Kg/ha with 52% of coefficient of variation. The correlation results shows that wheat yields are strongly correlated with rainfall of the period January to March. This investigation concluded that climate change is altering wheat yield and it is crucial to adept the necessary adaptation to challenge the risk.
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Piha, M. I. "Optimizing Fertilizer Use and Practical Rainfall Capture in a Semi-Arid Environment with Variable Rainfall." Experimental Agriculture 29, no. 4 (October 1993): 405–15. http://dx.doi.org/10.1017/s0014479700021128.
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SummaryFertilizer use in the semi-arid tropics is considered risky because of the unreliable rainfall pattern. A flexible system of fertilization was devised which allowed for high yields in wet years, and reduced financial losses in dry years. Relatively immobile nutrients, such as phosphorus, potassium and sulphur, were applied at rates determined from generalized soil properties and the total nutrient uptake required for a crop to achieve its maximum yield potential in a season with average rainfall. Nitrogen was applied as a series of split applications, which were adjusted during the season according to the degree of water stress observed. This approach was tested in on-farm trials for maize production on nutrient-poor sandy soils in three regions of Zimbabwe, and resulted in larger yields and profits than current recommendations, providing an accept able level of financial return. Small further yield increases were possible, at acceptable returns, when these fertilizer practices were combined with the establishment of tied-ridges by ox-drawn implements, 30 days after planting. By careful estimation of yield potential, and appropriate adjustments to soil fertility, this system of soil management could be applied to other cropping situations and other areas with variable rainfall.Uso de fertilizantes para condiciones de precitación variable
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Devkota, Niranjan, and Nirash Paija. "Impact of Climate Change on Paddy Production: Evidence from Nepal." Asian Journal of Agriculture and Development 17, no. 2 (December 1, 2020): 63–78. http://dx.doi.org/10.37801/ajad2020.17.2.4.
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This study assesses the long-run relationship and short-run dynamics between paddy yields and climate variables, particularly maximum and minimum temperature and rainfall, using time-series data from 1971 to 2014 in Nepal. Applying Autoregressive-Distributed Lag Regression or ARDL bounds testing approach for analysis of co-integration between the variables, we confirm that there is a long-run relationship among the variables. Furthermore, we employ Granger non-causality tests for robustness. The findings reveal that rainfall has substantial effects on the rice yield. Specifically, a positive and significant relationship exists between rice yields and rainfall and that this relationship is unidirectional. Rainfall impacts on rice yield and holding all things constant, a 1 mm increase in rainfall increases rice yields by 0.65 percent. Given the effects of temperature on rice crops and increasing climate change vulnerabilities, agricultural scientists should focus on research and development of temperature tolerant rice varieties in the production of rice yields.
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Yu, Ji Lin, Hua Qi, Lin Xue Nie, Wei Jian Zhang, Hong Bing Zheng, Ming Liu, Zhi Qiang Lin, and Ming Chao Gao. "Effects of Environment Variables on Maize Yield and Ear Characters." Advanced Materials Research 726-731 (August 2013): 106–13. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.106.
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The global warming brings important effects to the ecological system, agricultural environment and crop yields. Summer maize variety ZD 958 was planted at different sowing dates and densities. Relationship between yield, ear characters and climatic factors were studied. Results showed that rainfall was the key limiting factor affecting maize production; the effective accumulated temperature after florescence was the important factor affecting bald ear tips and shrunken kernels; rainfall after florescence was the major factor affecting row grains and kernels deep; correlation between yield and average light intensity, rainfall, effective accumulated temperature reached an evident level during growth period. Rainfall had the largest direct effect on yield, and the indirect effect from effective accumulated temperature by average light intensity on yield expression was strong, while indirect effect between effective accumulated temperature and average light intensity was week. Rainfall after florescence had the maximum direct effect, while rainfall and average light intensity affecting on yield decreased significantly, direct effect effective of accumulated temperature increased obviously. 2a trials in 2011 and 2012, early sowing (April 25th) with middle density (67 500 plants·hm-2) had the higher yield, increased 71.28% and 58.88% compared with high density (90 000 plants·hm-2) with late sowing (May 25th) treatments. Interaction between sowing date and density had an significant effect on bald kernels and kernels deep, but little influence on row grains and barren ear tip.
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Pepó, P., and V. Kovačević. "Regional analysis of winter wheat yields under different ecological conditions in Hungary and Croatia." Acta Agronomica Hungarica 59, no. 1 (March 1, 2011): 23–33. http://dx.doi.org/10.1556/aagr.59.2011.1.3.
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Wheat is the second most important field crop on arable lands in Hungary and Croatia. Yield variations between years are high in both countries. In the short term these variations are mainly the result of the weather parameters specific to individual growing seasons. The aim of this study was to compare variations in winter wheat yields over years in four counties in Hungary and five in Croatia, with the emphasis on the impact of rainfall and mean air temperature regimes. The results showed that rainfall in spring was most decisive for winter wheat yields. The highest winter wheat yields were obtained when the rainfall in the winter half-year ranged from 230–260 mm and the spring rainfall from 180–230 mm. The precipitation in the growing season is much higher in eastern Croatia than in eastern Hungary, so water shortage is a more pronounced environmental problem for wheat in Hungary. This is probably why wheat yields were lower in eastern Hungary than in eastern Croatia in the period tested. Pearson correlation analysis on the yields and meteorological data between 1990 and 2009 revealed a positive correlation between spring rainfall and the yield, and a negative correlation between spring temperature and the yield. The results proved that yields were determined not only by weather conditions, but by many other factors (crop rotation, tillage, fertilization, variety, crop protection, etc.).
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Lubinga, Pathias N., Lydia M. Chabala, Mutisungilire Kachulu, and Vernon R. N. Chinene. "Effect of Climatic Variability on Maize and Soybean Yield under High Input Farming System in Copperbelt Province, Zambia." Journal of Sustainable Development 12, no. 4 (July 30, 2019): 53. http://dx.doi.org/10.5539/jsd.v12n4p53.
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In many developing countries, the effect of climate change on agriculture is evaluated with reference to small scale farmers, mainly under low input systems. As a result, literature on climate variability and its effect on high input farming systems are scanty. We evaluated the impact of climatic variability on maize and soybean yield under a high input management system. The objectives of the study were to: (i) assess rainfall and temeperature variability at a high input farm (ii) evaluate the effect of rainfall and temperature on maize and soybean yield under high input management system. (iii) assess the impact of El Niño and La Niña on maize and soybean yield. Data for rainfall and temperature was obtained from Zambia Meteorological Department which was complimented by records from the weather station located at the study site. Yield data for both maize and soybean was provided by ZAMBEEF farm. The analysis covered 32 years from 1980 to 2012. Time series plot was used to investigate the trend in minimum and maximum temperature and seasonal rainfall. Correlations were done in SPSS to establish the strength and direction of association between climatic variables (temperature and seasonal rainfall) and maize and soybean yield. Multiple Regression in SPSS was then used to analyze variation in maize and soybean yield due to climatic variables. Results revealed that minimum temperature had an increasing linear trend of 0.3°C to 0.5 per decade while maximum temperature showed an increasing linear trend of 0.2°C to 0.3°C per decade. On the other hand, seasonal rainfall was variable with over the period studied. The variations in maize and soybean yield explained by seasonal rainfall and temperature was not significant with only 17.2% and 20.1% of the variation explained, respectively. Although there was no significant impact of both El Niño and La Niño on the yields of both crops, regression analysis revealed a negative relationship between El Niño and soybean yield and a positive relationship with maize yield and a positive relationship was revealed between La Niña and maize and soybean yields.
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Santos, Eduardo Silva dos, Plinio Antonio Guerra Filho, Sheyla Sales de Oliveira, Breno dos Santos Silva, Antonio Emanuel Souta Veras, and Daniela Abreu de Souza. "Interannual rainfall variability and soybean yields in Mata Roma municipality, Maranhão." Revista Engenharia na Agricultura - REVENG 31, Contínua (March 21, 2023): 19–30. http://dx.doi.org/10.13083/reveng.v30i1.14276.
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Among the activities affected by rainfall variability, agriculture is one of the most vulnerable. In this sense, this work aimed to relate the interannual rainfall variability with the soybean yields variability in Mata Roma municipality, Maranhão, considering dry and rainy events. For this, for the period from 2003 to 2019, data on average soybean yield and rainfall in this producing region, which is one of the main ones in the State, were analyzed. Monthly rainfall data for the period 1985-2019 were also analyzed and the dry and rainy events were identified in these two meteorological data series. The results showed that rainfall from January to May explains about 99%, on average, of the interannual variation of yields and that occurrence of dry and very humid periods disfavor the soybean crop, causing, in most cases, yields and productions far below the average for the study region. Based on the regression analysis, it was found that maximum average yield of 2942.52 kg×ha?1 was reached with an average rainfall of 1709 mm.
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Cornish, PS, and GM Murray. "Low rainfall rarely limits wheat yields in southern New South Wales." Australian Journal of Experimental Agriculture 29, no. 1 (1989): 77. http://dx.doi.org/10.1071/ea9890077.
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Alternative models were compared for predicting the yield of wheat (Triticum aestivum) from water-use and water-use efficiency. A locally derived empirical model most closely predicted experimental yields at Wagga Wagga and was used to compute water-limited potential yields for the district surrounding Wagga Wagga for the period 1960-84. District yields were close to the predicted potential in dry years, but reached a plateau of about 2.0 t/ha regardless of rainfall and the water-limited potential yield. The yields were less than 50% of potential when water-use exceeded 300 mm, which occurred in 19 years between 1960 and 1984. Some individual farmer-yields approached the potential. These results are similar to others from South Australia. Together, they suggest that low rainfall does not directly limit yield in many years over much of the wheat belt of southern and south-eastern Australia. We suggest therefore that an analysis of the reasons for low yield on farms could lead to substantial increases in yield for many farmers.
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DUTTA, RISHIRAJ, ERIC M. A. SMALING, RAJIV MOHAN BHAGAT, VALENTYNE A. TOLPEKIN, and ALFRED STEIN. "ANALYSIS OF FACTORS THAT DETERMINE TEA PRODUCTIVITY IN NORTHEASTERN INDIA: A COMBINED STATISTICAL AND MODELLING APPROACH." Experimental Agriculture 48, no. 1 (September 9, 2011): 64–84. http://dx.doi.org/10.1017/s0014479711000834.
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SUMMARYThis study analyses the factors affecting tea productivity in Northeast India using a combined statistical and modelling approach. The effects of a number of genotypic, environmental and management factors on tea yield are quantified and modelled, using a three-year (2007–2009) field trial in Assam, Northeast India. Simulations of the potential tea yield are obtained using the Cranfield University Plantation Productivity Analysis (CUPPA) Tea model to find out how well the predicted and observed values for tea production match. This combined approach shows that plantation age has a significant negative (R2 = 0.77) effect on tea yield. Monthly rainfall had a significant positive effect on monthly yields (R2 = 0.43). Rainfall was more strongly associated with tea yield when rainfall in month x was related to the tea yield in month x + 1 (R2 = 0.49). When repeating the analysis for a hypothetical situation that the fields are fully planted, the correlation between monthly rainfall in month x and tea yield for month x + 1 increases (R2 = 0.58). Adjusted yields show a higher correlation than actual yields. The results obtained show a close correspondence between predicted and observed yields, indicating that the model could be used on contrasting soil types, genotypes and also on daily, weekly and monthly weather data. It can be further calibrated and validated for Northeast Indian conditions if more required input parameters are collected in a series of plantations. Tea research might benefit from developing new versions of the CUPPA Tea model for the major clonal tea cultivars, with a more flexible module for fertiliser application as is currently the case.
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Kalaivani, A. S., and M. Ramesh. "Rainfall and Its Impact on Maize Yield." International Review of Business and Economics 1, no. 3 (2018): 104–6. http://dx.doi.org/10.56902/irbe.2018.1.3.30.
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The present study is to analyze the impact of rainfall on maize yield in Cuddalore district based on ten years data (2005-2006 to 2014- 2015). The study conducted with the objective of relationship between area, production, actual rainfall, and maize yield in the study area. The results of the study reveal that actual rainfall adversely affects maize productivity, while the effect of actual rainfall is maximum negative and no significant for maize crops.
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Chourghal, N., H. Belguerri, K. Maamri, F. Bahlouli, A. Salamani, and M. Benaini. "Diagnosis of the rainfall-wheat yield relationship in the current and future climate change conditions in Eastern Algeria." Biosystems Diversity 31, no. 2 (May 2, 2023): 158–62. http://dx.doi.org/10.15421/012316.
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Future projections indicate that rain-fed agriculture in North Africa is among the most vulnerable in the world in the context of future climate change. This article aims to diagnose the relationship between rainfall and wheat yield in both current and future climatic situations in a semi arid agro-climatic conditions represented by the region of Bordj Bou Arreridj. For the current situation, we used 15 years (1995–2009) of recorded rainfall and durum wheat yield series. Future rainfall projections (2071–2100) were generated by the MED-CORDEX climate model version CCLM4-8-19 under RCP 6.0 scenario. Simulated data over the observed period and that of the future on the maximum evapotranspiration (ETM) of durum wheat and the water deficit (WD) accumulated over the cycle as well as future yields are obtained using a simple agro meteorological crop simulation model, previously validated. In both current and future situations, precipitations, ETM, WD and yields data are first analyzed, then yields are related by regression to three components of rainfall: annual rainfall, cumulative rainfall over the crop cycle (November–June) and cumulative rainfall during spring (March–May). In the observed climate, annual precipitation averages 382.3 ± 96.3 mm, cumulative rainfall over the crop cycle (November–June) averages 278.3 mm and cumulative rainfall during spring is 101.9 mm. These last decrease to 303.7 ± 99.4, 232.3 and 83.3 mm in the future situation. Observed yields (1995–2009) averages1.9 ± 0.64 q/ha in the observed situation and decrease to 15.5 ± 0.54 q/ha in future climate. ETM are low and WD values are high in the current climate, with a worsening of the situation in the future climate, particularly during spring. The correlation between yields and précitations is always positive in both weather conditions, but the best R2 are 0.65 and 0.82 and concern spring rains. In semi-arid regions, cumulative rainfall towards the end of the growing season is currently impacting the grain yield of durum wheat and will become more decisive in the context of future climate change.
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Xiao, Pei Qing, Wen Yi Yao, and Chang Gao Wang. "Soil Erosion Process in Sloped Shrub Plots under Simulated Rainfall." Advanced Materials Research 347-353 (October 2011): 2094–97. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2094.
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Runoff, sediment yield and infiltration process of shrub plots were studied under rainfall intensities of 45, 87 and 127 mm/h with 20° slope gradient using simulated rainfall experiment. The results showed that cumulative runoff and cumulative sediment yield of shrub plot had an obvious positive correlation with rainfall time. Under rainfall intensity of 45 mm/h, runoff and sediment yield of shrub plot kept a constant level. Under rainfall intensity of 87 mm/h, runoff kept a fluctuant increase, whereas sediment yield basically kept steady. Under rainfall intensity of 127 mm/h, runoff and sediment yield of shrub plot increased evidently due to the formation of erosion pits. Infiltration rate of shrub plot had a negative relation with runoff as well as sediment yield.
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Jones, M. J., and A. Wahbi. "Site-factor Influence on Barley Response to Fertilizer in On-farm Trials in Northern Syria: Descriptive and Predictive Models." Experimental Agriculture 28, no. 1 (January 1992): 63–87. http://dx.doi.org/10.1017/s0014479700023024.
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SummaryThe effects of site and rate of application of fertilizer on the grain and straw production of barley were investigated in 75 trials on representative farmers' fields. Grain and straw production were strongly but curvilinearly correlated with growth-period rainfall over the range 136–568 mm, almost irrespective of soil type, previous crop or fertilizer rate, and responded positively to applied nitrogen and/or phosphorus in 74 of the trials. Responses to nitrogen increased and those to phosphorus decreased with increasing rainfall. Yields tended to be lower but responses to nitrogen were higher where barley followed barley and were influenced by the availability of phosphate and mineral nitrogen in the soil at planting time.These results are summarized in regression equations, which express yield quadratically in terms of fertilizer rates, seasonal rainfall and their interactions over the full 75-trial data set and within representative sub-sets. The wide differences in crop response to fertilizer highlight the problem of recommending fertilizer rates for a low and variable rainfall environment. Simple second-order multiple regressions for different geographical areas, rainfall zones, crop rotations etc., based on mean rainfall values, account for around 40% of the variance in yield response to nitrogen and phosphorus fertilizer; only a small proportion of the remaining variance appears attributable to rainfall variability. Alternatively, yield may be expressed as a function of fertilizer rate and rainfall using rainfall probability values derived from long-term records. This can be used to estimate the probabilities of predetermined yield responses for specified sites and fertilizer rates and to produce maps of those probabilities.
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Silungwe, Festo, Frieder Graef, Sonoko Bellingrath-Kimura, Siza Tumbo, Frederick Kahimba, and Marcos Lana. "Analysis of Intra and Interseasonal Rainfall Variability and Its Effects on Pearl Millet Yield in a Semiarid Agroclimate: Significance of Scattered Fields and Tied Ridges." Water 11, no. 3 (March 20, 2019): 578. http://dx.doi.org/10.3390/w11030578.
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Establishing food security in sub-Saharan African countries requires a comprehensive and high resolution understanding of the driving factors of crop production. Poor soil and adverse climate conditions are among the major drivers of poor regional crop production. Drought and rainfall variability challenges are not fully being addressed by rainfed producers in semiarid areas. In this study, we analysed the spatiotemporal rainfall variability (STRV) and its effects on pearl millet yield using two seasons of data collected from 38 rain gauge stations scattered randomly in farm plots within a 1500 ha area of semiarid central Tanzania. The STRV effects on pearl millet yield under flat and tied ridge management were analysed. Our results show that seasonal rainfall can vary significantly for neighboring fields at distances of less than 200 m, which impacts yield. The STRV for daily rainfall was found to be more critical than for total seasonal rainfall amounts. Scattering fields can help farmers avoid total harvest loss by obtaining at least some yield from the areas that received adequate rain. The use of tied ridges is recommended to conserve soil moisture and improve yields more than flat cultivation in semiarid areas.
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FitzGerald, RD, ML Curll, and EW Heap. "Wheat for fodder and grain on the Northern Tablelands of New South Wales." Australian Journal of Experimental Agriculture 35, no. 1 (1995): 93. http://dx.doi.org/10.1071/ea9950093.
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Thirty varieties of wheat originating from Australia, UK, USA, Ukraine, and France were evaluated over 3 years as dual-purpose wheats for the high rainfall environment of the Northern Tablelands of New South Wales (mean annual rainfall 851 mm). Mean grain yields (1.9-4.3 t/ha) compared favourably with record yields in the traditional Australian wheatbelt, but were much poorer than average yields of 6.5 t/ha reported for UK crops. A 6-week delay in sowing time halved grain yield in 1983; cutting in spring reduced yield by 40% in 1986. Grazing during winter did not significantly reduce yields. Results indicate that the development of wheat varieties adapted to the higher rainfall tablelands and suited to Australian marketing requirements might help to provide a useful alternative enterprise for tableland livestock producers.
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Zhang, Heping, Neil C. Turner, Narelle Simpson, and Michael L. Poole. "Growing-season rainfall, ear number and the water-limited potential yield of wheat in south-western Australia." Crop and Pasture Science 61, no. 4 (2010): 296. http://dx.doi.org/10.1071/cp09288.
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Knowledge of the contribution of ear number per unit area, grains per ear, and kernel weight to grain yield is important in understanding the limits to the water-limited potential yield of wheat in rain-fed agricultural regions. This paper analyses the relationships between grain yield, yield components and growing-season rainfall using data from the low-, medium-, and high-rainfall zones of the cropping region of south-western Australia. The aim of the paper is to quantify the contribution of ear number, grains per ear and kernel weight to grain yield and define the yield components of wheat required to achieve its water-limited yield. The grain yield of wheat was closely related to the number of grains per unit area (r2 = 0.90) and ears/m2 (r2 = 0.75), but poorly correlated with kernel weight (r2 = 0.30) and grains/ear (r2 = 0.09). The number of grains per unit area was highly related (r2 = 0.75) to the number of ears per unit area across the rainfall zones of the cropping region, and strongly correlated (r2 = 0.88–0.94) to the spike dry weight at anthesis. The highest yields achieved in the field were close to the water-limited potential estimated using the French and Schultz method. To achieve this water-limited potential, wheat requires 1 ear per m2 for every mm of growing-season rainfall (r2 = 0.92). We conclude that the number of ears per unit area is the most important factor required in order to achieve high yields of up to a yield of 6 t/ha in wheat in rain-fed south-western Australia.
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Munodawafa, Adelaide. "The Effect of Rainfall Characteristics and Tillage on Sheet Erosion and Maize Grain Yield in Semiarid Conditions and Granitic Sandy Soils of Zimbabwe." Applied and Environmental Soil Science 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/243815.
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In semiarid regions, rainfall is one of the primary factors affecting soil erosion and crop production under rain-fed agriculture. The study sought to quantify the effect of rainfall characteristics on sheet erosion and maize grain yield under different tillage systems. It was carried out under semiarid conditions and infertile sandy soils of Zimbabwe. Rainfall amount and intensity were recorded every 24 hours, while sheet erosion was measured from four tillage systems (Conventional Tillage (CT), Mulch Ripping (MR), Tied Ridging (TR) and Bare Fallow (BF)). Maize (Zea mays L.) was grown on three tillage systems (CT, MR, and TR). Rainfall amount varied significantly (P<0.001) between seasons (164–994 mm). CT recorded the highest average soil losses (15 t/ha), while MR and TR recorded 1.3 and 1.2 t/ha, respectively. Maize grain yields increased with increasing seasonal rainfall giving yield-responses of 0.9 t/ha (TR) to 1.3 t/ha (MR) for every 100 mm rainfall increment. Overall, treatments didnot differ significantly (P<0.497), except during drier seasons (P<0.025). Regression equations showed that yields can be confidently predicted using rainfall amount and time, withR2values of 0.82 to 0.94. Maize grain yields proved to be mostly dependent on rainfall amount than fertility. The productivity of the soils decreased with increased length of cultivation.
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Kwakye, Jacob. "Effect of Temperature and Rainfall Variability on Selected Crop Yields in Wenchi Municipality of Ghana." American Journal of Environment and Climate 2, no. 1 (April 3, 2023): 24–32. http://dx.doi.org/10.54536/ajec.v2i1.1328.
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The paper examines how rainfall and temperature variability affect the yields of selected food crops in the Wenchi municipality of Ghana. The study employed the quantitative research method to achieve its objective. The datasets consisted of historical records of monthly observational data of temperature and rainfall collected over 22 years (2000-2021), and yearly records of cassava, yam, and maize yield data collected over 22 years (2000 to 2021). Trend analysis was conducted to examine the trend in the climate variables (temperature and rainfall) and the yield of the selected food crops (cassava, yam, and maize) over the study period. A marginal variation in annual rainfall and temperature was observed over the study period. The municipality experienced slightly decreasing trends in annual rainfall and increasing trends in annual temperatures. Significant variations and increasing trends in the yields of the respective food crops were observed. Using standard regression methods, the result shows that maximum and minimum temperatures are associated with increased yields for yam, maize and cassava, but annual rainfall is not significantly associated with the variations in the yields of the selected food crops. The study, therefore, suggests that further research be undertaken to investigate how other environmental and non-climatic factors influence crop production in the Wenchi municipality.
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Shin. "Influence of Spatial Rainfall Distribution on Sediment Yield: An Experimental Study." Journal of the Korean Society of Civil Engineers 35, no. 1 (2015): 111. http://dx.doi.org/10.12652/ksce.2015.35.1.0111.
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RAO, UMB, and K. VIJAYALAKSHMI. "Rainfall-yield relationships in rainfed sorghum in India." MAUSAM 37, no. 4 (April 7, 2022): 529–32. http://dx.doi.org/10.54302/mausam.v37i4.2595.
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The variation in rainfall and its distribution causes high fluctuations in production and productivity under rainfed agriculture. The data generated during last decade by All India co-ordinated Project for Dryland Agriculture were used for developing yield rainfall relationships.
Total rainfall and its distribution affected the yield. Based on the correlations between yield and weekly rainfall from time of seeding, the growing season was divided into different periods. Various models were tried for prediction. The study was carried out for monsoon sorghum crop for four locations, viz., Hyderabad, Jhansi, Udaipur and Akola.
Delayed seeding reduces the yield of sorghum. The crucial periods for rainfall are 8-10,6.12,3.6, 11.13 weeks for Hyderabad, Jhansi, Udaipur and Akola respectively. At Udaipur, high rainfall during 9-11 weeks reduces the yield possibly due to pollen wash and/or lodging. With the models tried, It IS possible to predict the yield of this crop with higher degree of predictability.
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Xu, Jiajia, Jianjun Zhang, Minyi Li, and Fenzhong Wang. "Effect of Rain Peak Morphology on Runoff and Sediment Yield in Miyun Water Source Reserve in China." Water 11, no. 12 (November 20, 2019): 2429. http://dx.doi.org/10.3390/w11122429.
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The research on the impact of rainfall patterns on runoff and sediment yield is still insufficient, especially under natural rainfall conditions. We analyzed the influence of rain peak morphology on runoff and sediment yield based on the data of rainfall, runoff, and sediment in the bare runoff plot of Shixia, a small watershed in the Miyun district of Beijing, from 2007 to 2016. We took 0.4 mm min−1 as the standard of rain peak classification and the peak width, peak number, peak value, peak position and multi-peak continuity as the indexes of rain peak morphology. The results showed that: (1) Peak number, peak value, and peak width were significantly correlated with runoff and sediment yield, while peak position was irrelevant. The order of correlation between rain peak morphology indexes and runoff yield was peak width (0.71) > peak number (0.69) > peak value (0.33) > peak position (0.05). The order of correlation between rain peak morphological indexes and sediment yield was peak width (0.62) > peak value (0.36) > peak number (0.36) > peak position (−0.09). The multi-peak continuity was not correlated with runoff (0.12) and sediment yield (0.45). (2) When the number of rain peaks was greater than one in a single rainfall, the amount of runoff and sediment production increased significantly. (3) For multi-peak rainfall, 90 min was the boundary point of the rain peak interval, and the sediment yield formed by rainfall with a rain peak continuity >1/90 min−1 was significantly larger than the rainfall of ≤1/90 min−1. (4) Covariance analysis showed that the runoff caused by rainfall with a peak at the middle positions was obviously more than rainfall with a peak at the front position. However, the peak position had no significant effect on the sediment yield. (5) The peak rainfall amount of a rainfall (TPR) was a comprehensive index reflecting peak number, peak value and peak width, and the correlation between it and the sediment yield and runoff reached 0.60 and 0.71, respectively. Statistical rainfall characteristic indexes included rainfall amount, average rainfall intensity, rainfall duration, I5 (maximum 5-min rainfall intensity), I10, I15, I20, I30, and I60, among which I60 had the strongest correlation with runoff and sediment yield (0.69, 0.60), which were much larger than other rainfall indexes (0.08~0.47, 0.14~0.48) except rainfall amount (0.75, 0.37). By establishing a regression equation, it was found that both TPR and I60 had good explanatory power for runoff and weak explanatory power for sediment yield.
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FITSUM BEKELE, DIRIBA KORECHA, and LISANEWORK NEGATU. "Influence of rainfall features on barley yield in Sinana district of Ethiopia." Journal of Agrometeorology 19, no. 2 (June 1, 2017): 125–28. http://dx.doi.org/10.54386/jam.v19i2.684.
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Rainfall data and barley production of Sinana district, South East Ethiopia for the period 1995-2016 were used to analyze influence of rainfall features on barley yield using correlation and regression techniques. Downscaled output of CSIRO-Mk3.6.0 GCM model for RCP 8.5 emission scenario for the period 2020-2049 were used to determine future rainfall and to ascertain its impact on barley yield. Sixrainfall characteristics viz. onset date, end date, length of growing season, seasonal (June-September, kiremt) rainfall, and total annual rainfall were analysed. Kiremt (JJAS) contributes 37 per cent of annual rainfall and varied between 271.4 mm and 854.0 mm during the study period with coefficient of variation (CV) of 43 per cent. The barley yield was found to vary between 14.79qha-1and 35.84qha-1 with CV of 27.9 per cent during the same period. The results indicated that among all the rainfall features studied, the kiremt rainfall had strong positive relationship (r = 0.668**) with barley yield and explained nearly 45 per cent of total variance in the yield. Under future climatic scenario during 2020-2049 period, the projected rainfall is going to be less than the mean rainfall that may cause reduction in barley yield by 1.8 to 4.4 per cent in Sinana district.
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CHOWDHURY, A., and M. M. DANDEKAR. "Effect of rainfall on groundnut (Arachis hypogaes L) Yield over Rajkot district." MAUSAM 42, no. 4 (February 28, 2022): 353–56. http://dx.doi.org/10.54302/mausam.v42i4.3264.
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Fisher's technique has, been adopted for finding out, effect of the rainfall on its distribution on the groundnut yield in Rajkot district. For this purpose yield and rainfall data from 1966 to 1986 have been .10-14 el e data used, From dally values, weekly rainfall for the crop growth period. VIZ., 23rd to 40th standard weeks were computed. Periods of adverse or beneficial effects of rainfall on yield were found. Yield were found.
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Bhandari, Govinda. "Effect of rainfall on the yield of major cereals in Darchula District of Nepal." International Journal of Environment 3, no. 1 (February 28, 2014): 205–13. http://dx.doi.org/10.3126/ije.v3i1.9954.
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Rainfall is one of the most important factors for the growth of cereals. Inadequate water results poor growth and reduced yield. This study is aimed to explore the relationship between rainfall and yield of major cereals in Darchula district of Nepal. The yield of individual cereals is correlated with the seasonal rainfall data using MS Excel to identify the effect of rainfall on yield of cereals. The amount of rainfall in the years 1974, 1977, 1980, 1985, 1986, 1987, 1991, 1992, 1994, 1996, 1997, 1999 and 2000 was reduced which has greatly affected the yield of rice, wheat and maize in 1986 and 1987. In the years 1976, 1977, 1999 and 2000, the decrease in the amount of rainfall has reduced the yield of all major cereals in Darchula district of Nepal. DOI: http://dx.doi.org/10.3126/ije.v3i1.9954 International Journal of Environment Vol.3(1) 2014: 205-213