Bivariate relations between penetration resistance and soil moisture content was conducted on sandy loam soil (Eutric leptosol) at the University of Juba demonstration farm. Soil Penetration Resistance (PR) was measured using an Eijkelkamp pocket penetrometer to a depth of 0-15 cm depth with Soil Moisture Content (SMC) measured using an Eijkelkamp sensor theta probe. Measured SMC within the plots varied from 9-35% whereas, the PR varied from 0.1 to 4.5 kg cmG 2 . The results showed significant negative correlation between PR and SMC expressed by a polynomial function with r 2 = 0.48 and Pearson Coefficient, R = -0.657. Spatial distribution of PR and SMC using both kriging and Inverse Distance Weighting (IDW) interpolation methods showed no significant differences. However, PR tended to decrease with an increasing SMC in an easterly direction. The results also showed that the spherical model gave the best fit for both PR and SMC at r 2 = 0.527 and 0.747 with moderate and strong spatial dependency at 28.9 and 10.53%, respectively. Though the effective range A 0 , for both PR and SMC at 24.14 and 30.4 m, respectively were more or less similar, the strong spatial dependency of SMC suggested its significance as a limiting factor for plant growth as opposed to PR which on average was at 1.592 kg cmG 2 .
Fractal dimension fd, was used as one of the parameters to describe dessication cracking pattern of a remolded Black Cotton soil (Eutric Vertisol). The fractal dimension computed from filtered, thinned and skeletonized binary images of soil cracks using the Fractal3 software provided an insight into temporal variability of fd as well as its relationship with the Crack Intensity Factor (CIF) and Soil Moisture Content (SMC). The results showed that even for single crack, the fd prior to filtering and thinning were higher than after. Cracking patterns were observedfroma chosen soil sample during dessication and the corresponding relationship between fd and CIF compared and monitored. As the critical SMC decreased during drying (45% to 27%), the CIF soil increased (0.023% -5.75%), so did the fd (1.233 to 1.7193). The fd showed a positive linear correlation with CIF at r 2 = 0.247 (P < 0.05) whereas the correlation of fd with SMC was best described using a polynomial function at r 2 = 0.969 (P < 0.05). The fd was sensitive to dessication cracking and therefore on SMC changes. Visual observation of dessication cracking showed that CIF increased and attained stability after day 4 while the computed and logarithmic transformed crack area attained stability between days 7 to 10 gradually decreasing to values below 2%. The estimated crack Cover or Brightness of the digitized binary images also gave better approximation of the CIF though this was slightly higher. Our results showed that dessication cracking of the Eutric Vertisol was independent of antecedent critical SMC and was time-constrained. Further soil cracking therefore stopped once maximum CIF was attained and only widening and deepening of pre-existing cracks continued.
This study simulated the biophysical, economic and environmental implications of cowpea fertigated with human urine (equivalent to 60 kg N ha-1) as source of organic N. The DSSAT CROPGRO model was used to simulate harvested cowpea yield, N (leached) , N (uptake) , monetary returns or gross margins in ($) under two different treatments: without fertigation or human urine (T 0) and with fertigation (T 1). Biophysical analysis using the Cumulative Probability Distribution (CPD) showed a 50% probability of the harvested cowpea yield under T 1 being higher than under T 0 at 1060 and 600 kg ha-1 respectively, accounting for a 43.4% difference. The Mean Gini Stochastic Dominance (MGSD) analysis was used to assess the gross margin and helped in deciding on the best strategic and management option. The findings of this study revealed a 50% probability (CPD 0.5), of higher gross margin under T 1 at $-215 higher than under T 0 at $285. This was a $70 difference per season under T 1 and so enhancing a faster payback and a larger monetary return on overall investments. Similarly, seasonal analysis with fertigation showed that at CPD 0.5 , the N (leached) was still < 4 kg N ha-1 per season and so posed no environmental risks. The simulation results also showed higher a probability of N (uptake) of about 270 kg N ha-1 during fertigation compared to about 95 kg N ha-1 under T 0. Therefore, the DSSAT CROPGRO model can be used to successfully forecast future cowpea yields, gross margin, N (leached) , N (uptake) under different management practices to enable smallholder farmers in South Sudan make informed decisions on sustainable cowpea production.
The DSSAT-CROPGRO model was used to characterize phenology and cultivar coefficients of cowpea treated with diluted human urine (equivalent to 60 kg-N/ha), simulated under ten irrigation schedules for a 5 year production period in Juba County of Central Equatoria State, South Sudan. Two treatments (T 0) without urine and (T 1) with human urine dilution during the growing season of 2016 were used and, 5-years model simulations on the grain number/m², LAI, canopy height (m) and pod number/m² compared. Irrigation schedules were carried out once the volumetric soil moisture content measured using the Theta Soil Moisture Probe was close to 15%. Results showed that cultivar coefficients EM-LF, FL-LF and FL-SH for both T 0 and Y2015 (calibrant) were on average 6-8 days shorter than under T 1. Also, the SIZLF under T 1 was about 8-17% greater than under both T 0 and calibrant Y2015 indicating the significance of diluted human urine on cowpea cultivar coefficients and phenology. The model also gave good agreement between observed and simulated growth parameters with low RMSE of the pod nr/m² at 17 for T 0 , and 37.5 for T 1 , the RMSE for grain nr/m 2 was 360 kg for T 1 , and 347 kg for T 0 , whereas the RMSE of LAI showed no significant difference. The d-indices in estimating LAI and canopy height were generally low and showed the largest errors than for pod nr/m² and grain nr/m². The results showed that the model satisfactorily simulated and underscored the significance of diluted human urine on both phenology and cultivar coefficients of cowpea.
A simple Feed Forward Neural Network (FFNN) model with a learning back-propagation algorithm was applied to forecast rainfall data from 1997-2016 of Juba County, South Sudan. Annual rainfall data were aggregated into three seasons MAMJ, JAS and OND and later trained for best predictions for the period 2017-2034 using the Alyuda Forecaster XL software. Best training was attained once the minimum error or cost function of the weight was attained during gradient descent and expressed as Mean Square Error (MSE) and AE of the input variable. The results showed that for MAMJ and JAS months, the number good forecasts were over 97% whereas this was between 60-80% for OND months. The Seasonal Kendal (SK) test on future rainfall forecasts as well as the Theil-Sen slope showed a declining monotonic trend in the mean amounts in all three seasons with MAMJ, JAS at OND at 100, 150 and 80 mm respectively towards the end of 2034. Declining onset of MAMJ rains is expected to significantly affect the timing for land preparation and crop planting. The forecast accuracy of the FFNN can be used as a vital tool for decision makers in projecting future rainfall events.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.