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The Hulu Langat basin, a strategic watershed in Malaysia, has in recent decades been exposed to extensive changes in land-use and consequently hydrological conditions. In this work, the impact of Land Use and Cover Change (LUCC) on hydrological conditions (water discharge and sediment load) of the basin were investigated using the Soil and Water Assessment Tool (SWAT). Four land-use scenarios were defined for land-use change impact analysis, i.e. past, present (baseline), future and water conservation planning. The land-use maps, dated 1984, 1990, 1997 and 2002, were defined as the past scenarios for LUCC impact analysis. The present scenario was defined based on the 2006 land-use map. The 2020 land-use map was simulated using a cellular automata-Markov model and defined as the future scenario. Water conservation scenarios were produced based on guidelines published by Malaysia’s Department of Town and Country Planning and Department of Environment. Model calibration and uncertainty analysis was performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The model robustness for water discharge simulation for the period 1997–2008 was good. However, due to uncertainties, mainly resulting from intense urban development in the basin, its robustness for sediment load simulation was only acceptable for the calibration period 1997–2004. The optimized model was run using different land-use maps over the periods 1997–2008 and 1997–2004 for water discharge and sediment load estimation, respectively. In comparison to the baseline scenario, SWAT simulation using the past and conservative scenarios showed significant reduction in monthly direct runoff and monthly sediment load, while SWAT simulation based on the future scenario showed significant increase in monthly direct runoff, monthly sediment load and groundwater recharge.

The Hulu Langat basin, a strategic watershed in Malaysia, has in recent decades been exposed to extensive changes in land-use and consequently hydrological conditions. In this work, the impact of Land Use and Cover Change (LUCC) on hydrological conditions (water discharge and sediment load) of the basin were investigated using the Soil and Water Assessment Tool (SWAT). Four land-use scenarios were defined for land-use change impact analysis, i.e. past, present (baseline), future and water conservation planning. The land-use maps, dated 1984, 1990, 1997 and 2002, were defined as the past scenarios for LUCC impact analysis. The present scenario was defined based on the 2006 land-use map. The 2020 land-use map was simulated using a cellular automata-Markov model and defined as the future scenario. Water conservation scenarios were produced based on guidelines published by Malaysia’s Department of Town and Country Planning and Department of Environment. Model calibration and uncertainty analysis was performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The model robustness for water discharge simulation for the period 1997–2008 was good. However, due to uncertainties, mainly resulting from intense urban development in the basin, its robustness for sediment load simulation was only acceptable for the calibration period 1997–2004. The optimized model was run using different land-use maps over the periods 1997–2008 and 1997–2004 for water discharge and sediment load estimation, respectively. In comparison to the baseline scenario, SWAT simulation using the past and conservative scenarios showed significant reduction in monthly direct runoff and monthly sediment load, while SWAT simulation based on the future scenario showed significant increase in monthly direct runoff, monthly sediment load and groundwater recharge.

Corn (Zea mays L.) is planted in two seasons per year in northern Iran (mid-April as a main crop and mid-June as a second crop). The main objective of this study was to determine whether corn yield response would differ between these two seasons and different plant populations. Two field experiments were conducted at the Agricultural Research Center of Golestan - Iran in 2007 and 2008 at different planting densities. The results showed that the values of grain yield and most traits were significantly lower in the second season. Maximum grain yield was observed at planting densities of 6.5 plants m -2 in the first season, whereas in the second season grain yield was the same for planting densities between 2.5 and 12.5 plants m -2. Based on the second-year experimental results, the following functions were fitted to show the relationship between yield ha -1 (Y) and planting densities (X) for the first and second seasons, respectively: (Y = -167.6X 2 + 2672.2X + 511.77; R 2 = 0.992) and (Y = 1200.1 ln(X) + 2924.4; R 2 = 0.935). This study found that the optimum plant population was 6.5 plants m -2 under low heat stress, and should be reduced to 2.5-4.5 plants m -2 under heat stress conditions.

Corn (Zea mays L.) is planted in two seasons per year in northern Iran (mid-April as a main crop and mid-June as a second crop). The main objective of this study was to determine whether corn yield response would differ between these two seasons and different plant populations. Two field experiments were conducted at the Agricultural Research Center of Golestan - Iran in 2007 and 2008 at different planting densities. The results showed that the values of grain yield and most traits were significantly lower in the second season. Maximum grain yield was observed at planting densities of 6.5 plants m -2 in the first season, whereas in the second season grain yield was the same for planting densities between 2.5 and 12.5 plants m -2. Based on the second-year experimental results, the following functions were fitted to show the relationship between yield ha -1 (Y) and planting densities (X) for the first and second seasons, respectively: (Y = -167.6X 2 + 2672.2X + 511.77; R 2 = 0.992) and (Y = 1200.1 ln(X) + 2924.4; R 2 = 0.935). This study found that the optimum plant population was 6.5 plants m -2 under low heat stress, and should be reduced to 2.5-4.5 plants m -2 under heat stress conditions.

Spreadsheets are now widely used for data entry and analysis. Therefore, Texture AutoLookup (TAL) is a computer program designed to work within 1–2–3 for Windows and EXCEL to determine the USDA soil textural class. TAL determines the textural class without having to repeat data entry because data is taken directly from the spreadsheet itself. Moreover, TAL works even with two particle size data or with imperfect data (that is, the sum of the three particle sizes being unequal to 100%). TAL is independent of the particle‐size analysis method, and TAL allows textural class names to be modified or be translated into another language.

Spreadsheets are now widely used for data entry and analysis. Therefore, Texture AutoLookup (TAL) is a computer program designed to work within 1–2–3 for Windows and EXCEL to determine the USDA soil textural class. TAL determines the textural class without having to repeat data entry because data is taken directly from the spreadsheet itself. Moreover, TAL works even with two particle size data or with imperfect data (that is, the sum of the three particle sizes being unequal to 100%). TAL is independent of the particle‐size analysis method, and TAL allows textural class names to be modified or be translated into another language.