Cropping systems and soil quality and fertility in south-central Uganda

Little is known about how cropping systems influence soil quality and fertility in Uganda. Some cropping systems are more valued and as a result are given more nutrients and planted in certain soils, all of which leads to varying soil quality and fertility. This study compared soil quality (soil pH, cation exchange capacity (CEC), electric conductivity (EC), total N, and depth to restrictive layer (DRL)) and fertility (extractable P, K, Ca, Mg, and Na, and base saturation (BS) from five cropping systems (banana (Musa × paradisiaca L.)-dominant (B), coffee [Coffea robusta (L.) Linden]-dominant (C), banana-coffee (BC), annual with no crop rotation (ANR), and annual with crop rotation (AR); fertilized and unfertilized soils; and three soil types (black (Phaeozem), red (Ferralsol), and black-stony) in south-central Uganda. The analysis included farm assessments to establish management history of studied fields and soil sampling from 52 fields in Masaka District, Uganda. Main-effects ANOVA was employed to determine differences in means in soil under different cropping systems, soil types, and fertilizer use. Soil quality (pH at depths of 0 to 10 and 20 to 30 cm, CEC, and EC) and fertility (extractable Ca and Mg) varied by cropping system. The AR and B systems had higher soil quality and fertility compared to other cropping systems. Soil quality (pH at depths of 0 to 10 and 0 to 15 cm and DRL) and soil fertility (extractable P and K) varied by soil type. Black and black-stony soils had higher soil quality and fertility than red soils. Soil quality and fertility did not vary by fertilizer use. The results of this study indicate that both cropping system and soil type are associated with soil quality and fertility in south-central Uganda

Alternatives for Drought-damaged Soybeans—Bean Crop or Forage

As people reflect on the reasons for the irregular development and poor soybean production in Iowa this year, the next important questions relate to evaluation of crops in individual fields and planning when and how to harvest them to the greatest economic advantage. This evaluation involves reviewing normal crop growth and development, assessing the condition of the crops in individual fields relative to normal and to think through several harvest scenarios. Will this field have a harvestable soybean crop? Are there concerns about the crops? What use or management alternatives do I have

Crop Diversification, Tillage, and Management System Influence Spring Wheat Yield and Water Use

Depleted soil quality, decreased water availability, and increased weed competition constrain spring wheat production in the northern Great Plains. New management systems are necessary for improved crop productivity. The objective of our study was to compare productivity and soil water use of spring wheat (Triticum aestivum L.) in four crop rotations (continuous wheat, wheat–pea [Pisum sativum L.], wheat–forage barley [Hordeum vulgaris L.]–pea, and wheat–forage barley–corn [Zea mays L.]–pea) in two tillage (tilled and no-till) and management systems (conventional and ecological). Conventional management included recommended seed rates, early planting date, and broadcast N fertilization. Ecological management included variable seed rates, delayed planting, banded N fertilization, and increased stubble height. Spring wheat in diversified rotations averaged 35 mm greater preplant soil water content, 37 mm greater water use, 0.8 kg ha–1 mm–1 greater water use efficiency, and 473 kg ha–1 and 817 kg ha–1 greater grain and biomass yields than continuous wheat. Wheat in conventional management averaged 28 fewer heads m–2, 4 additional seed head–1, and 2 mg seed–1 heavier seed weight than wheat under ecological management, resulting in 644 kg ha–1 greater yield. Wheat under ecological management used 8 mm more water, but water use efficiency was 2.6 kg ha–1 mm–1 greater under conventional management. Postharvest soil water content was similar among rotations, tillage, and management systems, suggesting that wheat uses most available soil water. Spring wheat in diversified rotations planted early in the season is more resilient and should confer greater production stability than continuous wheat systems planted late

Corn (Zea mays L.) seeding rate optimization in Iowa, USA

Collecting soil, topography, and yield information has become more feasible and reliable with advancements in precision technologies. Combined with the accessibility of precision technologies and services to farmers, there has been increased interest and ability to make site-specific crop management decisions. The objective of this research was to develop procedures to optimize corn seeding rates and maximize yield using soil and topographic parameters. Experimental treatments included five seeding rates (61 750; 74 100; 86 450; 98 800; and 111 150 seeds ha−1) in a randomized complete block design in three central Iowa fields from 2012 to 2014 (nine site-years). Soil samples were analyzed for available phosphorus (Olsen method), exchangeable potassium (ammonium-acetate method), pH, soil organic matter (SOM), cation exchange capacity (CEC), and texture. Topographic data (in-field elevation, slope, aspect, and curvature) were determined from publically available light detection and ranging data. In four site-years, no interaction occurred between seeding rate and the descriptive variables. Three of the site-years resulted in a negative linear seeding rate response which made it impossible to determine an optimum seeding rate above the lowest seeding rate treatment. The seeding rate optimization process in five site-years resulted in seeding rate by variable interactions; four site-years had a single seeding rate by variable interaction (pH, in-field elevation, or curvature) and one site-year had three seeding rate by variable interactions (pH, CEC, and SOM). Meaningful seeding rate optimizations occurred in only three of nine site-years. There was not a consistent descriptive variable interaction with seeding rate as a result of weather variability