Soil-water conservation and rainwater harvesting strategies 3 in the semi-arid Mzingwane Catchment

8 Abstract 9 Various soil water management practices have been developed and promoted for the semi arid areas of Zimbabwe. These include a 10 variety of infield crop management practices that range from primary and seconday tillage approaches for crop establishment and weed 11 management through to land forming practices such as tied ridges and land fallowing. Tillage methods evaluated in this study include 12 deep winter ploughing, no till tied ridges, modified tied ridges, clean and mulch ripping, and planting basins. Data collected from the 13 various trials since the 1990s show that mulch ripping and other minimum tillage practices consistently increased soil water content 14 and crop yields compared to traditional spring ploughing. Trial results also showed higher soil loss from conventionally ploughed plots 15 compared to plots under different minimum tillage practices. 1

Evidence and Lessons Learned from Long-Term On-Farm Research on Conservation Agriculture Systems in Communities in Malawi and Zimbabwe

A long-term study was carried out in the Zidyana Extension Planning Area (EPA), Malawi and in the Zimuto Communal Area, Zimbabwe, to evaluate the effect of different conservation agriculture (CA) systems on crop productivity, soil quality and economic performance. Maize productivity results from Zidyana showed that CA systems out-yielded the conventional system in seven out of nine cropping seasons. Labour savings relative to the conventional control ranged from 34–42 labour days ha−1 due to reduced time needed to make manual ridges and for weed control, leading to higher net benefits of 193–444 USD·ha−1. In Zimuto, yield benefits were apparent from the second season onwards and there was a much clearer trend of increased yields of CA over time. Greater net benefits (in USD·ha−1) were achieved on CA systems in Zimuto compared with conventional control treatments due to overall higher yields from CA systems. In Zimuto, both increased infiltration and a gradual increase in soil carbon were recorded, which may have contributed to the greater yield response of CA in this area. In Zidyana, yield increases were attributed primarily to enhanced water infiltration since no increases in soil carbon levels were measured. Farmers highlighted critical challenges to the adoption of CA. These will have to be addressed in future research and extension to provide effective solutions to farmers

Maize Kernel Abortion Recognition and Classification Using Binary Classification Machine Learning Algorithms and Deep Convolutional Neural Networks

Maize kernel traits such as kernel length, kernel width, and kernel number determine the total kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. There are a few methods that allow the extraction of ear and kernel features through image processing. We evaluated the potential of deep convolutional neural networks and binary machine learning (ML) algorithms (logistic regression (LR), support vector machine (SVM), AdaBoost (ADB), Classification tree (CART), and the K-Neighbor (kNN)) for accurate maize kernel abortion detection and classification. The algorithms were trained using 75% of 66 total images, and the remaining 25% was used for testing their performance. Confusion matrix, classification accuracy, and precision were the major metrics in evaluating the performance of the algorithms. The SVM and LR algorithms were highly accurate and precise (100%) under all the abortion statuses, while the remaining algorithms had a performance greater than 95%. Deep convolutional neural networks were further evaluated using different activation and optimization techniques. The best performance (100% accuracy) was reached using the rectifier linear unit (ReLu) activation procedure and the Adam optimization technique. Maize ear with abortion were accurately detected by all tested algorithms with minimum training and testing time compared to ear without abortion. The findings suggest that deep convolutional neural networks can be used to detect the maize ear abortion status supplemented with the binary machine learning algorithms in maize breading programs. By using a convolution neural network (CNN) method, more data (big data) can be collected and processed for hundreds of maize ears, accelerating the phenotyping process

Crop Productivity, Nutritional and Economic Benefits of No-Till Systems in Smallholder Farms of Ethiopia

Smallholder maize and wheat production systems are characterized by high drudgery. On-farm trials were run for three seasons in Ethiopia. The study assessed the effect of 2 WT direct seeding and growing season on (1) soil quality, and (2) maize and wheat productivity, energy and protein gains, and gross margins, on smallholder farms in Ethiopia. For the wheat crop, the effect of different soil types and agroecological conditions on productivity was assessed. The treatments in paired plots were (i) conventional ploughing practice and (ii) no-till (NT). Soil properties, crop yield, nutrition gains and gross margins were determined. No-till improved soil properties in the short term. No-till produced 1210–1559 kg ha−1 grain, 18–29 GJ ha−1 energy and 121–194 kg ha−1 proteins, and generated 358–385 US$ha−1 more than the conventional practice in the maize system. In the wheat system, no-till treatment had 341–1107 kg ha−1 grain, 5–16 GJ ha−1 energy and 43–137 kg ha−1 proteins, and generated 230–453 US$ ha−1 more than conventional practice. No-till can be more productive and profitable in the Ethiopian maize and wheat-based cropping systems